# Optimization of energy integrated systems for vehicles applications

Zlatina Dimitrova

► To cite this version:

Zlatina Dimitrova. Optimization of energy integrated systems for vehicles applications. Engineering Sciences [physics]. CNAM Paris, 2020. tel-03140405

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# Mémoire d'habilitation à diriger des recherches

Présenté par

Dr. Zlatina Dimitrova

Chef de projet en recherche et innovation
à la Direction de la Recherche et de l'Innovation de PSA Groupe

## Optimization of energy integrated systems for vehicles
applications

12 Novembre 2020

### Composition du jury:

**Rapporteurs:**

Madame Anna FONTCUBERTA I MORRAL, Professeur des universités, Ecole Polytechnique Fédérale de Lausanne

Monsieur Jean François HETET, Professeur des universités, Ecole centrale de Nantes

Monsieur François LANZETTA, Professeur des universités, Université de Franche-Comté

**Garant:**

Madame Christelle PERILHON, Maître de conférence HDR, Conservatoire national des arts et métiers

**Examinateurs:**

Monsieur Sylvain ALLANO, Professeur des universités, Chief Scientific Officer de l'entreprise Flying Whales

Monsieur Miroslav DIMITROV, Maître de conférence HDR, Académie Militaire Nationale de la Bulgarie

Monsieur Sofiane KHELLADI, Professeur des universités, ENSAM Paris

Monsieur Plamen PUNOV, Maître de conférence HDR, Université technique de Sofia

**Invité:**

Monsieur Stéphane LEFEBVRE, Professeur des Universités, Conservatoire national des arts et métiers
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Acknowledgements

I would like to thank Professor Anna Fontcuberta i Morral, Professor Jean-François Hétet, Professor François Lanzetta, Professor Sofiane Khelladi, Associate Professor Plamen Punov, Associate Professor Miroslav Dimitrov, Professor Sylvain Allano and Professor Stéphane Lefebvre for their interest to participate in the jury and for their availability to evaluate this work. I would like to thank them to have accepted this mission. It is for me a pleasure and an honor to have proposed them as members of my habilitation jury. I would like to express my special gratitude to the reviewers of my work for the very constructive reports and appreciations of my work.

I would like to thank Associate Professor Christelle Périlhon for having given me the opportunity to write this manuscript and to present my application for habilitation in CNAM Paris. I would like to thank her for her support and her engagement to guide me through the habilitation application procedure. Her confidence, her motivation and her advices, were very helpful and stimulating during the preparation. I appreciate a lot our discussions, very helpful for my progress.

I would like to acknowledge Professor Jean-François Deü for the advices related to the habilitation process.

I further gratefully acknowledge the support of PSA Groupe and especially the support of the Research and Innovation Department. I would like to express my very great appreciation to Ms. Carla Gohin, Senior Vice President for Research, Innovation and Advanced Technologies of PSA Groupe for having given me the possibility to realize my habilitation project. I would like to acknowledge Mr. Ladimir Prince, Chief Scientific Officer and Vice President for Research, Innovation and Advanced Technologies of PSA Groupe for his support of my habilitation project, for his understanding and his assistance in the leading of the valuable research projects in PSA Groupe.

Many special thanks to General Major Dr. Grudi Angelov, Commandant of "Rakovski" National Defense College of Bulgaria for the assignment of my lectures in the Master Programs of "Rakovski" National Defense College. I am honored by the opportunity by the invitation to teach at this prestigious education institution. I would like to thank Colonel Associate Professor Miroslav Dimitrov, Commandant of the Logistics department of "Rakovski" National Defense College for the great welcome and the organization of my lectures in his department. Many special thanks as well to Colonel Professor Dimitar Nedialkov, member of the staff of "Rakovski" National Defense College and member of the Institute for Space Research of the Bulgarian Academy of Sciences for his support, his recommendation and his very high appreciation of my technical and research skills. I would like to thank all my colleagues and my students from "Rakovski" National Defense College of Bulgaria for their great welcome.

At this place, I would like to offer my special thanks to Mr. Guillaume Faury, Chief Executive Officer of Airbus Group for his permanent encouragement and the motivation during the habilitation. I am particularly grateful to Mr. Faury for his very first support of the idea of the habilitation project.

I would like to thank my very special friend "police man" for the interest on my research.

My very special thanks go also to Mr. Tomasz Krysinski, Vice President for Research and Innovation at Airbus Helicopters for the unconditional support and for the excellent relation with PSA Groupe.

I would like to express my gratitude to all my colleagues from PSA Groupe, and our "OpenLabs", for the collaboration on the research topics. I would like to thank my colleagues and my students for their
contribution to the publications, the patents, the presentations and our participations to international
scientific conferences and meetings.

I would like to thank Nicolas for his help, his support and for the technical discussions.

Finally, many thanks to my family, my parents Nadia and Kiril Dimitrovi, my brother Miroslav, my aunt Maria, for the permanent support, for the motivation and for their hope and their optimism.

Versailles, 12 November 2020

Z.D.
# Abstract

The main threads of the researches presented in this habilitation manuscript are the improvement of the energy efficiency of the vehicles energy systems, their emissions reduction, presenting competitive cost effective solutions of the energy storage and conversion technologies. The research considers energy efficiency, economic and environmental impact studies of innovative energy systems. In this manuscript the vehicles energy system evolves from autonomous on-board system to grid related system, with multi-energy vectors. Three main research topics are developed:

* Energy efficiency, storage and conversion on vehicles board

Internal combustion engines have limitation on efficiency due mainly to the heat losses. Processing an energy integration, it has been shown than an organic Rankine cycle can improve the overall powertrain efficiency by recovering the heat into electricity. In parallel, hybrid electric vehicles attractiveness increases as it enables important energy saving. The energy recovery potential of a single stage organic Rankine cycle for a thermal engine in combination with a mild hybrid electric powertrain is studied. The environomic methodology for design is applied on hybrid electric vehicles and electric vehicles, in order to explore the techno-economic and environmental trade-off for different hybridization level of the vehicles powertrains.

The variable engine valve trains are important for the gas exchanges and influence directly the effective efficiency of the internal combustion engine. This research presents an innovative application of an electromagnetic actuator for a future variable engine valve train. The design method is inversed to create a control law model. The control architecture is designed for a robust control of the actuator. The investigations show that very good dynamic performances of the controller are obtained.

The problems of the range and the energy storage of the vehicle on board are important and are introduced. Multi-objective optimization method is applied to estimate the optimal vehicles energy system designs for urban mobility and for long way electric mobility (> 500 km). Lithium-Ion battery systems become a major storage component in vehicle drivetrains. In this research, the investigation of the energy balance evolution for Li-Ion battery system is described. A method to determine the energy balance through experimental investigations and validate the model is also proposed.

* Extension of the vehicle energy systems, the vehicles as grid related systems

The recharging of the vehicles and their connection to the energy grid became an important question. The energy system is extended and includes the energy storage and conversion of vehicles and their connection to the grid. The extended energy system is modelled on vehicles flows on the highway and a single objective optimization is performed to define the optimal energy storage capacity of the vehicles and the optimal number of recharges of from the grid. The recharging time and the occupation rates of the chargers are as well evaluated. The optimality is researched for long way drives on electric mode.

An innovative mobile robot, equipped with energy power system, does the recharging of the high voltage batteries. The navigation function is central in the design of the mobile robot. The experimental results on a mobile robot platform show a distance error lower than 0.7% and an angle error less than 0.5%, promising for the acceptable accuracy of the system.

This research studies an innovative concept (SOFC-GT a Solid Oxide Fuel Cell with gas turbine module) for vehicle propulsion, considered to deliver mobility services and integrated energy services for a household. The major advantage of the energy integrated dual system is the reduction of the total global
warming potential (GWP) impact by around 30000 kg CO₂ eq. (60%) (from 50590 kg CO₂ eq. to 20338 kg CO₂) in comparison to separate energy services for mobility and household needs.

* Energy storage and conversion systems for energy vectors; fuel cell electric vehicles, hydrogen and their ecosystem

This report researches the use of SOFC system powered by hydrogen produced through reforming of liquid fuel from renewable resources such as bioethanol. The SOFC installation has an integrated fuel reformer. Ni doped with a small amount of Pd as an active phase components is used for catalyst of the bio-ethanol steam reforming. The catalyst, which is used during the process, has an important role to achieve 100% conversion because it increases the rate of reaction in such a way that the system tends toward thermodynamic equilibrium.

To consider the impact of the different energy vectors, well-to-wheels balance on the energy efficiency and the CO₂ emissions is done and discussed for different vehicles propulsion systems.

**Keywords:**

Energy storage, energy conversion, vehicles propulsion systems, energy grids and systems, multi-objective optimization
# Résumé

Le fil conducteur des recherches présentées dans ce manuscrit d'habilitation est l'amélioration de l'efficacité des systèmes énergétiques des véhicules, la réduction de leurs émissions, au travers de solutions compétitives et rentables de technologies de stockage et de conversion d'énergie. La recherche porte sur l'efficacité énergétique ainsi que sur les études d'impacts économiques et environnementaux des systèmes énergétiques innovants. Dans ce manuscrit, les systèmes énergétiques des véhicules évoluent des systèmes autonomes embarqués vers des systèmes liés au réseau, avec des vecteurs multi-énergies. Trois grands thèmes de recherche sont développés:

* Efficacité énergétique, stockage et conversion à bord des véhicules

Les moteurs à combustion interne ont une limitation de l'efficacité due principalement aux pertes thermique. En faisant une intégration énergétique, il a été démontré qu'un cycle de Rankine à fluide organique peut améliorer l'efficacité globale du groupe motopropulseur en récupérant la chaleur et en la transformant en électricité. Parallèlement, l'attractivité des véhicules électriques hybrides augmente car elle permet d'importantes économies d'énergie. Le potentiel de récupération d'énergie d'un cycle de Rankine à fluide organique pour un moteur thermique en combinaison avec une chaine de traction à faible hybridation électrique est étudié. La méthodologie de conception « environomique » est appliquée aux véhicules hybrides électriques, afin d'explorer le compromis technico-économique et environnemental pour différents niveaux d'hybridation des groupes motopropulseurs des véhicules.

Les systèmes de distribution à levées de soupapes variables sont importants pour les échanges de gaz et influencent directement l'efficacité effective du moteur à combustion interne. Cette recherche présente une application innovante d'un actionneur électromagnétique pour un futur système de distribution variable. La méthode de conception est inversée pour créer un modèle de loi de contrôle. L'architecture de commande est conçue pour une commande robuste de l'actionneur. Les investigations démontrent de très bonnes performances dynamiques du contrôleur de l'actionneur.

Les problèmes d'autonomie et de stockage d'énergie à bord des véhicules électriques sont importants et sont introduits. Une méthode d'optimisation multi-objectifs est appliquée pour estimer les conceptions optimales de systèmes énergétiques des véhicules électriques pour la mobilité urbaine et pour la mobilité électrique à longue distance (> 500 km). Les systèmes de batterie au lithium-ion deviennent un élément de stockage majeur dans les systèmes de propulsion des véhicules. Dans cette recherche, l'étude de l'évaluation du bilan énergétique de la batterie Li-Ion est décrite. Une méthode pour déterminer le bilan énergétique par des investigations expérimentales et valider le modèle est également proposée.

* Extension des systèmes énergétiques des véhicules, les véhicules en tant que systèmes liés au réseau

La recharge des véhicules et leur connexion au réseau énergétique est devenue une question importante. Le système énergétique est étendu et comprend le stockage et la conversion d'énergie des véhicules et leur connexion au réseau. Le système énergétique étendu est modélisé par des flux de véhicules sur l'autoroute et une optimisation est réalisée pour définir la capacité optimale de stockage d'énergie des véhicules et le nombre optimal de recharges sur les trajets. Le temps et les taux d'occupation des chargeurs sont également évalués. L'optimalité est recherchée pour les longs trajets en mode électrique.

Un robot mobile innovant, équipé d'un système d'alimentation en énergie, recharge les batteries haute tension. La fonction de navigation est centrale dans la conception du robot mobile. Les résultats expérimentaux sur une plateforme de robot mobile montrent une erreur d'estimation de la distance
inférieure à 0,7% et une erreur d'estimation d'angle inférieur à 0,5%, promettant une précision acceptable du système de navigation.

Cette recherche étudie un concept innovant (SOFC-GT une pile à combustible à oxyde solide, couplé à une turbine à gaz) pour la propulsion des véhicules, considéré comme fournissant des services de mobilité, et des services énergétiques intégrés pour un ménage. Le principal avantage du système d'énergie intégrée combiné est la réduction de l'impact total du potentiel de réchauffement climatique (GWP) d'environ 30000 kg CO₂ éq. (60%) (de 50590 kg CO₂ éq. à 20338 kg CO₂ éq.) par rapport à des services énergétiques séparés pour la mobilité et les besoins des ménages.

* Systèmes de stockage et de conversion d'énergie pour les différents vecteurs énergétiques; véhicules électriques à pile à combustible, hydrogène et leur écosystème

Ce rapport étudie l'utilisation d'un système d'extension d'autonomie sur la base d'une pile à combustible à électrolyte solide (SOFC), alimenté par l'hydrogène produit par le reformage du carburant liquide à partir de ressources renouvelables telles que le bioéthanol. L'installation SOFC dispose d'un reformeur d'hydrogène intégré. Le Ni dopé avec une petite quantité de Pd est utilisé comme catalyseur du reformage du bioéthanol. Le catalyseur, qui est utilisé pendant le processus, a un rôle important pour atteindre une conversion de 100% du bioéthanol en hydrogène car il augmente la vitesse de réaction de telle manière que le système tend vers l'équilibre thermodynamique.

Pour prendre en compte l'impact des différents vecteurs énergétiques, le bilan du puits à la roue de l'efficacité énergétique et des émissions de CO₂ est réalisé et discuté pour différents systèmes de propulsion des véhicules.

## Mots clés:

Stockage d'énergie, conversion d'énergie, systèmes de propulsion des véhicules, systèmes et réseaux d'énergie, optimisation multi-objectifs
# Contents

<table><tr><td>Acknowledgements</td><td>v</td></tr><tr><td>Abstract</td><td>vii</td></tr><tr><td>Keywords:</td><td>viii</td></tr><tr><td>Résumé</td><td>ix</td></tr><tr><td>Mots clés:</td><td>x</td></tr><tr><td>Contents</td><td>xi</td></tr><tr><td>Summary</td><td>xiii</td></tr><tr><td>Chapter 1 Introduction</td><td>1</td></tr><tr><td>1.1 Context of the research</td><td>1</td></tr><tr><td>1.2 Main research questions and domains</td><td>3</td></tr><tr><td>1.3 Personal experiences and motivations</td><td>6</td></tr><tr><td>1.4 Outline and contribution of the present work</td><td>7</td></tr><tr><td>Chapter 2 Literature review</td><td>9</td></tr><tr><td>2.1 Energy efficiency, energy storage and conversion on vehicles board</td><td>9</td></tr><tr><td>2.2 Extension of the vehicle energy systems: the vehicles as grid related systems to the energy grids for optimal energy services for mobility</td><td>13</td></tr><tr><td>2.3 Fuel cell electric vehicles, energy vectors for hydrogen and their ecosystem</td><td>16</td></tr><tr><td>Chapter 3 Energy efficiency, storage and conversion on vehicles board</td><td>21</td></tr><tr><td>3.1 Energy efficiency</td><td>21</td></tr><tr><td>3.2 Energy converters on-board</td><td>26</td></tr><tr><td>3.3 Energy storage on-board – high voltage batteries</td><td>35</td></tr><tr><td>Chapter 4 Extension of the vehicle energy systems: the vehicles as grid related systems to the energy grids for optimal energy services</td><td>51</td></tr><tr><td>4.1 Design of electric vehicles grid related energy systems for optimal mobility service</td><td>51</td></tr><tr><td>4.2 Coordination interest for the optimization of electric vehicles long distance trips</td><td>57</td></tr><tr><td>4.3 Slow charging: Robotized charging of high voltage batteries for electric and hybrid electric vehicles</td><td>64</td></tr><tr><td>Chapter 5 Energy storage and conversion systems for energy vectors: fuel cell electric vehicles, hydrogen and their ecosystem</td><td>73</td></tr><tr><td>5.1 Energy balance of the production and the conversion of hydrogen in the fuel cells</td><td>76</td></tr><tr><td>5.2 Hydrogen from (bio)-ethanol steam reforming, selection of the catalysts</td><td>78</td></tr></table>
<table><tbody><tr><td>5.3</td><td>A well-to-wheels balance of the different energy storage and conversion technologies for sustainable mobility solutions</td><td>90</td></tr><tr><td><strong>Chapter 6</strong></td><td><strong>Conclusions</strong></td><td><strong>95</strong></td></tr><tr><td>6.1</td><td>Energy efficiency, energy storage and conversion on vehicles board</td><td>95</td></tr><tr><td>6.2</td><td>Extension of the vehicle energy systems: the vehicles as grid related systems to the energy grids for optimal energy services for mobility</td><td>96</td></tr><tr><td>6.3</td><td>Energy storage and conversion systems for energy vectors: fuel cell electric vehicles, hydrogen and their ecosystem</td><td>97</td></tr><tr><td>6.4</td><td>General conclusion of the research work</td><td>98</td></tr><tr><td><strong>Chapter 7</strong></td><td><strong>Research perspectives</strong></td><td><strong>99</strong></td></tr><tr><td>7.1</td><td>Goals and philosophy</td><td>99</td></tr><tr><td>7.2</td><td>Research axes</td><td>99</td></tr><tr><td>7.3</td><td>From the method to the tool</td><td>100</td></tr><tr><td>7.4</td><td>Applications</td><td>101</td></tr><tr><td><strong>Appendix A</strong></td><td><strong>Methodology</strong></td><td><strong>103</strong></td></tr><tr><td>A.1.</td><td>Multi objective optimization and energy integration</td><td>103</td></tr><tr><td>A.2.</td><td>Economic models</td><td>107</td></tr><tr><td>A.3.</td><td>Environmental model: Life cycle assessment</td><td>109</td></tr><tr><td><strong>Appendix B</strong></td><td><strong>Models</strong></td><td><strong>111</strong></td></tr><tr><td>B.1.</td><td>Simulation models: EV, HEV, SOFC</td><td>111</td></tr><tr><td><strong>Appendix C</strong></td><td><strong>Energy efficiency, storage and conversion</strong></td><td><strong>123</strong></td></tr><tr><td>C.1.</td><td>Optimal designs for efficient mobility service for hybrid electric vehicles</td><td>124</td></tr><tr><td>C.2.</td><td>Optimal designs of electric vehicles for a long-range mobility</td><td>133</td></tr><tr><td><strong>Appendix D</strong></td><td><strong>Mobile Robotics</strong></td><td><strong>139</strong></td></tr><tr><td>D.1.</td><td>Perception</td><td>139</td></tr><tr><td>D.2.</td><td>Localization: SLAM</td><td>139</td></tr><tr><td>D.3.</td><td>Cognition: Mobile Robot Navigation Algorithms</td><td>140</td></tr><tr><td><strong>Appendix E</strong></td><td><strong>Extended energy systems</strong></td><td><strong>141</strong></td></tr><tr><td>E.1.</td><td>Integration of the environmental energy services for mobility and household using electric vehicle with a range extender of solid oxide fuel cell</td><td>141</td></tr><tr><td><strong>References</strong></td><td></td><td><strong>151</strong></td></tr><tr><td><strong>Nomenclature</strong></td><td></td><td><strong>159</strong></td></tr><tr><td><strong>List of Figures</strong></td><td></td><td><strong>171</strong></td></tr><tr><td><strong>List of Tables</strong></td><td></td><td><strong>177</strong></td></tr><tr><td><strong>Curriculum Vitae</strong></td><td></td><td><strong>179</strong></td></tr></tbody></table>
# Summary

The main thread of the researches presented in this habilitation manuscript are the improvement of the energy efficiency of the vehicles energy systems, their emissions reduction, presenting competitive cost effective solutions of the energy storage and conversion technologies. There is a need of holistic design method for the future energy systems that considers on a simultaneous way the technical, economic and environmental challenges. For this reason, the research considers energy efficiency, economic and environmental impacts studies of innovative energy systems. In this manuscript the vehicles energy systems evolve from autonomous on-board systems to grid related system, with multi-energy vectors. This extended energy system considers the different energy grids (electricity, natural gas, hydrogen) and is integrated for large-scale improvement of the system efficiency, effective cost investment and environmental impacts reduction. The new integrated energy system has to deliver effective, affordable and sustainable energy services for mobility, electricity and heating. The manuscript is organized in 7 main chapters:

* Introduction

* Literature review

* Energy efficiency, storage and conversion on vehicles board

* Extension of the vehicle energy systems: the vehicles as grid related systems to the energy grids for optimal energy services for mobility

* Energy storage and conversion systems for energy vectors: fuel cell electric vehicles, hydrogen and their ecosystem

* Conclusions

* Research perspectives

## Energy efficiency, storage and conversion on vehicles board

In this chapter, the vehicle is considered as energetically autonomous system. This means that the energy system is limited on the storage and conversion on-board. The main energy services to be delivered by the onboard energy systems are the mobility and the cabin heating and cooling. The research domain considers three main domains: optimization of the global energy efficiency of the system (considering powertrain architectures), improvement of the efficiency of the main energy converters (for example internal combustions engines) and the optimization of the operating conditions of the high voltage batteries, which are the main storage system for the electric vehicles. This research category regroups from one side modelling, simulation and optimization works, and from the other one experimental tests and test benches design and realization. The research is realized in the period 2012-2020 and implements works on methodology development (Optimization of the energy efficiency) and technology development (Camless electromagnetic actuator and busbars for Li-Ion battery pack).
The following research items are included:

* Energy efficiency:

* Waste heat recovery

* Optimal design for efficient mobility service for hybrid electric vehicles

* Energy conversion:

* Development of robust control of an electromagnetic actuator

* Energy storage:

* Optimal designs for a long-range electric mobility

* An energy balance evaluation in Li-Ion battery pack under high temperature operation

**Energy efficiency:**

In this chapter, different technologies for energy recovery (kinetic and thermal) and their combination in different powertrain architectures are considered for improvement of the energy efficiency. The organic Rankine cycle is evaluated as waste heat recovery technology. The electrification of the ICE based powertrains brings different hybrid electric architectures, classified functionally according to their degree of hybridization and the size of the high voltage battery. The HEVs recover the kinetic energy during braking but are powered by ICEs, which present still thermal losses. The research question to investigate in this chapter is the efficiency improvement by the combination of two type energy recovery systems (HEV and ORC).

The optimal design of the hybrid electric vehicles for the kinetic energy recovery and the choice of the optimal battery size of the high voltage battery for HEV are as well investigated according to techno-economic and environmental criteria. This is a published article and is presented in Appendix C.

The research applies multi-objective and energy integration methodology.

To sum up, this research presents studies of the efficiency performance and the economic optimization for a middle class vehicle with a downsized gasoline engine. Two technological options are studied for the efficiency improvement of the baseline vehicle powertrain:

* Low degree electric hybridization technology – called Mild Hybrid electric powertrain

* Waste heat recovery system for two heat sources the engine water circuit and the exhaust gases, means an external thermodynamic cycle – ORC

The **main conclusion** coming from the driving cycles evaluation is the MHEV is an efficient technology for the drives with speed variations. The selected ORC design is efficient on the cycles using the charged area of the engine. The MHEV and the ORC are complementary technologies and their combination is especially efficient on the combined cycles containing urban and extra urban part. For the combined cycles and peri-urban the combination of ORC and MHEV bring the best fuel saving potential, superior to 35% in comparison with normal ICE vehicle.

The optimal environmental configurations for hybrid electric vehicles are researched by using multi objective optimization techniques. The optimization methodology is based on a genetic algorithm and is applied for defining the optimal set of decision variables for powertrain design. The analysis of the environmental Pareto curves on NEDC illustrates the relation between the economic and the environmental performances of the solutions. The life cycle inventory allows calculating the environmental performance of the optimal techno-economic solutions. The environmental and the economic trades-off are defined for the different impact categories. Their impact for the production phase and the use phase of the vehicle is studied. The sensitivity of the impacts categories on the electricity production mix is as well studied. The solutions in the lowest emissions zone show that the maximal powertrain efficiency on NEDC is limited on 45.2% and the minimal tank-to-wheel CO₂ emissions are 30 g CO₂/km. They have the maximal cost – 75000 Euros. The increase of the electric part of the powertrain increases all environmental categories, because of the materials and the processes to produce the electric components.
**Energy conversion:**

In this part of the chapter, it is presented the development of an innovative electromagnetic actuator for engine valve train. This converter acts on the principle of linear electric machine, in bi-modes, converting electricity to force or force to electricity. The application of the designed innovative actuator is to replace the total mechanical engine valve train. This research contributes to improve the internal combustion engine (ICE) effective efficiency, by reducing the mechanical friction and by increasing the induced (thermodynamic) efficiency of the ICE.

The contribution of the presented research work is the use of an innovative concept of engine valve train based on Lorentz force electromagnetic actuator, which is characterized by performant and efficient magnetic circuit, lightweight moving part, direct driven by the current. This is an active actuator, which solves the problems highlighted in the previous researched – the control robustness and precision. A method for control, based on simulation and experimental steps is presented for the innovative actuator. The performances of the control are assessed. Test bench is built for the control research.

**To conclude**, the Lorentz force based electromagnetic actuator was developed and experiments are used to identify unknown model parameters for robust controller design. Simulations and experimental results show that a control law structure with a nonlinear feedforward and a plating strategy allow a real improvement of the convergence valve trajectory to a desired trajectory and make the actuator less sensitive to disturbances and parameter variations. The precision of the control law attests the controllability of the actuators technology and thus the good performances of the engine. The robust and precise controlling of the actuators on the intake side is especially important for the regulation of the airflow in the engine and this for gasoline engine concerns directly the regulation of the engine torque. The respect of the velocity during the valve landing requirements (at 6000 rpm 0,41 m/s measured for 0,5 m/s criterion for acceptability) is an important step and insures the respect of the noise criteria and the mechanical durability of the system.

**Energy storage:**

The compactness and the energy density are the main technology challenges in front of the energy storage in high voltage batteries for electric vehicles. The range of the electric vehicles and the range of electric mode for the electrified vehicles is the main challenge. This chapter investigates optimal design methods to increase the range of the electric vehicles. The trade-off of the electric vehicles is illustrated. The operational parameters and especially the temperature in the battery pack is an important parameter for the good performances of the batteries. Energy balance of the high voltage battery pack is investigated by experimental way in this chapter. The experimental data of the energy balance are used to design effective cooling strategies and technologies for batteries cooling. The experimental data allow as well modelling the thermal behavior of the batteries. The thermal models can be used to monitor the state of change and the state of health of the high voltage battery. Experimental test bench for the battery pack measurements was designed and operated to establish the energy balance.

**In conclusion**, this research presents a thermal energy balance of two types of the battery packs during the discharge process. One battery package was equipped with Al busbars while the other had Cu busbar. The investigated battery packages were made of sixteen cylindrical cells. The calculation of the energy balance allowed to calculate the amount of generated heat inside the battery packages which is key information from the operation safety point of view. The conducted calculations were validated by the measurements of real battery package. The difference between the calculated model and the measured values were 6% for the battery package with Cu busbar and 1% for the battery package with Al busbar. By replacing the Al with Cu busbar it is possible to reduce the generated heat $Q_{GEN}$ by 10%. Cu- and Al-based busbars serves to the electrical connections in the battery pack, but as well serve as solution of passive cooling to evacuate the heat from the battery pack.
# Extension of the vehicle energy systems: the vehicles as grid related systems

The recharging of the vehicles and their connection to the energy grid became an important question.

This research contains the following items:

* Design of electric vehicles grid related energy systems for optimal mobility service

* Coordination interest for the optimization of electric vehicles for long-distance trips.

* Slow charging: robotized charging of high voltage batteries for electric and hybrid electric vehicles

* Integration of the environomic energy services for mobility and household using electric vehicle with a range extender of solid oxide fuel cell

The works are developed in the period 2017-2020 with the Robot charger project and the PhD thesis of my student Jean Hassler. The "Robot charger" is an experimental project with the development of a functional prototype and charging of the high voltage batteries of an electric vehicle with innovative wireless inductive technology for charging.

This chapter considers the recharging ecosystem and the design of the electric vehicles grid related energy systems for optimal mobility service. The question is to research recharging strategies that extend the autonomy of the electric vehicles and allow using electric vehicles for long range trips. The communication strategy and the coordination of the recharging plans of the vehicles reduce the waiting times on the recharging stations, increase the rate of use of the stations and optimize the number of chargers and so reduce the investment costs that are needed. The electric vehicles are considered as related to the electricity grid. The optimal recharging plan and the coordination of the recharging between the vehicles and the recharging infrastructure allow using electric vehicles for long distance trips. The rapid charging infrastructure is invested and deployed for the high ways. The domestic recharging systems are slow speed systems and the recharging operation at home is a frequent operation. An innovative robotized solution for recharging is investigated to do the recharging task at home. This innovation is called Robot Charger. The household is considered as cluster for energy integration where energy services for mobility and household needs such as electricity and heating are integrated. In this cluster integration the vehicle provides energy services to the household when is not used. The vehicle is equipped with highly efficient energy propulsion system based on electric vehicle with a range extender module of solid oxide fuel cell, using renewable fuel. The environomic multi-objective optimization is used for the energy services integration.

The following conclusions are coming out.

This research focused on the development of a smart cost and simple localization system for a robot, used for recharging of the batteries in an unknown environment. The localization in an environment is a fundamental capacity for a mobile robot. Smart cost platform means the use of low cost sensors and low cost hardware, thus the algorithm of localization has to run on low power computer and with simple sensors. The algorithm must respect three main constraints: computation efficiency, accuracy of the approximation of the position and ease of implementation. After implementing our platform on a three Omni wheels robot with a LIDAR and encoders, set up the communication protocols and set the parameters, a reference map was successfully build. According to the test's results, the tracking performance is really good at moderate speeds (100 - 200 mm/s) as well as the localization accuracy with an error maximum of 2 cm. Thus, the SLAM is efficient enough to meet the requirements for the application.
The designs for different mobility services (urban and long ways distance electric mobility) of the electric vehicles are defined and the time interaction with the charging infrastructure is analyzed. Information and connectivity solutions that inform in real time the drivers for the availability and the location of the charging infrastructure, including the possibility to book and pay for charge points in different places is an important part of the solution. Combined with fast, i.e. high power charging capacities, pure electric vehicles would be able to serve long range needs. The analysis of the environmental Pareto curves on NEDC illustrates as well the relation between the economic and the environmental performances of the solutions.

This research presents the developed method and its modeling to find the optimal strategy for recharging on the highway for a flow of electric vehicles, in the case of long-range electric mobility. The flows are composed by different numbers of vehicles that have to travel long range distance on the highway. They have to stop and recharge their batteries on the chargers located on the stations. The waiting and recharging times are evaluated, considering the stops, the occupation rate, and the location of the chargers. In a first level simple optimization is performed by minimizing the waiting time for charging. The order of the vehicles stops their recharging strategy come out as results. Differential evolutionary genetic algorithm is used to solve the optimization problem. The initial population of the vehicles flows is generated randomly and the genetic algorithm operators (evaluation, selection and mutation) are used to converge to the solutions. The results from the study cases show that an additional number of chargers are needed to be installed to minimize the waiting time on the highway and to reduce the waiting and the recharging time on the highway stops. The investment cost of the chargers and the infrastructure has to be as well considered.

The vehicle is considered as grid related to the house and this means that its energy service of mobility and comfort is related to the energy services for the family house. The study contributes to consider in an integrated scale mobility and energy services for a household and introduces the use of the vehicle in dual mode. Solid oxide fuel cell coupled to gas turbine (SOFC-GT) module is introduced as a range extender of the autonomy of the electric vehicle. A base energy scenario for the house is also defined. The total yearly cost for the integrated system in dual mode stays still higher (1000 €/year) than the base case separately delivered energy services. This is due to the high investment cost of the equipment in the vehicle and the house. The major advantage of the energy integrated dual system is the reduction of the total GWP impact with around 30000 kg CO₂ eq. (60%). - from 50590 kg CO₂ eq. to 20338 kg CO₂ eq. This study illustrates large scale economic and environmental benefits opportunities by integration of the energy services for mobility and household.

## Energy storage and conversion systems for energy vectors: fuel cell electric vehicles, hydrogen and their ecosystem

This chapter presents the integration of the energy grids of electricity, natural gas and hydrogen. Renewable resources of energy are important for the production of clean hydrogen. The global well to wheels CO₂ emissions are reduced with the utilization of a green hydrogen. The hydrogen is an energy vector that can be used as energy storage of renewables energies in the energy grids.

The research is divided on three parts:

* Energy balance of the production and the conversion of hydrogen in the fuel cells

* Hydrogen production from (bio)-ethanol steam reforming, catalysts selection

* Well-to-wheels balance of the CO₂ emissions of the different energy storage and conversion technologies for sustainable mobility solutions

The research work is done in the period 2016-2020. The experimental works on the catalyst systems are done with the chemical department of the Lodz University of Technology (Poland). The global environmental impacts and comparisons of the different conversion and storage systems (well-to-wheels CO₂ emissions) are assessed in the frame of the workshops between PSA and Airbus.
This chapter presents innovative propulsion system with high efficiency, using fuel cells. Hydrogen is considered as clean energy, but several questions related to its production, its large-scale storage and distribution remains. The current state of the art of fuel cell vehicles is the use of pressurized hydrogen gas at 700 bar, stored on board the vehicles and converted into electricity by a proton-exchange membrane fuel cell (PEMFC). The storage and the distribution under 700 bar of hydrogen pressure present technical, economic and environmental problems. The idea of the research in this field is to propose a concept of energy storage under form of bioethanol and to convert bioethanol to hydrogen on board the vehicle by a solid oxide fuel cell (SOFC).

The hydrogen production is considered for different energy vectors and their ecosystem. This chapter presents the concept of electric vehicle with serial range extender to extend the vehicle autonomy. Fuel cell system powered by hydrogen produced through reforming of liquid fuel from renewable resources such as bioethanol is considered as a range extender module. The energy balance of the different types of fuel cells, powered by bio-ethanol is investigated. Three variants for ethanol conversion are investigated: variant 1: reforming of bio-ethanol in external reformer to hydrogen and its conversion in alkaline fuel cell, variant 2: reforming of bio-ethanol in external reformer to hydrogen and its conversion in a solid oxide fuel cell; variant 3: direct reforming of the bio-ethanol in the solid oxide fuel cell. The chemical processes for each variant are proposed and thermodynamic energy balance is calculated. The catalyst system for the bio-ethanol steam reforming is investigated on experimental way. It is needed a high active and selective catalytic system delivering 100% of bio-ethanol conversion to hydrogen. The total conversion of bio-ethanol to hydrogen is essential from the economic point of view. The catalyst which is used during the process has an important role to achieve 100% conversion. Typical catalytic systems used in steam ethanol reforming processes typically include precious metals Rh, Pd, Pt, Ru and Ir in monometallic formulations. Bimetallic systems are couple of precious metals with smaller amounts of transition e.g. Pd-Ni, Rh-Ni, Rh-Co and others. In view of the high price of noble metals, it is suggested to use higher concentrations of transition metals doped with small amounts of no noble metals eg. Ni doped with a small amount of Pd as an active phase components. The catalysts selection is done with laboratory experiments. While as a support it is suggested the usage of binary oxide system containing Al₂O₃, ZnO or CeO₂ or ZrO₂. The main goal of this research is to select the most active and selective system which can be directly used in micro-reactor before the SOFC fuel cell. During the research tasks the most suitable liquid fuel will be selected and will be used as a substrate for hydrogen generation and finally to produce electricity using a SOFC fuel cell.

To consider the impact of the different energy vectors, well-to-wheels balance of the CO₂ emissions is done for different vehicles propulsion systems. The impact on the energy efficiency and the emissions is discussed.

**To conclude**, in view of the high price of noble metals, it is suggested to use higher concentrations of transition metals doped with small amounts of no noble metals eg. Ni doped with a small amount of Pd as an active phase components. The catalysts selection is done with laboratory experiments. While as a support it is suggested the usage of binary oxide system containing Al₂O₃, ZnO or CeO₂ or ZrO₂. After the investigations, the most optimal content of nickel for monometallic nickel supported catalyst is 5 % wt. of Ni. Further increase of the nickel content does not increase significantly the catalytic efficiency. All catalytic systems showed high activity and selectivity at high temperature of the reaction especially above 500°C. The main products obtained during the process were CO, CO₂ and H₂. The high stability and high selectivity to hydrogen formation for mono and bimetallic systems was confirmed.

SOFC with integrated reformer of the bioethanol is the best fuel cell for the concept for a range extender unit. It presents the best energetic balance, from energy conversion point of view. In conclusion, it is proposed the working principle of the bioethanol fuel cell for range extender unit for vehicle application with 15.15 MJ of electric energy output per kilogram of converted bio-ethanol.
As main conclusion, the well-to-wheels (WtW) CO₂ emissions depend of the efficiency of the conversion chain. They are strongly influenced by the electricity production mix. Fuel cell and electric powertrain technology help to reduce considerably the WtW CO₂ emissions, if we use low carbon electricity production mix and low carbon process for the hydrogen production. The production of electricity or hydrogen from renewable resources is a viable solution for low WtW CO₂ emissions. The use of renewable electricity for the electrolysis to produce hydrogen, is the best configuration for almost zero CO₂ emissions, 3 g CO₂/km

## Research perspectives

This part presents the future works for the 4 coming years in the scientific department of PSA Groupe. The presented works on the technologies development will continue as innovations works to evaluate the technical and the economic maturity for the market.

In the context of the strong regulation of CO₂ emissions reduction, my research project is to implement the developed optimization method to a tool for the evaluation of energy integrated systems. The tool can be used for prospective research of solutions for affordable and sustainable mobility for the period 2030-2050.

### From the method to the tool

User platform can be created, implementing libraries related to the technologies, different energy resources and as well energy demand profiles. The optimization structure contains the simulation models and the optimizer based on genetic algorithm. As results the tools gives information about the sizing and the operation strategy of the technological equipment, distribution of the heat for city energy needs, mobility energy strategies for public and private transportation or consideration of the logistic strategy for goods, per rail or road.

The time horizon is for research is 2030 to 2050 and the temporary dynamic profiles could be considered, monthly or hourly. The solving algorithms are using mixed-integer nonlinear programming. The following characteristics can be noticed:

* decision variables,

* Parametrized assumptions & constraints

* Multiple indicators: Cost, CO₂, primary Energy, Exergy, Employment, Waste

* Uncertainties, Monte-Carlo Simulation

### Applications:

The following applications can be developed:

* Evaluation of the services that electric vehicles could provide to the electric grid

* Evaluation of the strategies to use renewable energies in the mobility scenarios

* Evaluation of the scenarios of production and storage, distribution of clean hydrogen from biomass
<figure><img src="image_3.png" /></figure>
# Chapter 1 Introduction

## 1.1 Context of the research

Automotive transport is governed by strict environmental regulations. They are local, national or international. The European regulation for CO₂ emissions requires the lowest levels of emissions per kilometer in comparison with other regions of the world. Figure 1-1 illustrates the CO₂ emissions forecast per regions. It is visible that there is a worldwide convergence for the reduction of the CO₂ emissions. The CO₂ emissions levels are very low in Europe going from 130 gCO₂/km in 2015 to 95 gCO₂/km in 2020. The next steps in Europe are to decrease the average value of emission per fleet to 75 gCO₂/km in 2025. From 2025 the first time the European automotive market will be driven by the environmental regulations. The massive penetration of electric vehicles is expected from 2023, because the market is being prepared. The European carmakers are already being developing, industrializing and commercializing electric vehicles.

<figure><img src="image_4.png" /><figcaption>Figure 1-1: CO₂ emissions regulatory forecast per regions</figcaption></figure>

By 2030 the CO₂ emissions have to be reduced to 50 gCO₂/km, representing a slope of 30% reduction in comparison of the 2025 level. The target of 50 gCO₂/km is ambitious and demands global investment in the energy technologies for vehicles. The regulation of the CO₂ emissions is given in a tank-to-wheels vision. This means that the regulation considers only the emissions during the use of the vehicles. The emissions for the production of the fuels are not considered. The global emissions from life cycle point of view, including well-to-wheels analysis and impact of the energy vectors production are not considered for the moment. The European parliament supports the targets of 32% for renewables in 2030 and minimum 14% of renewable energy in the transportation sector in 2030.

Decarbonisation and emission reduction from road transport are the main drivers for the electrification of vehicles, (xEV). The envisaged European CO₂ fleet emission limits for 2025 and 2030 already require a massive introduction of partially electrified vehicles (range extenders (REX), plugin hybrid electric vehicles (PHEV)) or full EV's (battery electric vehicles (BEV), fuel cell electric vehicles (FCEV)) [1] Further, local or regional air quality regulations such as potential zero emission zones will drive demand for these vehicles.
According to the ERTRAC[1] report the electrification of the road transport will significantly contribute to the improvement of air quality and the climate protection. To do that the electricity used has to be from low carbon / carbon neutral sources, (solar, wind, hydro, geo thermal). In the European electricity roadmap, it is assumed that electricity (or hydrogen) will be provided on an increasingly CO2-neutral basis.

The interest of the introduction of the electrification technologies in the automotive sector is explained in many researches [2], [3] and others. Today the state of the art considers that the technologies needed for the electrification of the vehicles gained in maturity and the largest deployment would allow decreasing the prices of the electrified vehicles. The users' expectations are the same as for the conventional fossil fuels vehicles:

* Prices as low as todays ICE driven vehicles or at least vehicles with total cost of ownership competitive to those as of today

* Range, reliability, durability and re-sale value of electrified vehicles similar to conventional vehicles

* Range adapted to specific use cases

* Usage comfort as good as the state of the art ICE-powered vehicles (availability, "re-fueling" time and possibilities, passenger comfort, transport volume)

* Safe parking and infrastructure also with direct connection to charging facilities

To meet the targets of sustainable development and greenhouse emission reduction of the future vehicles fleet, the automotive industry needs to deploy cost-competitive and efficient advanced energy conversion systems for the future commercial personal vehicles.

The challenges of the research work are to implement in the design action, the energy balance optimization for reducing the CO2 and the environmental impacts. The design system has to satisfy the regulation of energy efficiency and the energy classes imposed on the future vehicle fleet and to consider the global energy vision. The design of the energy conversion systems for passengers' cars requires the consideration of the economical competiveness, the highest conversion efficiency and the lowest environmental impacts. The approach in this work consists to choose adapted technology (technological options), give a cost vision and define environmentally optimal energy solutions for mobility. This work consider the energy system for mobility based on the energy storage and conversion systems on vehicle board and the energy and the conversion of the energy vectors for mobility.

The research questions are:

* How the design methods have to be modified to incorporate these competitiveness problems – energy efficiency, CO2 and pollutant emissions, and cost, in the future vehicles and the energy service for mobility?

* Can one implement the environmental criteria in the design step of an efficient energy storage and conversion chain?

* Can we consider the vehicles as energy grid related systems, delivering optimal energy service for mobility?

This research proposes an evaluation method, based on multi-objective mathematical optimization, which can consider on a holistic way the objectives and proposes a structured vision on the possible decisions. The novelty of this research is to present the optimization method and to discuss possible applications and results, considering new technological options for energy conversion and storage of the energy. Energy balance of high voltage batteries is performed. The vehicles are considered related to the electrical grid. The energy grid is larger, combining interconnections between the electricity grid and others energy vectors, such as the hydrogen and the natural gas. The main research questions discussed in this manuscript are presented in paragraph 1.2.
**1.2 Main research questions and domains**

The research work can be summarized in 3 main categories:

* Energy efficiency, storage and conversion on vehicles board.

* Extension of the vehicle energy systems. The vehicles as grid related systems to the energy grids for optimal energy services for mobility.

* Energy storage and conversion systems for energy vectors. Fuel cell electric vehicles, hydrogen and their ecosystem.

The following paragraphs introduce the research topics developed in the manuscript.

**1.2.1 Energy efficiency, storage and conversion on vehicles board**

This research domain considers the vehicle as autonomous system from energy point of view. The vehicle has to deliver two main energy services: for mobility and for thermal comfort. The thermal comfort is related to the heating and the cooling of the cabin. The main target is to improve the energy efficiency of the system to reduce the energy consumption, the emissions and to increase the range. The efficiency improvement of the global on board energy system is investigated through waste heat recovery and electric hybridization on a system level. The components of the system such the converters and the stockers are as well investigated for efficiency improvement on components level.

**Waste heat recovery**

The efficiency gains of the energy systems for vehicles are directly related to the fuel saving and the emissions reductions. The development of efficient technologies for the driving profiles and the comfort demands is then needed. There comes the idea to define a vehicle energy system with integrated energy services for mobility and comfort. With the definition of the system we need a methodology for the integration of the energy services. This methodology is called an energy integration methodology. The energy flows are integrated into the energy system to increase the efficiency. The energy integration considers the energy demand profiles for mobility and comfort and uses technologies recovering waste energy into the energy system. Organic Rankine Cycle (ORC) is selected to recover the heat waste of the internal combustion engine (ICE). Waste heat recovery technologies and hybrid electric vehicles are briefly introduced in the following sections.

**Optimal designs for efficient mobility service for hybrid electric vehicles**

Hybrid electric vehicles designs are investigated according to technical, economic and environmental optimizations. The novelty of the present study is the application of the environomic optimization methodology for optimal design and operation parameters of the vehicle energy system. Methods, techniques to analyze, to improve and to optimize energy systems have to deal not only with the energy consumption and the economics, but also with the environmental impacts. The systematic consideration of the thermodynamics, the economic and the environmental impacts in the design and the operation of the energy system is called environomics.

**Development of robust control of an electromagnetic actuator**

The camless valvetrain is considered as a promising solution for improvement of the engine performance and its efficiency. Most of the camless valvetrains, based on reluctance force, suffer the problem of high impacts at valve seating, which induces noise and restricts its mass production.
The difficulty for the engine valves actuators in the case of full camless engine is to cover the large range of forces and dynamics, required for operating correctly all intake and all exhaust valves. Camless engine means that such electromagnetic valves are deployed to the complete intake and exhaust side. From previous studies and experiences with reluctance force based actuators, we know that main difficulties for the realization of camless engine valvetrain are:

* the precision of the actuators, where landing, the chock of the valves inducing noise

* the density of the delivered force

* the high electric consumption

The novelty of the presented research work is the use of an innovative concept of engine valve train based on Lorentz force electromagnetic actuator, which is characterized by performant and efficient magnetic circuit, lightweight moving part, direct driven by the current. This is an active actuator, which solves the problems highlighted in the previous researched – the control robustness and precision. A method for control, based on simulation and experimental steps is presented for the innovative actuator. The performances of the control are assessed.

The electrification of the vehicles induces the problem of their autonomy and their range. The on-board storage system impacts them directly.

### Optimal designs of electric vehicles for a long-range mobility

The problems of the range and the energy storage of the electric vehicle on board are important and are introduced. The energy system of electric vehicles is studied for different ranges and mobility usages. Multi-objective optimization method is applied to estimate the optimal vehicles energy system designs for urban mobility and for long way electric mobility (> 500 km). The optimal designs considering technical, economic and environmental criteria are presented. The trade-off between the autonomy of the electric vehicles and the energy densities of the high voltage batteries are illustrated.

### An energy balance evaluation in Li-Ion battery packs under high temperature operation

The high voltage batteries are one of the main components of the powertrain of electrified vehicles. Li-Ion batteries are the storage system for electricity in electric and hybrid electric vehicles.

The development of the battery system is related with many challenges. Some of them are the fast charging, the charging infrastructure, the fast evolution of technologies, and the management to keep the system in optimal operating conditions. In the thermal management system of the battery, the major challenge is to maintain the battery cells in the optimal range of temperature without ever exceeding limitations defined by cell manufacturers. This challenge grows with the needs of high power charging introduced to decrease of duration of the charge. To manage the thermal behavior of the battery one can use active systems such as cooling plate with liquid, fans or passive systems as the phase changes of the materials. The precise characterization of the energy balance of the high voltage battery pack is important for the design of different kind of thermal solutions. The energy balance of a battery pack is investigated experimentally to construct a thermal model for implementation in simulation tools for design.

### 1.2.2 Extension of the vehicle energy systems, the vehicles as grid related systems

This research domain considers the electrified and the electric vehicles as connected to the electric grid and as a part of it. The vehicles became related to the energy grid and their can provide energy services for mobility but also for the households. New design methods have to consider the new limits of the extended energy system and the recharging systems became part of the optimization. The choice and the compromise between the size of the onboard storage and the frequency of the access of the vehicles to the recharging infrastructure are important to be defined. The recharging process takes time and the need of service for optimal recharging became visible. The vehicles have to be connected to the recharging infrastructure and domestic robotized solutions connected to the vehicles appear to bring recharging service. In a large scale energy services integration the vehicles can provide when not used
for mobility, energy services for the household. Significant reduction of the global CO2 emissions is possible by the consideration of the larger energy system and the integration of the energy services for the expanded energy system.

## Design of electric vehicles grid related energy systems for optimal mobility service

The electrification of the vehicles and their connection to the electricity grid present the opportunity to consider the vehicle energy system as grid related. The autonomous energy system is enlarged and connected to the rest of energy grids. Thus the vehicles have to deliver as well the main energy service, which is the mobility but they can deliver or be part of the rest of the energy services that grid related systems can deliver: the electricity and the heat production. There is a need to consider the extension of the vehicle energy systems and to adapt the design of this enlarged system to deliver optimized energy services for all energy needs of the system.

## Coordination interest for the optimization of electric vehicles long distance trips

This research investigates the way to use the electric vehicles for long way trips, by extending in an intelligent way their intrinsic autonomy.

The drop in battery prices and the rise of their energy density has permitted the introduction of consumer cars with more than 400 km range, while improvements in high power charging point units (with a rate 100 kW and above) offer hundreds of kilometers range gain in 20 minutes. One risk remains: the travel time depends on the availability of charging stations, which can drop during rush hours, due to long queue, or grid power overload. These situations can highly affect user experience. Service for optimal recharging plan is studied.

## Slow domestic charging: Robotized charging of high voltage batteries for electric and hybrid electric vehicles

Users expectation for electric vehicles are the same as for the conventional vehicles: range adapted to specific use cases, usage comfort as good as the state of the art internal combustion engines -powered vehicles (availability, "re-fuelling" time and possibilities, passenger comfort...), and infrastructure also with direct connection to charging facilities. The management of the recharging of the batteries for the users is a central question. The domestic charging of the vehicles through robotized systems, which are innovative products and services, proposes users comfort and service.

## Integration of the environomic energy services for mobility and household using electric vehicle with a range extender of solid oxide fuel cell

The biggest part of the state of the art studies on energy conversion systems for vehicles are concentrated on the on-board energy storage and conversion systems during the phase where the vehicle is used, when this vehicle is driven. The holistic methods might be used to optimize not only the design and the energy systems of the vehicles, but are also enablers to evaluate new energy services of the vehicles propulsion systems, when the vehicles are not used for driving. They are related to the different energy grids- electricity and gas. In this system the vehicles are considered to be related to the energy grids. Their design is as well optimized for the additional energy services and functions in the enlarged energy integrated system.

The environomic optimization method is used in this research to explore the optimal design for vehicles with innovative propulsion system, considering the mobility and also the integrated energy service to the households. The environomic design of vehicles integrated energy systems consists in the systematic and holistic optimization for vehicle energy systems, according to technical, economic and environmental objectives. The method is developed for vehicles applications and is presented for electric vehicles in [2], for hybrid electric vehicles in [3] and for electric vehicles with innovative range extender module with SOFC in [4]. Solid oxyd fuel cells technologies are described in [5] and researched as range extenders for battery electric vehicles in [4] and [6].
### 1.2.3 Energy storage and conversion systems for energy vectors; fuel cell electric vehicles, hydrogen and their ecosystem

#### Fuel cell electric vehicles, energy vectors for hydrogen and their ecosystem

Several solutions have been developed in order to improve the competitiveness of the electric vehicles. Technical work on advanced battery technologies, and economic aids related to environmental bonuses for the customer investment cost decrease are the most important. The range extender concept (serial hybrid) proposes an additional autonomy for the electric vehicles. However, internal combustion engines are known to have low efficiency 30-35% (for gasoline ICE used as a range extender) and still use fossil based fuels. They can also be operated with biofuels, but after the combustion the CO₂ emissions are released in the atmosphere but in this case we do not introduce additional amounts of CO₂ to the atmosphere.

The fuel cells with their high efficiency and electrochemical conversion of the fuel directly to electricity avoid the combustion and seems to be an excellent candidate to substitute the ICE in an optimal conversion chain. The proton-exchange membrane fuel cell (PEMFC) has been studied for most of the automotive applications. The advantages of the PEMFC are that they offer high safety, support well transients and operate on low temperature (80 °C). In 2005, the project FISYPAC built an electric vehicle powered by a 17 kW PEMFC coupled with a Li-Ion battery with capacity of 13 kWh. The vehicle was tested and reached autonomy of 500 km on NEDC with 1 kg/km of hydrogen (H₂) consumption.

The weakness of the PEMFC is that they use hydrogen with high purity stored on the vehicle board. With the limited hydrogen distribution infrastructure, the use of PEMFC is also limited. The hydrogen has to be stored on 700 bars and this introduces some safety constraints. Additionally, it is important to emphasize that PEMFC must be powered by high quality pure hydrogen. Even a small amount of CO (about 10ppm) can deactivate the anode material used in fuel cell zone during the operation.

The main idea of this research axe is to study liquid fuels (for example bio-ethanol) as a source of hydrogen to avoid storage of compressed hydrogen under 700 bars on the vehicle board. An efficient conversion system for the bio-ethanol is researched based on a serial range extender vehicle architecture with fuel cells able to convert liquid fuel into electricity. A solid oxide fuel cell with integrated bio-ethanol reformer is proposed for converter in the range extender concept. The catalyst system for the ethanol steam reforming is investigated on experimental way. The performance of the catalyst system defines the conversion efficiency of the fuel cell system.

The well-to-wheels (WtW) CO₂ emissions balance, including the emissions for the production of the energy and the emissions during the use phase of the vehicle is performed. The well-to-wheels (WtW) CO₂ emissions balance illustrates the importance of the energy vectors for the global carbon dioxide emissions, the efficiency of the vehicles propulsion system.

### 1.3 Personal experiences and motivations

The major part of my research work is for the Research and Innovation department of PSA, where I spent 14 years of professional experience. My research work is performed in the Research and Innovation Direction of PSA, where I am employed since 2006. I started my carrier as young engineer in the "Engines department" of the Research and Innovation Direction. My mission was to research innovation and technologies to improve the combustion process and thus the effective efficiency of the internal combustion engine. I was appointed to that position during 3 years (2006-2009). The core knowledge and skills are related to the thermodynamics, the mechanics and the internal aerodynamics.

From 2010 to 2012 I was employed in the department for vehicles development of PSA and engaged in the development and the industrialization of commercial vehicles. This was for me a large-scale industrial project, where I experienced the management of projects: human resources, budget, planning, engineering and quality management, industrial and time to market constraints. The production plant is located in Russia and I had the opportunity to visit the country and to practice the Russian language. I have managed R&D budget and industrial investments for the production plant.
I integrated the Research and Innovation Department in June 2012 and I was appointed to the research domain of vehicles propulsion systems. I started my Ph.D. thesis at Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland in 2012 at the Laboratory of Industrial Energy Systems and Process Engineering (IPESE). I was doing my PhD work at the same time as my research activities in the PSA Research and Innovation Department. The duration of my Ph.D. thesis was 3 years and 3 months. I was affiliated to the doctoral school of Energy of EPFL where I followed and validated 12 ECTS credits. The topic of the Ph. D. research is entitled “Environomic design of vehicle integrated energy systems”. This topic is at the crossroad between Process and Mechanical Engineering. The research was motivated by the need of the automotive industry to meet the targets of sustainable development and greenhouse emission reduction of the future vehicles fleet.

Today I am a researcher in the Scientific Department of PSA. My research in related to the research and innovation projects in the domain of the energy and the vehicles propulsions systems. My nomination at this position is effective since January 2016. I am involved in the leading of the research activities, PhD thesis leading and partnerships with the academic research institutions (universities or research institutes) or with others industrials partners.

My professional project is to prepare my habilitation to direct research (HDR) and to continue to lead the research activities in the industry, for example in PSA. In the industry, we have considerable research resource – PhD Thesis, participation in research institutes in national and international level. For example, we have 80 PhD students in total and around 30 PhD new thesis are discussed every year for approval, with French Association Nationale de Recherche Technologique (ANRT) and the academic partners. My ambition is to transfer the knowledge and the good practice from the academia for research leading and to apply them from efficient and collaborative research in the industry, based on partnerships with the academia.

My main motivation is to increase and to transfer the knowledge and the interest of the research to the students and the professionals from the industrial sectors.

**Practice and content of the teaching**

In 2019, I was invited as a guest lecturer to teach two disciplines in the Master Programs of the National Military Academy “Rakovski” of Bulgaria. The Academy has a unique contribution to the foundation of the Bulgarian Officers Corps, as well as to the establishment of the new Bulgarian State. Today it is a national institution for higher education, qualification, and scientific research on issues of national security and defense. The mission of Rakovski Defense and Staff Academy is to prepare military leaders and civilian personnel in all spheres.

For the Specialized Master Programs I proposed two new disciplines and they were selected in the chair of Logistics and Transportation. The interest of introduction of my lectures is the teaching of modern structured scientific and engineering methods deployed in the Research and Development sector of the automotive industry. They insure structured and systematic management of the technologies.

**1.4 Outline and contribution of the present work**

The manuscript contains 7 Chapters and 5 Appendixes (A, B, C, D, E).

Chapter 1 introduces the motivations and the context of the research.
Chapter 2 introduces the prior art in the main research domains:

* Energy efficiency, storage and conversion on vehicles boards.

* Extension of the vehicle energy systems. The vehicles as grid related systems to the energy grids for optimal energy services for mobility.

* Energy storage and conversion systems for energy vectors. Fuel cell electric vehicles, hydrogen and their ecosystem.

Chapters 3, 4, 5, present the research results for the main research domains.

Chapter 3 presents the studies of the optimal designs of vehicles energy systems to increase the global energy efficiency onboard. Waste heat recovery and hybridization (electrification) of the vehicles are studied.

An electromagnetic actuator for engine valve train is presented. The purpose of the development of this technology is to increase the conversion efficiency of the internal combustion engine to reduce the fuel consumption and to reduce the CO₂ emissions trough most efficient onboard energy converter – the internal combustion engine.

Chapter 3 studies the optimization of the on board energy storage system constituted from the high voltage batteries. The impact of the quantity of batteries to increase the vehicle range is studied. The recharging of the batteries is related with the heating of the batteries. Thus, the energy balance of the batteries is studied to obtain optimal performances on the battery storage.

Chapter 4 studies the connection of the vehicles to the energy grids. Technologies and strategies for recharging are studied for electric vehicles. A concept of integrated energy services for mobility and the household are researched. An innovative concept of electric vehicle with a highly efficient range extender module is presented to deliver optimal services for mobility and for heating and electricity generation for a household.

Chapter 5 studies the hydrogen as energy for fuel cell vehicles and the hydrogen ecosystem. The conversion of the bio-ethanol to hydrogen is studied. The optimal catalyst is selected to maximize the hydrogen yield obtained by bio-ethanol steam reforming. A Well-to-wheels CO₂ emissions balance of different energy storage and conversion technologies (different propulsions systems for vehicles) is presented. The impact of the energy vectors (fuels, electricity and hydrogen) for the sustainable mobility is estimated.

Chapter 6 draws the conclusions of the research work, estimating the contribution of the energy integration method.

Chapter 7 draws the perspectives of the research domains, related to the global integration of the energy grids for the different energy vector on a large scale. Thus, the energy grids can constitute large-scale energy conversion and storage system for the optimization of global energy services, for mobility, electricity production and cooling/ heating.

Appendix A presents the methodology for the optimization of energy integrated systems. This methodology is transferred and adapted for the study of extended and grids related vehicle energy systems.

Appendix B introduces the models of the technologies (the converters and the storage technologies), the possible powertrains architectures coming out from the combination of the energy technologies. The simulation models of the selected powertrain architectures are described. Appendix B contains also the description of the economic and the environmental models, used in the optimizations.

Appendix C contains the research results of the optimal designs for efficient mobility service for hybrid electric vehicles and the optimal designs of electric vehicles for a long-range mobility.

Appendix D contains basic definitions in the mobile robotics.

Appendix E contains the research results of the study "Integration of the environomic energy services for mobility and household using electric vehicle with a range extender made of solid oxide fuel cell".

The Appendixes C and D include the results from recently published journals articles.
# Chapter 2 Literature review

## 2.1 Energy efficiency, energy storage and conversion on vehicles board

### Waste heat recovery

The vehicle is a dynamic system. The size and the efficiency of the converters depend on the dynamic driving profile. Energy integration is used to increase the energy efficiency of the vehicle energy system. An adapted methodology is proposed in [7] to select the best points for the integrated design of the system. The method clusterizes the dynamic profile on typical multi-periods where the vehicle is used. The design of the energy system is optimized for these main multi-periods. The energy integration is applied in [7] on hybrid electric vehicles. The potential of energy recovery of a single stage Organic Rankine Cycle is studied. The ORC is considered for a thermal engine in a hybrid electric powertrain. The contribution of the ORC is assessed for different drive cycles. After the process integration, a multi-objective optimization defines the design of a hybrid electric vehicle with an optimal waste heat recovery system. The energy balance of the internal combustion engine, for different operating points, is defined and analyzed in [8]. An energy integration methodology, based on process integration techniques is discussed in [8]. It is applied on the extended energy system of the vehicle. The possibilities of an ORC to recover waste heat from a 900 kW fast passenger ferry Diesel engine are investigated in [9]. The authors analyze in [10] the feasibility of an “on-board” innovative and patented ORC recovery system. The vehicle thermal source can be either a typical diesel engine (1400cc) or a small gas turbine set (15–30kW).

Hybrid vehicles can use energy storage systems to disconnect the engine from the driving wheels of the vehicle. This option enables the engine to be run closer to its optimum operating condition. Nevertheless fuel energy is still wasted as heat through the exhaust system. The article [11] presents the model of the engine of a diesel-electric hybrid bus, coupled with a hybrid powertrain. The performances of a hybrid vehicle over a drive-cycle are analyzed. This showed that including the waste heat recovery technology reduces the fuel consumption with 2.4% for a typical drive-cycle. The ORC has large applications such as agricultural biogas combined heat and power engines, biomass boilers, landfill biogas plants, small geothermal plants and concentrated solar thermal systems [12]. Most of the projects related to ORC systems, in the transport industry, discuss performance optimized solutions that include heat recovery in engine exhaust gas. The authors describe in [13] the development and exergetic assessment of a new hybrid vehicle incorporating gas turbine as powering option. A multiple heat source supercritical ORC (Organic Rankine Cycle) for vehicle waste heat recovery is studied in [14]. A vehicle energy-supplying system is proposed to satisfy energy demand of vehicle in every season in [15]. This system is based on an ORC. The authors modeled a hybrid electric heavy duty vehicle to assess energy recovery using a thermoelectric generator in [16].

**The literature describes** waste heat recovery of internal combustion engines or other converters, by external thermodynamic cycles, such as the Rankine cycle, with organic fluid. The organic fluid helps to evaporate to lowest temperature and so helps to valorize the low-grade heat. Few studies mentioned the waste heat recovery on hybrid electric vehicles and its potential for fuel consumption reduction.
Optimal designs for efficient mobility service for hybrid electric vehicles

In the next decade, electric vehicles are expected to increase their market penetration. Main technologies for energy storage and conversion, the drive train components and the energy management will reach high maturity [1]. The industrialisation of those components on high scale and volume will reduce the cost of the electrification. The affordable cost will lead to the democratisation of electric vehicles on all vehicles segments. Hybrid electric vehicles with different levels of hybridization ratio are designed for urban and peri-urban drives. They allow zero emissions drives from thank-to-wheels view (25 km or 50 km range). The policy in the big cities is to support by incentives Hybrid electric vehicles with zero emission vehicles modes. The scarcity of not only fuel resources but also the adverse effects of the operation of energy intensive systems on the environment (pollution, degradation) have to be taken into consideration. Thus, the system can be properly designed and operated. The systematic consideration of thermodynamic, economic and environmental aspects for the design and the operation of an energy system is called environomics [17]. Environomic analysis is an extension of thermo-economics [18]. Environomic design of electric and hybrid electric vehicles are studies in [2], [19].

The automotive vehicles are increasingly restricted by environmental regulations. They have to reduce emissions of CO2 and pollutants of exhaust gases of vehicles. The design choices of the vehicle powertrain become an important question. A simulation method of environmental impacts generated by the phase where the vehicles is used is proposed in [20]. Delogu et al. investigate in [21] the light weighted design and electrified powertrain as important strategies in the automotive industry to reduce fuel consumption and reduce emissions. Lightweighting of electric vehicles is considered a step to reduce forward the electric consumption of the vehicles. Alegre et al. showed in [22] a modelling of electric and parallel-hybrid electric vehicle with choice of type and size of the battery or weight. The tools allows observing the performance impact and the vehicles range. Through the use of a Geographic Information System combined with a mathematic algorithm (genetic algorithms) the planning of charging stations was obtained. The installation investment cost was minimized and the geographic distribution was optimized in order to increase the quality of the service. Electrified vehicles like hybrid electric vehicles, plug-in hybrid electric vehicles, battery electric vehicles, fuel cell electric vehicles and fuel cell hybrid electric vehicles are emerging as less polluting alternatives to internal combustion engines driven vehicles. It is important to assess the market penetration in the future of these categories of vehicles. A 'two-step' method is used in [23] to estimate the optimum market penetration of lightweight and electric-drive vehicles in the long-term and the impact on the light-duty vehicle fleet, focusing on Japan. The authors analyse in [24] different charging strategies for a fleet of electric vehicles. They test the value of a vehicle owner that can choose when and how to charge.

The authors analyse in [25] the scenario of development by the Danish Climate Commission. The results show that even with a limited short-term electric car fleet, these will have a significant effect on the energy system; the energy system's ability to integrated wind power and the demand for condensing power generation capacity in the system.

Alternative scenarios for energy planning are proposed for the transportation sector in [26]. The analysis of the projection of energy demand and greenhouse gas emission, in the form of CO2, NOx, and CH4, was conducted. The results show that by implementing an efficient vehicle scenario, global warming potential can be reduced by 15.80%.

**The literature shows** that emission policy considers hybrid electric vehicles as local emission reduction solution. Simulation models of fleets of HEV are used for energy planning activities. Few studies appear to consider on a holistic way the environomic scenario for the HEV use.
## Development of robust control of an electromagnetic actuator

The literature review highlights the problems and the research done on them. Liu and Chang focus in [27] on the valve seating performance of an electromagnetic valvetrain (EMVT) with moving coil linear actuator. The EMVT has inherent advantages to achieve low valve seating velocity. The seating performance of EMVT is evaluated by two indicators: seating velocity and holding force. Mercorelli studies in [28] a Lyapunov-based adaptive control law for an electromagnetic actuator: a direct adaptive non-linear control framework for a non-linear electromagnetic actuator is presented; the control strategy is an adaptive one in which the main parameter depends on the variable inductance of the actuator itself. Hoffmann and Stefanopoulou present in [29] the valve position tracking in electromechanical camless valvetrains (EMCV). One of the main problems in the EMCV actuator is the noise and wear associated with high contact velocities during the closing and opening of the valve. The contact velocity of the actuator and the valve can be reduced, and thus, noise and wear, by designing a tracking controller that consists of a linear feedback and an iterative learning controller. Peterson et al. describe [30] a nonlinear self-tuning control for soft landing of an electromechanical solenoid valve actuator (reluctance force actuator), which is a kind passive actuator with springs on the moving part. The authors studied low contact velocities or “soft landing” of the actuator on the solenoid faces and between the actuator and the valve is also necessary in order to maintain similar noise and wear levels with conventional camshaft-driven engines. Jang Liu et al present in [27] an electromechanical valve actuator (EMVA) using solenoid to actuate valve movement independently for the application of internal combustion engine. They proposed an EMVA structure by incorporating the hybrid magneto-motive force (MMF) implementation in which the magnetic flux is combined by the coil excitation and permanent magnets. This type is also a passive type of actuator. Making use of the dedicated flux arrangement, the proposed device can be used to fulfill the VVT features with reduced power source requirement and less electric device components. A dual flux channels EMVA is detailed and the design procedures are presented.

**The literature review illustrates** the technical problems related to the design and to the control that electromagnetic actuators for engine valve trains have. For this reason it is needed an innovative design and control of the actuators to solve all these technology problems stated in the prior art.

## Optimal designs of electric vehicles for a long-range mobility

Around 2030 electric vehicles are predicted to increase their market penetration and to bring evolution concerning the main technologies for energy storage and conversion, the powertrain components and the energy management [1]. The industrialization of those components on high scale and volume reduces the cost of the electrification and to its democratization on all vehicles segments. According to the ERTRAC Report [1] it is possible to distinguish two types of electric vehicles: user friendly affordable vehicles and high performance electric vehicles. The first category will develop in small and middle segments (A/B/C) and they represent a more optimized range/cost solution enabling a very big market penetration and then a considerable impact on greenhouse emissions reduction. The ranges of these vehicles are adapted to 200 km of drive and suit for urban and sub-urban use. The major challenge is to achieve significant cost reduction of the battery and the components of the propulsion system. The introduction of these vehicles is related to the support of the governmental policies (transportation energy taxes, city center driving), the infrastructure deployment and the cost of the electricity. The electric vehicles with high performances are deployed in the C/D/E segments. They will maximize the range in the performances offering at least 600 km of autonomy for a single charge of the battery. The cost of these vehicles is high because of the high battery capacity on board. The cell energy density needs to be improved to allow the deployment of these vehicles. The significant battery capacity can be rapidly recharged with high power chargers. These cars are designed for long distances use, on the highways and their deployment is related to the deployment of the infrastructure of high-speed chargers all along the highways.

The customers' expectations and considerations for electrified vehicles are acceptable prices, range, reliability, durability and re-sale value similar to conventional vehicles. Some advantages may also be achievable through synergies of the electrification with the connectivity and the automation of the vehicles. This will provide benefits of the users by easy access to the charging infrastructure, optimization of the traffic and minimization of the energy consumption. Charging is a crucial topic for
the success of the electrification. Purely electric vehicles are pushed to compete with internal combustion engine vehicles one-on-one, where refilling takes place around once every week. With the right mix of infrastructure, electric vehicles could compete with vehicles powered by internal combustion engines, but with the added dimension of the refilling comfort. Daily e-charging would mean that drivers never have to stop at filling stations, except the rapid charging stations for the long way drives. Information and connectivity solutions that inform in real time the drivers for the availability and the distribution of the charging infrastructure, including the possibility to book and pay for charge points in different places is an important part of the solution. Combined with fast, i.e. high power charging capacities, pure electric vehicles would be able to serve long range needs.

Zenginis and al. propose in [31] the waiting time in the queue of the customers for recharging their batteries. The model considers the stochastic recharging of the batteries capacities and the class of the vehicles. Furthermore, a charging strategy is proposed according to which the drivers are motivated to limit their energy demands. Karakitsios and al. introduce in [32] a methodology to analyze the impact of the fast inductive charging technologies to the grid. Levinson and al. explore the potential of public charging infrastructure to spur US battery electric vehicle [33]. They used market analysis and simulations. Zhang and al. develop in [34] a multi-period flow refueling location problem for electric vehicles (EVs) and charging infrastructure. The optimization model determines the optimal location of chargers for every station. Sun and al. explore in [35] the choice of the users to fast charging of the battery electric vehicles as a function of the available charging stations and the distance between them, in Japan. Wu and Sioshansi develop in [36] a model to optimize the location of public fast charging stations for electric vehicles (EVs). They use a stochastic flow-capturing location model to solve the uncertainty in where EV charging demands appear. Schroeder and Traber provide in [37] the business case of fast charging technology in a case study for Germany. They estimated that the Return on Investment (ROI) of a public fast charging station constitutes the main contribution. The general EV adoption rate is estimated to be the main risk factor for investment in public charging infrastructure.

**The literature review shows** that the success of the deployment of the electric vehicles is related to their access the recharging infrastructure. A global approach considering the vehicle as energy system related to the energy grid lacks.

## An energy balance evaluation in LI-ION battery packs under high temperature operation

Several researches have been done in the field of energy management in batteries. Energy and exergy analyses are conducted in [38] to investigate a new cooling system of hybrid electric vehicles (HEVs). A thermal energy storage system is integrated with a refrigeration cycle where octadecane is selected as the phase change material (PCM). The exergy destruction rate and the exergy of each component in a hybrid thermal management system are calculated. The findings of the exergy analysis reveal that the overall exergy of the system with PCM presence is 31%, having the largest rate of exergy destruction of 0.4 kW. In addition, the results of the parametric study show that an increase in PCM mass fraction results in an increase in exergy of the system.

A lithium-ion battery thermal management (BTM) system using the electric vehicle (EV) air conditioning refrigerant to cool the battery pack directly is investigated in [39]. In the system, basic finned-tube heat exchanger structure and a special aluminum frame are adopted to design the battery pack thermal management module with lithium-ion batteries of cylindrical shape. Experimental results show that the BTM system can control the battery pack's temperature in an appropriate preset value easily under extreme ambient temperature, as high as 40 °C. The temperature difference within the pack is less than 4 °C in the constant discharge rate of 0.5 C, 1 C, 1.5 C laboratory tests, and 1.5 °C in road drive tests.

Lithium-ion batteries are widely used in electric and hybrid electric vehicles due to their wide nominal range and high power densities. However, operating temperature, which has a pronounced effect on battery thermal and electrical performance, needs to be maintained within a specified range. In [41], a Li-ion battery pack has been tested under constant current discharge rates (e.g. 1C, 2C, 3C, 4C) and for a real drive cycle with liquid cooling. The experiments are performed using cold plates at 10°C, 20°C, 30°C, and 40°C coolant temperatures to obtain thermal and electrical parameters. For this, three 20Ah LiFePO₄ prismatic cells are connected in series and a battery thermal management system is designed
and developed for liquid cooling. To measure the temperature variation, 18 thermocouples are installed on the principal surface of all three cells. The results show that the battery pack temperature can be maintained within the required range at all four selected discharge rates with the coolant at 30°C. The discharge capacity of the battery pack increases with increasing coolant temperature and is found to achieve a maximum of 19.11 Ah at a 1C discharge rate with the coolant at 40°C.

Energy and exergy analysis are as well performed for PEM fuel cell [40]. This study presents the performance of a proton-exchange membrane (PEM) fuel cell in terms of its pressure and voltage parameters. The aim of this study is to improve the performance, efficiency and development of modeling and simulations of PEM fuel cells by experimental optimization. The energy and exergy efficiencies of the PEM fuel cell were found to be 47.6% and 50.4%, herein. Energy and exergy analysis can be performed for (PEM) fuel cell [41] or for the propulsion system for hybrid electric vehicles [7]. The literature review [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52] and [53] show that the energy management of the batteries is the target of experimental and simulation works. Energy and exergy analysis are useful to evaluate the performances of the energy management systems for energy storage systems (batteries) or convertors (fuel cells) or complete propulsion system of vehicles. The energy and exergy balances are an excellent way to detect the energy waste in energy integrated systems.

**The literature review describes** the passive and active ways of cooling and the methods of design from the experiments to the design of the solution. Energy and exergy analysis are useful to evaluate the performances of the battery packs and need to be investigated.

## 2.2 Extension of the vehicle energy systems: the vehicles as grid related systems to the energy grids for optimal energy services for mobility

### Design of electric vehicles grid related energy systems for optimal mobility service

The challenge is to integrate massively the electric vehicles in the energy grids. Several scenarios are researched through simulation and optimization to build viable energy planning for that. The authors present in [54] an optimization model for the planning of the infrastructure of chargers for electric vehicles. The model minimizes the overall (installation, maintenance, operation) placement costs of charging stations. The study illustrates results for the optimal charging stations placement layout and overall costs placement for different driving ranges and the required quality of service level by including a power system reliability check. The results are useful for the charging infrastructure investors and electric power system operators. The authors made in [55] the analysis of economic implications of innovative business models in networked environments for electro-mobility. Mainly the high investment cost for the EV and for the infrastructure are difficult points in the economic model. The research question that is investigated in [56] is where to install the charging stations to facilitate the long-distance travels and to meet the urban (local) demands considering both the existing stations and the installations are to be realized by legal regulations. The study in [57] introduces a multistage chance-constrained stochastic model for strategic planning of battery electric vehicle (BEV) inter-city fast charging infrastructure. A mixed integer programming model is developed to determine where and when charging stations are opened, and how many chargers are required for each station to meet the growing BEV inter-city demand. The optimization problem is solved by genetic algorithm. This study showed that investment in inter-city charging infrastructure is vital to alleviate the range anxiety. Other conclusion is that planning decisions depend on many factors, such as the design level of service and the vehicle range. The work in [58] established a mathematical model to optimize the layout of charging infrastructure based on the real-world driving data of 196 battery electric vehicles. The work has studied the connection of the number increase of charging points and retention rate. When the number of charging points increased from 60 to 220, the retention rate was 97% for slow charging and 95% for fast charging.
The authors developed a stochastic model in [59] that includes driving and charging behavior of BEV owners in Japan. The model is based on Monte Carlo methods and was implemented in MATLAB. They conducted simulations with this model to find out whether the existing infrastructure is adequate for the charging of a large number of BEVs.

**The literature review shows** that different simulation and optimization methods are used to optimize the energy planning and the resources and EV charging infrastructure distribution. The infrastructure for recharging of electric vehicles is characterized with high investment cost and high impacts on the electric grid. For this reason, an optimal techno-economic optimization for the planning of usage of the energy systems is needed.

**Coordination interest for the optimization of electric vehicles long distance trips**

The authors in [60] offer a model that guarantees the confidentiality of users' data, in particular their vehicle condition and destination. It is based on reservations, transmitted by mobile networks, which allow charging stations to estimate their waiting times and vehicles to choose stations based on these data. The authors in [61] offer a solution that only requires local communications, including a cooperative exchange between vehicles to determine who should stop at the next charging station. They are based in particular on the work of [62], which shows that the optimal solution to this relaxed problem is achieved when the occupancy rate is constant, this means when the vehicles are equally distributed at the different charging points. The authors of [63], on the other hand, use game theory tools to allow users to optimize their travel costs and recharging stations to maximize their earnings, assuming that hourly prices are freely set: this results in a non-collaborative game. Overloading of the electricity grid is also considered. A cooperative approach to game theory is developed in [64], which makes it possible to gain in convergence speed and robustness. However, these papers only compare the performance of their solutions with theoretical solutions. This approach does not allow the evaluation of the remaining potential gain.

**We propose** a method to find an optimal recharging solution based on a differential evolution algorithm, as well as a communication schema, easy to deploy, that would permit to coordinate vehicles and reduce waiting time at charging stations, during long distance trips. The novelty of this research is the introduction of a communication system between the vehicles and the infrastructure to coordinate and to optimize the charging planning of the electric vehicles.

**Slow charging: Robotized charging of high voltage batteries for electric and hybrid electric vehicles**

Battery electric vehicles (BEV) suffer from several disadvantages: the charging time of the battery, which is still much longer than refueling a liquid fuel tank and the lower autonomy, which creates range anxiety for the user. Accordingly, recent European market forecasts predict two possible scenarios: A sharp rise of EV sales that reach 60% of the market in 2030 due to a major technological breakthrough for EVs or a smoother increase in EV sales reaching 20% [64]. To support this mobility transformation and meet the growing stock of [1], the installation of associated equipment such as charging infrastructure for electric vehicles is necessary. In priorities, a deployment of charging stations to cover territories and thus compensate the lower autonomy of BEV [65] and a diversification of the technologies of charge. Today, the only technology available for EV users is the conductive charging by plug: domestic devices allow users to charge on the grid at home [66] and fast DC charging are available in most of the classical gas stations [67]. However, stakeholders are more and more interesting in wireless charging system (without plugs and cables) because it offers reliable, convenient and safe charging (No electric shock) for EV drivers [68]. The Wireless Electric Vehicles Charger (WEVC) is thus promising for EV charging and an alternative to the classic plug-in charger technology. The static WEVCS technology is used to charge the high voltage battery of a stationary EV. Generally, the transmitter pad is fixed on the ground of a parking place and the pick-up coil (induction plate) is on the underneath of the car [69] (Figure 2-1). Therefore, the power transfer has to be operational and efficient for a distance between 100 to 250 mm accordingly to the variety of ride height of passenger cars available on the market [70]. WEVC begins to enter the market of "charging technology for EV": Many actors (Companies or Research Groups) are developing stationary WEVCs for passenger cars. According to the literature, the current power range of existing prototypes is between 1 to 20 kW,
accepting an air gap of 100-300 mm with an efficiency between 71% and 93 % [69]. Furthermore, several charging pads just start to be sales, the performance of two technologies on sale are details in Table 2-1.

<table><caption>Table 2-1: Properties of stationary WEVCS on the market.</caption><thead><tr><th>Corporation / R&D Institute</th><th>Vehicle type</th><th>Maturity</th><th>Cost (kUSD )</th><th>Air Gap distance (mm)</th><th>Operating Frequency (kHz)</th><th>Power Range (kW)</th><th>Efficiency (%)</th></tr></thead><tbody><tr><td>Plugless Power (Evatran Group) [71], [72], [73]</td><td>Passenger car (Tesla Model S, BMW i3)</td><td>On sale</td><td>6-13</td><td>100</td><td>20</td><td>3.3 - 7.2 (240 V / 50 A)</td><td>84-90</td></tr><tr><td>WiTricy Corporation [74]</td><td>Passenger car / SUVs</td><td>On sale</td><td>N/A</td><td>100 - 250</td><td>85</td><td>3.6 - 11</td><td>90-93</td></tr></tbody></table>

<figure><img src="image_5.png" /><figcaption>Figure 2-1: Static Wireless Electric Vehicles Charging System.</figcaption></figure>

However, WEVCS suffer from electromagnetic emissions, the lack of equipment on current EV enabling the wireless power transfer, and the lower energy efficiency compare to the plug-in technology [69]. Indeed, this technology shows the limits: the efficiency of power transfer decreases drastically while the air gap or the misalignment between the two coils increase, due to leakage inductance [70]. Therefore, high power transfer is needed to charge an EV that implies great energy losses and important harmful electromagnetic emissions or at least incompatibility with implant medical devices [69]. The system requires a perfect alignment between the two coils in order to maximize the power transfer and avoid leakage fluxes [70]. Thus, this type of system is very restrictive for the driver: the average driver must do 3 to 4 maneuvers [74] to park correctly and even though the alignment is not always optimal. In existing applications, a display with an oversized primary plate helps the driver to position himself correctly [69] [75] [76] but that entails a higher cost.

Thus, this research proposes to redesign completely the classic WEVC system in order to solve this problem by robotic charging system [77]. A mobile robot equipped with the transmitter part of the WEVC will position itself with accuracy under the receiver coil to maximize the power transfer and to reduce the leakage of electromagnetic emissions. The mobile robot must respect several positioning specifications (an accuracy of ±2 cm) while requiring inexpensive equipment and resilient algorithms.

## Integration of the enivironomic energy services for mobility and household using electric vehicle with a range extender of solid oxide fuel cell

Vehicle-to-grid scenarios have been proposed in [78]. Prototypes installations prouved that the technological means to deliver many of the vehicle-to-grid services are available. Charging electric vehicles can, however, be added to planned demand side management schemes without the need for additional capital investment. Balancing power plants operated with gas serve as stabilizers of the grid in the case of high renewable and variable electricity penetration in power systems. The smart-grid including electric vehicles is playing a stabilization role for the grid [79]. The results show that even a small share of the fleet of electric vehicles providing load balancing to the grid could lead to important reductions in gas use and energy excess. The integration of renewable energy in the electricity sector
and as well the adoption of electric vehicles in the transportation sector have the potential to significantly reduce greenhouse gas emissions with Vehicle-to-Grid technology. The study [80] aims to evaluate the greenhouse gas emission reduction in case of intermittency coming from the introduction of wind power through. The researchers propose in [81] an optimal vehicle to grid planning and scheduling. According to [82], the strategy to reduce CO₂ emissions in the transportation sector is its renewable based electrification. However, the technical option vehicle-to-grid (V2G) requires the vehicle users to temporarily abstain from the usage of their batteries for V2G. 'Range anxiety' and the 'minimum range' are important for the acceptance of the users to participate in V2G.

**The literature review** illustrates separated optimizations of the vehicles use. The electric vehicles are considered as stabilizers for the fluctuations of the electric grid, generated by the electricity production by the intermitant energy sources (wind and solar energy).

## 2.3 Fuel cell electric vehicles, energy vectors for hydrogen and their ecosystem

### Hydrogen from ethanol steam reforming, catalysts selection

Catalytic ethanol steam reforming (ESR) is conducted at middle temperature or high-temperature ranges. The challenge is to develop catalyst working in the range of 723-873 K. Few reports describe catalysts showing high activity, and durability at low temperatures, below 573 K.

Ethylene methane (or CHx species), and CO are regarded as coke precursors. Suppressing ethylene formation and methane formation and promoting water gas shift reaction (CO removal) helps to suppress coke deposition.

Four strategies for the suppression of coke deposition are reported in the literature [83].

* Use of basic support or promotor: Ethylene is usually formed by ethanol dehydration over an acidic site. Use of basic support results in decrease acidity and ethylene selectivity and so coke is suppressed.

* Use of redox active support or promotor. Redox active metal oxides such as CeO₂ with high oxygen mobility and oxygen storage capacity can contribute directly to oxidation of coke and/or adsorbed coke.

* Precise control of particle size and oxidation state of active metal. Highly dispersed small metal particles are suitable for the active site with coke resistance, because coke deposition mainly occurs over terrace site with high coordination number and the fraction of metal atom at terrace site decreased with decreasing metal particle size.

* Addition of second metal. Addition of Au, Cu or Co to Ni-based catalyst positively affects its stability and coke suppression, where Fe promotes steam gasification of deposed coke or adsorbed CHx species.

### Ni-based catalysts [84]

Various preparation methods, Ni precursors and supports have been actively investigated to obtain Ni catalysts with highly dispersed small Ni particles because the Ni-particle size is an important factor for controlling activity, selectivity and carbon deposition. For Ni catalyst, various supports have been investigated: Al₂O₃, M-Al₂O₃, Sn-La₂O₃, CeO₂ and etc.

As active metals, bi-metallic or tri-metallic catalysts such as Ni-Cu, Ni-Sn, Ni-Pt, Ni-R, Ni-Rh, Ni-Au and Ni-Cu-Fe have been used at 573-973 K with 1.5-9.0 of S/C ratio (steam/carbon molar ratio).

Among Ni-based catalysts, Al₂O₃ support has been applied widely to ESR because of its high surface area and stability. Al₂O₃ support has an active site and promotes formation of ethylene, a coke precursor of coke deposition. Modification of Al₂O₃ support by basic oxides (La₂O₃ and ZrO₂) neutralizes the acidic sites effectively and suppresses coke formation, although addition of excess amount of modifier decreases its specific surface area and activity.
Using CeO₂ – based support for Ni- catalyst is reportedly one means of improving ESR. The reason is that it can prevent coke deposition by virtue of its oxygen storage capacity (OSC) and oxygen mobility at its surface. It is reported that addition of Au to Ni/ CeO₂ suppressed coke deposition, although is caused a slight decrease in OSC ability and ESR activity. Doping basic MgO or CaO or CeO₂ is reported effective for carbon deposition suppression. Doping Zr, La, or Sm to CeO₂ improved OSC ability and thereby increase activity and durability. Pizzolitto et al. described that 8wt%Ni5wt%La-CeO₂ shows ethanol conversion (>
90%) and hydrogen purity (>
70%) even after 60h of reaction at 823 K. Reportedly, La- containing and Ni- containing perovskite oxide catalysts showed high activity and stability for ESR. In general, partial substitution of A and/or B site cations in ABO₃ structure can control the redox properties of perovskite oxide. Partially substituted La- Ni- based perovskite oxides can produce highly dispersed metallic Ni sites and can prevent sintering of the metal active site. Therefore, the ESR can proceed with less coke formation.

## Hydrogen ecosystem and hydrogen economy

The automotive industry is challenged by very strict emissions regulation. The CO₂ emissions levels tend to be drastically reduced to 50 g/CO₂ km in 2030. The electrification of the vehicles is currently in progress in Europe, driven by the European CO₂ emissions regulation and hydrogen is seen in the energy outlooks to be used as well for vehicular propulsion, and enabler to reach the CO₂ emissions targets. The current CO₂ emissions regulation considers the emissions from tank-to-wheel perspective and thus the electric and the fuel cell vehicles are accounted as zero emissions vehicles. The tank-to-wheel perspective means that only the emissions coming during the usage of the vehicles are considered. The emissions related to the production of the different fuels is not accounted. Introducing electricity and hydrogen as energy vectors for mobility depends on the global efficiency of the conversion chain, of the total energy resources to be used and the total CO₂ emissions from well-to-wheels perspective.

Renewable energies increase their part in the energy mixes due to the energy transition needs. The sustainable energy system should become clean but as well reliable and affordable. The renewable energy production and energy demand are intermittent thus an integrated system approach is required considering energy conversion and storage. The authors propose in [85] a concept for a neighborhood where locally produced renewable energy is converted and stored in the form of heat and hydrogen, accompanied by rainwater collection, storage, purification and use. Hydrogen can be obtained from different sources of raw materials including water. Among many hydrogen production methods, high purity of hydrogen can be obtained by water electrolysis. In terms of sustainability and environmental impact, water electrolysis was considered as most promising techniques for high pure efficient hydrogen production from renewable energy sources. It emits only oxygen as byproduct without any carbon emissions [86]. Figure 2-2 shows the hydrogen production methods.

<figure><img src="image_6.png" /><figcaption>Figure 2-2: Hydrogen production methods</figcaption></figure>
Biomass energy contributes a major portion in global renewable energy. Its share ever-grows in electricity production worldwide. H₂ is considered as a clean energy fuel having with high energy density (122 kJ/g). This is three times higher than hydrocarbon fuels. Combustion of H₂ fuels produces water and it does not contribute to greenhouse emissions. The authors present in [87] promising approaches applied to achieve biomass based hydrogen production by using microbes. This method is called bio-hydrogen fermentation using crops straws, agri-waste; industrial waste or naturally occurring organic rich biomasses (like molasses). The hydrogen is considered as carbon free fuel. Supply of low-carbon energy for heat, balancing of electricity at national grid and application in transportation sector preferred hydrogen over other carbon based gaseous biofuels. The fertilizers and the petroleum companies are considered to be at the largest users of H₂ with respectively 50% and 37%. With the demand of the fuel cells the demand of hydrogen is increasing with 6 % the last five years.

There are some applications of H₂:

* H₂ is utilized for hydration of substantial oils for fuel production, foods hydration, and alkali hydration for fertilizers production.

* As H₂ is the perfect electron donor, it is used for diminishing nitrate, perchlorate, selenite, and a suite of other oxidized water contaminations.

Currently, the industrial application of hydrogen is equivalent to only 3% of the total energy consumption, and it is expected to grow significantly in the years to come [87].

The current hydrogen market is characterized with low demand. In this case, small-scale water electrolysis based hydrogen production is more cost-efficient than large-scale production. This is due to the potential to reach a high number of operating hours at rated capacity and high overall system utilization rate. The study in [88] shows that there is a relatively good business case for local water electrolysis and supply of hydrogen to captive fleets of trucks in Norway. The model is efficient if the size of the fleet is sufficiently large to justify the installation of a relatively large water electrolyze system.

Through the case study done for Spain, the authors prospectively assesses in [89] the techno-economic and environmental performance of a national hydrogen production mix by following a methodological framework based on energy systems modelling enriched with endogenous carbon footprint indicators [89]. The steam reforming of natural gas still arises as the key hydrogen production technology in the short term. The water electrolysis is the main technology in the medium and long term. In scenarios with very restrictive carbon footprint limits, the biomass gasification also appears as a key hydrogen production technology in the long term.

Metal hydrides are known as a potential efficient, low-risk option for high-density hydrogen storage [90]. In addition, based on the thermodynamic properties of metal hydrides, they give the opportunity to develop a new hydrogen compression technology. They allow the direct conversion from thermal energy into the compression of hydrogen gas without the need of any moving parts.

The amount of hydrogen released after high-pressure hydrogen loading in carbonate was evaluated in [91]. Calcium carbonate (CaCO₃) and Mg-substituted CaCO₃ were selected as adsorbents. Their crystal structures, specific surface areas (SBET), hydrogen content at high pressure, and the amount of hydrogen released in water were investigated.

Imbalance costs caused by forecasting errors are considerable for wind farms connected to the electricity grid. In order to reduce such costs, two onsite storage technologies, hydrogen production and lithium batteries are considered. Probability density distributions of wind forecasting errors and power level are first considered to quantify the imbalance and excess wind power. Then, robust optimal sizing of the onsite storage is performed under uncertainty to maximize wind-farm profit [92].

Among several candidates of hydrogen storage, liquid hydrogen, methylcyclohexane (MCH), and ammonia are considered as potential energy vectors with high hydrogen content, in terms of their characteristics, application feasibility, and economic performance [93]. In addition, as a main motor in the hydrogen introduction, Japan has focused and summarized the storage methods for hydrogen into these three methods. Liquid hydrogen needs a huge energy consumption during liquefaction and faces the boil-off phenomenon during storage. MCH has main obstacles in largely required energy in
dehydrogenation. Lastly, ammonia encounters high energy demand in both synthesis and decomposition (if required). In addition, from cost calculation, ammonia with direct utilization (without decomposition) is considered to have the highest feasibility for being massively adopted, as it shows the lowest cost (20–22 JPY/Nm3-H₂ in 2050).

The hydrogen industry currently faces the dilemma that they must meet certain measurement requirements (set by European legislation) but cannot do so due to a lack of available methods and standards. The authors outline in [94] the four biggest measurement challenges that are faced by the hydrogen industry including flow metering, quality assurance, quality control and sampling.

A detailed breakdown of the energy conversion chains from intermittent electricity to a vehicle, considering battery electric vehicles (BEVs) and fuel cell electric vehicles (FCEVs) is presented in [95]. The traditional well-to-wheel analysis is adapted to a grid to mobility approach.

**The recent literature review gives an overview** of the technologies for the hydrogen production, hydrogen storage, hydrogen transport and distribution and hydrogen conversion on vehicles or energy grid conversion. The main idea is to cover the whole “life cycle” of the hydrogen as energy vector.

From the literature review it comes out that, the hydrogen is seen as the energy carrier and the connector for different energy grids: electricity, natural gas and as a storage medium for renewable energies. The leading spots in term of hydrogen research are in North of Europe and in Japan. Recent studies from Nederland and Norway consider the hydrogen with production from renewable energy and highlight the economic benefit of stand-alone installations with small production capacity. The literature review summarizes the ways of hydrogen production and classifies them. The main need is to produce clean hydrogen. The current production of hydrogen is coming from the natural gas steam reforming. The water electrolysis and the biomass are seen as future ways (technologies) for the production of clean hydrogen. Other question concerning the hydrogen production is the sizing of the capacity of the installations. This question is directly related to the storage of the hydrogen. The hydrogen is very reactive molecule and its storage is a complex process. The literature review describes the compressions levels for the compressed gas hydrogen. The compression process presents important operating costs. The classification of the hydrogen storage ways is proposed. According to the classification, hydrogen can be stored in physical, adsorption or chemical forms. The physical forms of storage are the compressed gas and the liquefied hydrogen. They deliver pure hydrogen but present high operating costs related to the high-energy consumption to maintain them in their respective physical state. The chemical storage concerns the metal hydrides and the chemical hydrides. Metal hydrides present different opportunities for storage of hydrogen in a solid state. They need to be investigated to improve efficiency, storage capacity, to reduce cost and energy demand. The adsorption of hydrogen is done in porous materials: MOFs or carbon based materials (calcite). This way of storage is as well under scientific investigation. Japan has the leading position with the hydrogen storage in calcite. The chemical hydrides (for example ammonia) are the most promising way of hydrogen storage and transportation in liquid form, in the future. Ammonia is the best chemical hydride candidate, presenting the highest hydrogen content. A research direction related to the ammonia is how to convert it directly in adapted converters (fuel cells) without dehydrogenation process, which is costly, and need purification. Japan is as well leading in the ammonia research area. The literature review gives as well summary of the thermodynamic properties for all categories of hydrogen storage.

The research points out lack of regulation and difficulties by the distribution of the hydrogen at the tank stations to the customers. Main challenges are the measures of the quantities, the control of the quality and the sampling of the hydrogen for purity control. These difficulties are related to the lack of measure instruments for large utilization, calibrated to work at 700-900 bar pressure and the purity control capacity available locally.

Hydrogen is clearly seen as an energy carrier for extended energy grids, as stocker for renewable electricity and depending of the installation size can be competitive to Li-Ion stationary battery used for local storage in the electricity grid. Hydrogen production from renewables and Li-Ion stationary batteries demonstrate benefit to balance the global electricity grid and to reduce losses related to intermittence of the renewables.
## Energy planning for sustainable mobility for 2030: Energy storage and conversion technology options for the automotive sector

Electric vehicles will not totally replace the conventional vehicles using internal combustion engines. The service of mobility will be adapted and different. The deployment of electric vehicles depends as well of the support of the governments to the customers' investments and the investment of the recharging infrastructure. The following literature review explains these points.

The authors analyze in [96] the factors supporting the transition to new forms of mobility, namely Electric Vehicles (EV). They analyzed EV, by dividing them into individual Battery Electric Vehicles (BEV), which are 100% electric, and Plug-in Hybrid Electric Vehicles (PHEV). Main conclusion is that policies should be tailored to each individual technology. Charging stations are drivers of electric mobility. As highlighted in [97], driven by the booming of electric vehicle (EV) market, the cost of lithium ion battery observes a remarkable decline. This fact could significantly improve the capability of EVs in coordinating with the power generation from distributed renewable energy. The smart charging is a cost-efficient electric vehicle – renewables energy coordination strategy. Vehicles to grid (V2G) could be more economically attractive in the long run due to its capacity to fully realize the potential of on-board EV batteries. Governments have implemented monetary and non-monetary policies to accelerate electric vehicle market diffusion [98]. The energy prices and financial incentives to influence EV adoption act positively. The authors identified in [99], 7 key elements which are significant risks in the supply for the electric vehicle industry: battery grade natural graphite, lithium and cobalt for electric vehicle batteries, and the rare earth elements dysprosium, terbium, praseodymium and neodymium for electric vehicle motors. The transition to electric mobility is accelerating, and, thus it is important to be able to anticipate and adapt future development of the electric vehicle-charging infrastructure [100]. Overall, the planning of electric vehicle charging infrastructure will support a successful transition to electric mobility. Electric vehicles can play a major role in the transition towards low-carbon energy systems [101]. Different energy scenarios are simulated with the help of planning software assuming a progressive increase in renewable energy sources capacity and electric vehicles penetration. Results show that, for the case study done for Germany, the additional electricity required leads to a reduction in CO₂ emissions only if renewable capacity for electricity production increases significantly. The Italian energy system benefits from the electrification of the transport even at low renewable capacity for electricity production. With assuming a complete electrification of private transportation where electric vehicles charging strategy is regulated to enhance renewable integration, CO₂ emissions can be reduced by 22% and 39% for Italy and Germany respectively.

**The literature review resumes** the main points of the development of the electrified vehicles PHEV, BEV or Fuel Cells vehicles. The problem is complex, with a high investment of time, financial and human resources. In this context, it is needed to plan the different stages and to give a clear strategic vision.
# Chapter 3 Energy efficiency, storage and conversion on vehicles board

This research domain contains three main parts: energy efficiency, energy conversion and energy storage on the board of the vehicles.

## 3.1 Energy efficiency

In the research and in the industry the main points for fuel (energy) consumption improvement are concentrated on the:

* Design parameters:

* Improvement of the peak efficiency of the powertrain components (downsizing)

* Improvement of the part-load efficiency of powertrain components

* Possibility of energy recuperation (thermal, kinetic, pneumatic)

* Optimization of the structure and the parameters of the propulsion system for the same vehicle

* Energy management parameters:

* Energy efficient and control strategies offered from chosen propulsion system

In this chapter are considered different technologies for energy recovery (kinetic and thermal) and their combination in different powertrain architectures. The organic Rankine cycle is evaluated as waste heat recovery technology. The electrification of the ICE based powertrains brings different hybrid electric architectures, classified functionally according to their degree of hybridization and the size of the high voltage battery. The HEVs recover the kinetic energy during braking but are powered by ICE, which present still thermal losses. The research questions to investigate in this chapter are the efficiency improvement by the combination of two type energy recovery systems (HEV and ORC).

The optimal design of the hybrid electric vehicles for the kinetic energy recovery and the choice of the optimal size of the high voltage battery for HEV are as well investigated according to techno-economic and environmental criteria. This is a published article and is presented in Appendix A.

### 3.1.1 Performance and economic analysis of an organic Rankine Cycle for hybrid electric vehicles

Internal combustion engines have a main limitation on efficiency due mainly to the heat losses. Processing an energy integration, it has been shown than an Organic Rankine Cycle (ORC) can improve the overall powertrain efficiency by recovering the heat into electricity. In parallel, hybrid electric vehicles attractiveness increases as it enables important energy saving. The contribution of this research is to design highly efficient vehicles, using both Organic Rankine Cycle and hybrid electric technologies. An adapted methodology based on energy integration techniques is required to choose the best points for the integrated system design. This study applies a methodology on hybrid electric vehicles, so as to define the powertrain configuration of the vehicle that is energy integrated.
The energy recovery potential of a single stage Organic Rankine Cycle for a thermal engine in combination with a mild hybrid electric powertrain is studied. The assessment is done for different drive cycles. A study of economic feasibility and fuel consumption improvement is also done, in order to characterize the integrated energy system.

### 3.1.1.1 Waste heat recovery and hybrid electric vehicles

The target of this research is to illustrate the results from the multi-constrain optimization, which take into consideration performance and economic constrains, in order to achieve an efficient design of an ORC loop for recovery of the waste heat on a hybrid electric vehicle. The presented design is adapted and evaluated to operate on hybrid electric vehicles architecture. Once we perform the optimization, we should study if there is one design solution which is better than the other, or if there exists different solution which could correspond to different users' profiles. Finally, for the best design we should perform both a study on driving cycles, and an economic study, to determine if the Organic Rankine Cycle benefit is important enough to be implemented on those existing vehicles.

The contribution of this research is to apply energy integration methodology on mild hybrid electric vehicles for optimal powertrain design of hybrid electric vehicles. The research studies the impact of an energy recovery technology, such as the ORC on the mobility performances of HEV energy system. After integration of the energy services, a sensitivity analysis on the fuel benefits is done for different real drives representative driving cycles. The results are obtained by simulation, using vehicles models containing experimental engines and vehicles data. The ORC performances are obtained by simulation.

The energy integration and the multi-objective optimization methodology are presented in Appendix A. The models of the vehicle and the ORC are presented in Appendix B.

#### 3.1.1.1.1 Focus on the operating points

According to [7], [8], the best ORC we can implement on a car should use two streams from the vehicle for the evaporator: the water cooling system, and the exhaust gases. Moreover, the cycle has a much better efficiency if it reaches a superheating temperature.

Usually, hybridization methods are efficient at low power of the engine. These zones allow to use an additional electric motor or to recover energy from braking. However, the ORC is more efficient at full load operations, when the total quantity of heat to be recovered is higher. It seems interesting to add such a system to a hybrid vehicle, as the two ranges are different: an electric device work mainly during urban cycles, whereas an ORC works on national roads or highway. We want to emphasize this complementariness, we try to optimize the cycle for high-loaded operating points.

#### 3.1.1.1.2 Multi objective optimization settings

We want to perform an economic, environmental and technical performance optimization. To do that, we should decide what indicator to use.

* Economic: the ORC investment cost is considered as an indicator, defined by equation (A-11).

* Performance: The net output power, used as an objective for performance. The net power is the difference of the turbine power and the pump power for a given operating time in equation (3-1):

$$P_{\text{net}} = P_{\text{turbine}} - P_{\text{pump}}, \text{kW} \qquad (3-1)$$

The exergetic efficiency is verified for every solution.
We need to choose the variable of the ORC design. The system has two degrees of freedom. We choose to define two temperatures to launch the energy integration model:

* The evaporation temperature of the fluid. The interval of variation is defined between 330 °K and 360 °K in order to obtain correct exergetic efficiency.

* A difference of temperature, ΔTmin [5], between the exhaust gas temperature and the superheat temperature of the cycle. The interval of variation is defined between 100 °K and 400 °K. This range is for second order for the results.

We use a simulation of a population with 200 individuals with a maximum number of evaluation of 10. The computing time is enough, and we get enough design to have a precise Pareto curve of the net output power depending on the investment cost.

### 3.1.1.1.3 Multi objective optimization results

#### Decision variables

We need to choose the variables for the ORC design:

* The evaporation temperature of the fluid $T_{evap}$. The interval of variation is defined between 330 °K and 360 °K. The criteria are to obtain good values of the exergetic efficiency.

* The superheat temperature $T_{superheating}$ is defined between 700 °K and 1050 °K.

The function to be optimized for the ORC design is defined as:

$$ \min(-\dot{P}_{net}(x), Cost_{ORC}(x)), \text{ with } x \in X(\text{decision\_Variables}) \quad (3-2) $$

#### Pareto-curves

The multi-objective optimization converges on the solutions presented on the Pareto curve (Figure 3-1). The trade-off between the ORC net power and the investment cost are illustrated to the Pareto curves. The best value of exergetic efficiency is reached at 25%. This is for investment cost interval between 350 and 700 Euros.

#### Method to get the ORC maps

As we want to get the ORC characteristics for each operating point, we should consider the new hot streams and cold streams, as defined in [7] and [8]. Firstly, we do the following assumption: the compressor power is the same for each operating point; we consider the flow rate to be the same. The heat cascades are defined in the energy integration method. By knowing the temperatures and the exchanged power, the energy integration module computes the power maps, the energy efficiency and the exergetic efficiency maps of the gasoline engine (Figure 3-2). The ORC map comes as a solution of the energy integration method.

<figure><img src="image_7.png" /><figcaption>Figure 3-1: ORC Pareto curves: techno-economic trade-off, exergy efficiency in [-], in the colorbar</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>Figure 3-2: Map of the ORC power output, design after multi-objective optimization, ORC net power in [kW], in the colorbar</figcaption></figure>
The maximum net power output is reached over the engine iso-line of 30 kW. This is the area of high rotation speeds and high loads. The optimal design of the ORC design is investigated for the loaded points. The high seeds and high loads zone is also the region with the most important heat losses. The selected design is extended on the engine operating field. One considers that the pump operates with permanent parameters.

## Simulation results on driving cycles

This study extends the methodology for design of vehicle energy systems on other drive cycles. The peri-urban cycle represents the home to work commuting. The holiday cycle is representative for long distances drives. The New European driving cycle (NEDC) is well known as reference and is as well considered. Table 3-1 summarizes their characteristics.

<table><caption>Table 3-1: Drive cycles characteristics</caption><thead><tr><th>Cycle a</th><th>Distance, km</th><th>Duration, s</th><th>Average speed, km/h</th><th>Min acceleration, m/s²</th><th>Max acceleration, m/s²</th></tr></thead><tbody><tr><td>NEDC</td><td>11.023</td><td>1180</td><td>32.26</td><td></td><td></td></tr><tr><td>Peri-urban</td><td>39</td><td>2440</td><td>57</td><td>-2</td><td>2</td></tr><tr><td>Holiday</td><td>847</td><td>28800</td><td>105</td><td>-1.9</td><td>1.9</td></tr></tbody></table>

## Comparison of different configurations

The gasoline engine in the hybrid electric powertrain obtains an additional flow of electrical power thanks to the heat recovery from the ORC. The studied architecture is a mild hybrid electric powertrain, as presented in Figure B-9. The hybrid electric powertrain has already an electric energy recovery system. This category of recovery system has an electric energy storage in the high voltage battery and is an effective solution for low speed drives, where the power delivered by the electric part of the powertrain is enough to drive the vehicles on purely electric drive. The fuel consumption is zero. The selected ORC design is optimized for the area characterized with high rotation speeds and high loads. This is a complementary zone, where the mild hybrid electric powertrain is not acting. The simulations results are displayed in the Table 3-2. The fuel consumption result on NEDC for the basic configuration of the vehicle is 4.6 l/100 km.

The Mild HEV is a low degree electrical hybridization of the vehicles powertrains, characterized with low capacity of the high voltage battery – between 1.2 kWh and 6 kWh. The efficiency improvement of the MHEV on the NEDC and the Peri-urban cycles is around 10 %, because these cycles except the urban part, have also a large extra urban part, which is more energy demanding and where the MHEV mode is not active. On the holiday cycle, the MHEV is not used. In conclusion the Mild HEV is an adapted technology for the city drive and can be consider as effective technology for urban drives.

The ORC design proposed for the net power map of the Figure 3-2 is optimal for high load and high speed engines operating points. For these points we have high temperature exhaust gases. Thus the waste heat recovery potential is important. The impact proposed ORC design is estimated on the different driving cycles. At first just the ORC impact is considered. From the results in Table 3-2 is visible that on the combined cycles (with urban part and extra urban part) – NEDC and peri-urban the efficiency improvement is 24% and respectively 34%. The ORC is well used in the charged part of the cycles. The fuel consumption reduction potential on holiday cycle is around 40%. The fuel consumption is below 4.5 l/100 km. The selected ORC design is an efficient equipment to recover the waste heat in the high speed demanding drives. In these zones the engine power demand is high.

It is as well interesting to analyze the aggregation of the MHEV and the ORC. For the combined cycles the combination of a MHEV and an ORC on a small downsized engine brings the best fuel saving potential – around 40%. The MHEV contributes to the efficiency improvement on the peri-urban drives and the ORC on the extra-urban drives. For the “holiday” long drive cycle, the ORC is the major contributor of the improvement.

<footnote>ª The peri-urban and holiday cycles are based on the parts of the WLTP cycle.</footnote>
The lowest consumption is obtained for NEDC cycle, because of its particularity to combine urban and highway drives at very smooth and progressive speed changing conditions. By their construction, based on WLTP, the peri-urban and the holiday cycles combine more transients and higher accelerations. In all configurations, their consumption is higher than the NEDC consumption.

In conclusion the mix of a low degree electric hybridization technology such as the Mild HEV and a technology for the waste heat recovery as the organic Rankine cycle is efficient for mixed usages of the vehicle. For the C-Segment vehicle the aggregation of the two energy recovery options is efficient. The powertrain efficiency increase for NEDC is of around 40%. The vehicle consumption is 3.32 l/100 km.

<table><caption>Table 3-2: Driving cycles and fuel consumption results on C-Segment Vehicle</caption><thead><tr><th>Driving Cycle</th><th>Vehicle type</th><th>Fuel consumption, l/100 km</th><th>Powertrain efficiency gain, %</th></tr></thead><tbody><tr><td rowspan="4">NEDC</td><td>C-Segment 1.2</td><td>4.72</td><td>-</td></tr><tr><td>C-Segment 1.2 MHEV</td><td>4.16</td><td>10.8</td></tr><tr><td>C-Segment 1.2 ORC</td><td>3.71</td><td>24</td></tr><tr><td>C-Segment 1.2 ORC MHEV</td><td>3.32</td><td>38.4</td></tr><tr><td rowspan="4">Peri-urban</td><td>C-Segment 1.2</td><td>5.56</td><td>-</td></tr><tr><td>C-Segment 1.2 MHEV</td><td>5.05</td><td>10</td></tr><tr><td>C-Segment 1.2 ORC</td><td>4.14</td><td>34.2</td></tr><tr><td>C-Segment 1.2 ORC MHEV</td><td>3.98</td><td>39.7</td></tr><tr><td rowspan="4">Holidays</td><td>C-Segment 1.2</td><td>5.94</td><td>-</td></tr><tr><td>C-Segment 1.2 MHEV</td><td>5.86</td><td>1.3</td></tr><tr><td>C-Segment 1.2 ORC</td><td>4.27</td><td>38.9</td></tr><tr><td>C-Segment 1.2 ORC MHEV</td><td>4.35</td><td>36.2</td></tr></tbody></table>

The system is interesting to be realized and to be tested. The practical difficulty is related to the ORC installation. The Mild HEV architecture is well known in the state of the art and exists in serial production. The discussed ORC system presents challenges on the direct mechanical connexion between turbine and the electric generator, because of the high the rotation speed that they have to operate. The choice and the realization of the small size ORC components (pump and turbine) is as well challenging.

## Conclusions

This research presents a study of the efficiency performance and the economic optimization for a middle class vehicle with a downsized gasoline engine. Two technological options are studied for the efficiency improvement of the baseline vehicle powertrain:

* Low degree electric hybridization technology – called Mild Hybrid electric powertrain;

* Waste heat recovery system for two heat sources the engine water circuit and the exhaust gases, means an external thermodynamic cycle – ORC.

The ORC is designed on an optimal way for high load and high speed engine operating conditions. The design is optimized considering techno-economic performances indicators. For this evaluation a multi-objective optimization methodology is applied, including the energy integration. For this reason, the best fuel consumption improvement is obtained for the holiday cycle, characterized by long ways drives at high speed. For the combined cycles such as NEDC and peri-urban the combination of ORC and MHEV bring the best fuel saving potential, superior to 35% in comparison with normal ICE vehicle.

The main conclusion coming from the driving cycles evaluation is the MHEV is an efficient technology for the driving cycles with speed variations. The selected ORC design is efficient on the cycles using the charged area of the engine. For these operating points, the small gasoline engine is highly charged, due to the high power vehicle demands. The MHEV and the ORC are complementary technologies and their combination is especially efficient on the combined cycles containing urban and extra urban part.

The research of the optimal design of the hybrid electric vehicles (Appendix A) presents a powertrain design study on hybrid electric vehicles, considering different vehicle usages through adapted driving
profile – normalized cycle. The optimal environomic configurations are researched by using multi
objective optimization techniques. The optimization methodology is based on a genetic algorithm and
is applied for defining the optimal set of decision variables for powertrain design. The analysis of the
environomic Pareto curves on NEDC illustrates the relation between the economic and the
environmental performances of the solutions. The life cycle inventory allows calculating the
environmental performance of the optimal techno-economic solutions. The environmental and the
economic trades-off are defined for the different impact categories. Their impact for the production
phase and the use phase of the vehicle is studied. The sensitivity of the impacts categories on the
electricity production mix is as well studied.

In a second step the optimization is extended to a multi objective optimization with three objectives, integrating the environmental impacts as objective. The analysis of the evolution of the four impacts categories allows choosing one main environmental category, the GWP, to be minimized.

The analysis of the environomic Pareto curves on NEDC illustrates the relation between the economic and the environmental performances of the solutions. The optimization problem is then simplified from 3 objectives to 2 objectives optimization. The life cycle inventory allows calculating the environmental performance of the optimal techno-economic solutions.

The solutions in the lowest emissions zone show that the maximal powertrain efficiency on NEDC is limited on 45.2% and the minimal tank-to-wheel CO2 emissions are 30 g CO2/km. They have the maximal cost – 75000 Euros.

The increase of the electric part of the powertrain increases all environmental categories, because of the materials and the processes to produce the electric components.

## 3.2 Energy converters on-board

There are three main energy converters known on-board for automotive applications: internal combustion engines (ICE), electric machines (delivering torque by rotation) and fuel cells.

The ICE and the traction electric machines are well known and largely used in the industry for vehicular applications. They are combined in different architecture and constitute the main conversion components in the thermal, hybrid electric and electric vehicles. The fuel cell is a less applied technology for onboard energy conversion vehicular applications. The technology is mature but the main issues to be solved are related to the hydrogen availability and the cost of the technology that is still elevated. The main principles are shortly described in Appendix C. Chapter 5 studies a fuel cell application in vehicular energy system. Other type of fuel cell (converters) could be considered: alkaline fuel cell (AFC) or Proton-exchange membrane fuel cells (PEMFC), Solid Oxide Fuel Cell (SOFC).

In this part of the chapter, it is presented the development of an innovative electromagnetic actuator for an engine valve train. This converter acts on the principle of linear electric machine, in bi-modes, converting electricity to force or force to electricity. The application of the designed innovative actuator is to replace the total mechanical engine valve train. This research contributes to improve the ICE effective efficiency, by reducing the mechanical friction and the induced (thermodynamic) efficiency of the ICE. The linear electromagnetic actuator represents, as well the opportunity to replace the well known mechanical function of the engine camshafts by electric driven valve train. This chapter investigates an innovative technology improving the energy conversion efficiency of the internal combustion engine.
## 3.2.1 Development of robust control of an electromagnetic actuator

This research presents an innovative application of an electromagnetic actuator for a future camless engine valvetrain. The actuators are designed for a small gasoline engine. The design method is inversed to create a control-oriented model. The control architecture is designed for a robust control of the actuator. An experimental test bench is built for the correlation of the control model with experimental measures. Control strategies are developed and the performance indicators of the actuators are evaluated. The robust controller method uses CRONE system design methodology. The investigations show that the design of the innovative actuator and of its controller provides very good dynamic performances.

### 3.2.1.1 System description: description of the innovative electromagnetic actuator

The Lorentz force based actuator module is designed for two valves, to act on an individual way each of the valves. The modules are equivalent for the intake or the exhaust side of the engine. The principle and the details of the actuator are described in [102].

<figure><img src="image_9.png" /><figcaption>Figure 3-3: Schematic of the actuator</figcaption></figure>

The valve module (Figure 3-3) is composed of one fixe part that forms the magnetic circuit, including one permanent magnet and ferromagnetic parts. The magnetic circuit is closed by using full ferromagnetic cylinders (26 and 24). The part 28 is also ferromagnetic and is used as cover to maintain fixed the cylinders (26 and 28). They are crossing the sandwich of the permanent magnet (22) and the two ferromagnetic parts (23a and 23b). The set of part indicated as (19) is fixed. The moving parts of the actuator are indicated as sets of parts (21) and (20). The actuator has one moving part for every valve. The moving part is composed of one carcass (42) and (36), which are concentric with the ferromagnetic cylinders (26) and (28). The carcass is wearing the wires (43, 44, 34, 35) and the valve is fixed on its bottom extremity of the carcasses (42) and (36). The material of the carcasses is chosen to be a composite. This allows enough good mechanical properties to pass the efforts for lightweight characteristics. The order of magnitude of the moving mass is around 36 grams, which insure low kinetic energy demand while moving. The composite is neutral for the electromagnetic field. The wires of every moving part are connected through flexible connections to the vehicle electric grid and can operate on different voltage levels. Other advantage of the innovative design is the small air gap in the magnetic circuit, important for the electric consumption of the actuator. The electric connection is independent for each valve, which insures an individual control and acting of the valves. The maximal valve lift is 7 mm. The size of the permanent magnet and the shape of the ferromagnetic parts that surround the permanent magnet are studied and optimized to deliver the target of the maximal force. The ferromagnetic cylinders close the magnetic circuit with a very small air gap, which is needed for good
efficiency of the actuator. The efficiency is directly related with the electricity consumption of the actuators, which has to be minimized. The discussed actuator design is innovative its magnetic circuit and lightweight moving part, including the valves and the carcasses, where the coper is wired.

The volume of the electromagnetic actuator module is compact. The dimensions are 50 mm*80 mm*55 mm. The actuator is delivering an electromagnetic force and is governed by:

$$F_{act}(I, Z) = B(Z) \cdot I \cdot L_{coil} \qquad (3-3)$$

where $F_{act}$ is the electromagnetic force delivered by the actuator in [N], $B$ is the magnetic field in [T], $I$ is the current amperage [A], $L_{coil}$ is the length of the wire in [m] and $Z$ is the valve position (m).

The size of the permanent magnet is related to the magnetic field and then to the electromagnetic force, delivered by the actuator and governed by equation (3-3) The relation between magnetic field, current and valve position has been modelled (Figure 3-4). This model has been obtained after simulation with Finite Elements Method Magnetics (FEMM), which contains Maxwell equations. The FEMM model of the actuator is validated with experimental results of the prototypes. As the current limit is 90A, the electromagnetic maximal force delivered by the actuator on each valve is 400 N. This level of force allows deploying the module also on the exhaust side, where the effort to open the combustion chamber under pressure is important. The actuator does not contain a mechanical spring with an energy storage function. Its structure is active, without a passive mass/spring system and present main advantages for the controlling of the actuator. The active principle of the controlling avoids the passive resonance modes, which are difficult to control and provoke vibrations and noise during when the valve lands on the valve seat.

<figure><img src="image_10.png" /><figcaption>Figure 3-4: Electromagnetic modelling of the actuator</figcaption></figure>

The introduced actuator has an efficient magnetic circuit. Due to its design the actuator is directly driven by the current and belongs to the so-called family of active actuators. This fact introduces the novelty of the study and also presents an advantage for the active control of the actuator and thus the precision obtained by the control of the actuator. The method used for the control of the active actuator presents interest for the state of the technology. The implementation and the experimental results are as well useful.

The actuator nonlinear model is described in Appendix B

### 3.2.1.2 Design of the control system

In order to make the EMA a reliable solution to perform variable valve timing operation in Camless engines, an extremely delicate stage to be addressed is the design of the Control System ([103], [104], [105], [106]). The main function of the CS is to move the valve from the closed to the opened position (and vice versa) while satisfying robust tracking of the reference position valve (Figure_B-11) [107],
low energy consumption requirements and avoiding high intensity impacts [108] (ex: impacting velocity less than 0.4 (m/s) at 6000 rpm).

<figure><img src="image_11.png" /><figcaption>Figure 3-5: Structure of the control system architecture</figcaption></figure>

For this purpose, a control system architecture presented by Figure 3-5 is proposed. It consists mainly of a robust controller designed with the CRONE methodology allowing a control by feedback position provided by a sensor system and a complementary feedforward control.

## Frequency domain system identification of the actuator

The global model of the actuator is highly nonlinear, and because of the natural integral behavior, the limits of displacement, the variable friction force and the very high speed of the actuator, it is difficult to achieve an open loop and time-domain classical system identification of its dynamical behavior. Thus, for the design of the feedback controller with the frequency-domain based CRONE methodology, a frequency-domain system identification of the linear behavior of the actuator around an operating point close to the middle valve position is achieved.

First, for the local stabilization of the valve position around 3mm, a very simple PID controller is designed from the gain and phase of the system roughly measured in open loop at 1000 rad/s. The mean value of $U_{act}$ and its sine variation amplitude have been defined by a trial and error process. The PID controller ensures a 1000 rad/s open-loop gain crossover frequency and a 40° phase margin. Using this PID feedback controller, the frequency response of the system is measured in closed-loop: the reference valve position $Z_{ef}$ is defined by a mean value of 3mm and a sine variation with a frequency successively from 1000rad/s to 10000rad/s, no feedforward ($U_{FF} = 0$) and filter effects are used. The experimental gain and phase measures are obtained by comparing the sine part of $Z_{mes}$ to that of $U_{act}$. Then, the parameters of a black-box model defined by (3-4) are optimally tuned to fit the measured frequency response of the experimental actuator. The optimization provides the nominal values of the linear model parameters.

$$G(s) = \frac{Z_{mes}(s)}{U_{act}(s)} = \frac{K \left(1 + \frac{s}{\omega_p}\right)}{s \left(1 + \frac{2\xi_1 s}{\omega_{n1}} + \left(\frac{s}{\omega_{n1}}\right)^2\right) \left(1 + \frac{2\xi_2 s}{\omega_{n2}} + \left(\frac{s}{\omega_{n2}}\right)^2\right)} \quad (3-4)$$

Optimized parameters:

$$K_0 = 276; \omega_{p0} = 9000 \text{ rad/s}; \quad (3-5)$$

$$\omega_{n10} = 1597 \text{ rad/s}; \xi_{10} = 0.8446;$$

$$\omega_{n20} = 9840 \text{ rad/s}; \xi_{20} = 0.1069.$$
<figure><img src="image_12.png" /><figcaption>Figure 3-6: Bode diagram of nominal plant frequency response (—) and set of frequency responses of G(s) (—)</figcaption></figure>

The nominal black-box model (3-4) and its parameters defined by (3-5) are derived to define an uncertain plant model that will be used for a robust controller design. Uncertainty ranges are defined by:

$$ 
\begin{aligned}
& K \in \left[ \frac{K_0}{1.5}, K_0 * 1.5 \right]; \omega_p \in \left[ \frac{\omega_{p0}}{1.1}, \omega_{p0} * 1.1 \right]; \\
& \omega_{n1} \in \left[ \frac{\omega_{n10}}{1.1}, \omega_{n10} * 1.1 \right]; \xi_1 \in \left[ \xi_{10} - 0.1, \xi_{10} + 0.1 \right]; \\
& \omega_{n2} \in \left[ \frac{\omega_{n20}}{1.1}, \omega_{n20} * 1.1 \right]; \xi_2 \in \left[ \xi_{20} - 0.05, \xi_{20} + 0.05 \right].
\end{aligned} 
\quad (3-6) $$

Figure 3-6 presents the set of frequency responses of G that defines the uncertain plant. It shows significant uncertainties both on the gain and on the phase of the plant.

## Tuning of the parameters of the nonlinear model of the actuator

Even if the linear model of the uncertain plant is useful to design a controller whose robustness could be verified by using the experimental system, the nonlinear model presented by Figure B-10 is needed to assess the control system (feedback and feedforward controller) for more realistic working cases, for instance when pressure forces are taken into account as disturbances. Thus, the mechanical, electrical and electromechanical parameters of the nonlinear model described by section 3 are tuned by minimizing the difference between the gains and phases of its numerically linearized model (obtained with the linmod Matlab-Simulink function) and the experimental gains and phase used in section 3.1. The optimized mechanical and electrical parameter values are:

$$ 
\begin{aligned}
& L_{\text{bob}} = 95.067 \, \mu\text{H}; R_{\text{init}} = 0.03050 \, \Omega \\
& m = 0.0676 \, \text{kg}; L_{\text{coil}} = 7.4409 \, \text{m}; K_{\text{visc}} = 1.5 \, \text{Ns/m}
\end{aligned} 
\quad (3-7) $$

Figure 3-7 permits the comparison between experimental closed time responses (for an engine speed of 6000 rpm) and those of the simulated nonlinear model. The agreement of these responses validates the proposed model.
<figure><img src="image_13.png" /><figcaption>Figure 3-7: Experimental time responses (—) and nonlinear model time responses (—): position; voltage; current</figcaption></figure>

CRONE design of a robust feedback controller

The CRONE CSD methodology is a frequency-domain approach developed since the eighties ([109], [110], [111], [112], [113], [114]). It is based on the common unity-feedback configuration presented by Figure 3-5. Three CRONE CSD generations have been developed, successively extending the application field. The controller or the open-loop transfer function is defined using fractional order integro-differentiation. They enable to design, simply and methodologically, LTI robust control-systems. Using frequency uncertainty domains (for instance provided by a perturbed black-box model), the uncertainties (or perturbation) are taken into account in a fully-structured form without overestimation, thus leading to control-systems that are as less conservative and thus as high performing as possible [115]. The first two generations are based on real fractional order differentiator or integrator and provide robust feedback controller for plants with gain variations. The third generation should be used for any kind of plant perturbation model: around open loop gain crossover frequency or closed loop resonant frequency, the nominal open loop $\beta_0(s)$ (for the nominal plant $G_0(s)$) is defined by a set of band-limited complex fractional order integrators. Integer order proportional integrator and low-pass filter define $\beta_0(s)$ respectively at low and high frequencies. Right half plane or underdamped poles and zeros of the nominal model of $G_0(s)$ are included in $\beta_0(s)$ for closed-loop internal stability and damping. After some high-level parameters of $\beta_0(s)$ being optimized for a robust and high performing closed-loop, the feedback controller $C$ is finally defined from the ratio $C_R(s) = \beta_0(s)/G_0(s)$.

<figure><img src="image_14.png" /><figcaption>Figure 3-8: Nichols plot G: nominal frequency response (—); uncertainty domains (—)</figcaption></figure>
When the designed controller has to be discrete-time for an implementation purpose, in order to take into account the effect of time-discretization, the initial digital control system design problem with the sample period Ts is transformed into a pseudo-continuous problem by:

* taking into account the transfer function of a zero-order hold on the plant input;

* computing the Z-transform of the continuous-time set {plant and holder};

* achieving a bilinear variable change, for instance $z^{-1} = (1-w)/(1+w)$.

The controller $C(w)$ can then be designed using the CRONE frequency-domain method knowing that $G(w)$ (with $w = j\nu$ and the normalized pseudo-frequency $\nu = \tan(\omega T_s/2)$) is the exact frequency response of $G(z)$ (with $z = e^{j\omega T_s}$). Figure 3-9 shows the Nichols plot of $G(w)$ whose uncertainty domains take into account the uncertain parameters (4) of the plant model.

To track reference valve position for engine speed up to 6000 rpm, the desired closed-loop bandwidth should be at least more or less 900 rad/s, the corresponding pseudo-frequency is 0.02. The required nominal resonant peak of complementary sensitivity $T$ is 1 dB, the resonant peak limitation ($T$) is 4.5dB, the sensitivity function limitation ($S$) is 6dB and the plant input sensitivity function limitation ($SG$) is 3 dB. Taking into account the measurement noise, the limitation of the control effort sensitivity function ($CS$) is 48 dB. A controller with a high static gain (without integrator) is enough to ensure a good tracking of a steady state reference signal with a good rejection of the effect of disturbance on the plant input. Three right half plane zeroes of $G_0(w)$ are included in $\beta_0(w)$. The high frequency order of $\beta_0(w)$ is defined to limit the control effort sensitivity at high frequency and to obtain a strictly proper controller. The nominal open loop transfer function to be tuned is thus defined by:

$$ \beta_0(w) = K_\beta \left(\frac{v_0}{w} + 1\right)^1 \left(\frac{1+w/v_1}{1+w/v_0}\right)^{\alpha_0} \left\{ \Re e_i \left\{ \left(\alpha_0 \frac{1+w/v_1}{1+w/v_0}\right)^{i b_0} \right\} \right\}^{-q_0 \text{sign}(b_0)} \frac{\left(1-\frac{w}{1}\right)\left(1-\frac{w}{1.22}\right)\left(1-\frac{w}{95}\right)}{\left(\frac{w}{v_1}+1\right)^4} \quad (3-8) $$

<figure><img src="image_15.png" /><figcaption>Figure 3-9: Nichols plot of $\beta_0$ nominal frequency response (—); uncertainty domains (—); 1 dB M-contour (—)</figcaption></figure>

By taking into account the frequency domain uncertainty, 4 parameters of $\beta_0(w)$ are optimized in order to minimized the variations of the peak value of $T$ and to fulfil performance frequency domain constraints on sensitivity functions $T$, $S$, $SG$ and $CS$. Optimized parameter values are: $Y_r = |\beta_0(jv_r)| = 6$ dB; $v_r = 0.015$ (resonant frequency); $v_0 = 0.018$; $v_1 = 0.1$. Thus, $a_0 = 0.99$, $b_0 = 0.81$, $q_0 = 3$ and $K_\beta = 2.28$. Figure 3-9 presents the Nichols plot of the optimized open loop frequency response. All sensitivity constraints are met: the greatest values of the resonant peaks of $T$, $S$, $SG$ and $CS$ are respectively 2.07dB, 5dB, -10dB and 47dB. This last value is very close to the 48dB limit which shows that a larger bandwidth
cannot be obtained: depending on the parametric state of the system, the closed loop bandwidth is more or less between 1000 rad/s and 5000 rad/s. Then, the frequency response of the fractional version $C_F(w)$ of the controller is fitted by an order 7 rational transfer function $C_R(w)$. Finally, the digital controller $C(z)$ is obtained by achieving the inverse variable change $w = (1-z^{-1})/(1+z^{-1})$.

### Nonlinear Feedforward and plating strategy

To ensure performance for high engine speeds and to reduce the effect of the cylinder internal pressure on the output of the exhaust camless valve actuator, since the controlled system has a notable nonlinear behavior, a nonlinear feedforward control is necessary. It is based on the inversion of the dynamic model of the actuator in order to obtain at each moment a balance of the forces having an influence on the movement of the valve. The resultant $F_{act}$ of the forces acting on the actuator given by (B-15) (which also includes the friction force) is used by the online calculation (in real time) of the corresponding current $I$ using the order 5 polynomial equation that approximates the electromagnetic model of the actuator depicted by Figure 3-4. No hysteresis phenomenon has been taken into account. A recorded estimation of the pressure force $F_{pres est}$ is used into the $F_{act}$ calculation. Using a strictly-proper inverse model of the electrical part of the model, it is easy then to deduce the necessary feedforward control voltage that should ensure the valve position reference tracking and the rejection of the effect of the internal pressure in the cylinder. Furthermore, a plating strategy is added in order to maintain the valve closed especially at the end of the intake cycle. This strategy consists in applying a constant and non-zero control signal outside the opening phases of the valve, outside these phases, the control signal remains that of the valve position reference tracking.

### 3.2.1.3 Experimental and simulation assessments

Figure 3-11- Figure 3-14 present the simulation results for exhaust valve control at 6000 rpm. It should be noted that the peak pressure force is observed after the exhaust valve closing and the actuator is able to overcome the pressure force at the opening valve. Figure 3-13 Figure 3-14 Figure 3-10 show the obtained experimental results valve at 6000 rpm. This test is only significant of the intake phases as the experimental testing bench is not able to apply a resistant force close to the pressure forces observed for exhaust phases. We observe that the robust feedback and nonlinear feedforward control system ensures a good valve position tracking, Finally, in order to validate the proposed control-system, experimental valve seating velocities are shown on Table 3-3 for various engine speeds. It is demonstrated that, except for the low engine speed (<2000 rpm) where the speed estimations are very noisy, the obtained seating velocities fulfill the imposed limitations for a smooth valve seating, which ensure a “soft-touch” behavior. All these simulations and experimental tests mean that the designed controller ensures a good level of precision to drive the actuators under realistic conditions.

<table><caption>Table 3-3: Seating valve velocity (m/s) at various engine speeds</caption><thead><tr><th>Engine speed (rpm)</th><th>Experimental velocity (m/s)</th><th>Limitation criteria, Mechanical system velocity (m/s)</th></tr></thead><tbody><tr><td>1000</td><td>0.18</td><td>0.05</td></tr><tr><td>2000</td><td>0.3</td><td>0.1</td></tr><tr><td>3000</td><td>0.11</td><td>0.2</td></tr><tr><td>4000</td><td>0.17</td><td>0.3</td></tr><tr><td>5000</td><td>0.32</td><td>0.4</td></tr><tr><td>6000</td><td>0.41</td><td>0.5</td></tr></tbody></table>
<figure><img src="image_16.png" /><figcaption>Figure 3-10: Pressure force (—) and force generated by the exhaust valve actuator (—) at 6000 rpm</figcaption></figure>

<figure><img src="image_17.png" /><figcaption>Figure 3-11: Opening and closing of the exhaust valve at 6000 rpm: reference signal (—); simulation response (—)</figcaption></figure>

<figure><img src="image_18.png" /><figcaption>Figure 3-12: Control voltage at 6000 rpm (exhaust simulation)</figcaption></figure>

<figure><img src="image_19.png" /><figcaption>Figure 3-13: Opening and closing of the intake valve at 6000 rpm: reference signal (—); experimental response (—).</figcaption></figure>
<figure><img src="image_20.png" /><figcaption>Figure 3-14: Control voltage at 6000 rpm</figcaption></figure>

Conclusions

The precision of the developed actuator and the robustness of the designed control system are important parameters for the integration of the actuators in the engine cylinder head. This electromagnetic actuator gives the possibility to manage the engine gas exchange on a very performant way. The precision provided by the control system attests the controllability of the actuators technology and thus the good performances of the engine. The robust and precise controlling of the actuators on the intake side is especially important for the regulation of the airflow in the engine and this for gasoline engine concerns directly the regulation of the engine torque. The respect of the velocity requirements during the valve landing is an important step and insures the respect of the noise criteria and the mechanical durability of the system.

These results are promising for the development of the engine control system that manages the gas exchanges of the engine. Future steps of the work could be the integration of the actuators in the engine cylinder head, the integration of the electronics and the development of the control strategies for full camless engine.

## 3.3 Energy storage on-board – high voltage batteries

This part of the chapter introduces and states the art of the current technologies for energy storage on board for electric and electrified vehicles that are the high voltage batteries. The compactness and the energy density are the main technology challenges in front of the energy storage. The process in this domain is discussed and the innovative technology of solid state batteries is introduced. The range of the electric vehicles and the range of electric mode for the electrified vehicles is the main challenge. This chapter investigates optimal design methods to increase the range of the electric vehicles. The operational parameters and especially the temperature in the battery pack is an important parameter for the good performances of the batteries. Energy balance of the high voltage battery pack is investigated by experimental way in this chapter. The experimental data of the energy balance are used to design effective cooling strategies and technologies for batteries cooling. The experimental data allow as well modelling the thermal behavior of the batteries. The thermal models can be used to monitor the state of change and the state of health of the high voltage battery.

### 3.3.1 The progress of the compactness and the energy density: the hope of the Solid State batteries.

Electrochemical batteries are key components of the electric vehicles and the hybrid electric vehicles. Batteries are devices that transfer chemical energy into electrical energy and vice-versa. They represent a reversible electrical energy storage system. Traction batteries are characterized in terms of power, which has to match the power of the electric path and nominal capacity, which has to match the desired driving range specification. The nominal capacity is usually expressed in Ah, which is the temporal
integral of the current that could be delivered by a full battery when it is completely discharged under certain conditions. A dimensionless parameter is the state of charge (SoC), which describes the capacity remaining in the battery expressed as a percentage of its nominal capacity. Desirable attributes of traction batteries for EV and HEV applications are:

* High specific energy

* High specific power

* Long lifecycle time

* Low initial and replacement costs

* High reliability

* Wide range of operating temperatures

* High robustness

Figure 3-15 illustrates the schematic of a battery cell.

<figure><img src="image_21.png" /><figcaption>Figure 3-15: Battery cell components and principle</figcaption></figure>

The specific energy is the amount of useful energy that can be stored in the battery par unit mass, typically expressed in Wh/kg. This parameter takes into account the fact that not the whole capacity can be actually used. In fact, the battery operation is typically defined by a certain SoC window, whose limits are the minimum SoC that can be attained during discharging and the maximum SoC that can be reached during charging. The charging and discharging phase of a Li-Ion cell are presented in Figure 3-16.

<figure><img src="image_22.png" /><figcaption>Figure 3-16: Discharging and charging phases of a Li-Ion cell</figcaption></figure>

The specific energy of a battery is important for the range of a purely electric vehicle. For hybrid electric vehicles, possible more important is the specific power, typically expressed in W/kg, which determines the acceleration and top speed that the vehicle has to reach.

A panoramic vision of the battery types and their applications is given in Figure 3-17. The energy densities of the batteries cells applied in the serial hybrid electric and electric vehicles are between 90 and 150 Wh/kg. The electric vehicles that are currently under development with a range target over 200 km can obtain Li-Ion batteries with 200 Wh/kg of cells density. The cells energy density is important
for the development of the electric vehicles and the research is working on future generation of batteries
with the target to increase their energy density.

<figure><img src="image_23.png" /><figcaption>Figure 3-17: Cells technologies – evolutions of the technologies and their maturity [116]</figcaption></figure>

Batteries are composed of a number of individual cells in which the main components are present: two electrodes, where half reaction takes place, resulting in a circulation of electrons through an external load, and a medium that provides the ions transport mechanism between the positive and negative electrodes. The cathode is the electrode where reduction (gain of electrons) takes place. It is the positive electrode during discharging and negative electrode during charging. Contrary, the anode is the electrode where oxidation (loss of electrons) takes place. It is the negative electrode during discharging, the positive during charging.

Recent research shows that batteries with solid electrolyte can present advantages to compactness and stability, in comparison of the current technologies of Li-Ion batteries, with liquid electrolyte. The main claims of the solid-state batteries are:

* Improved safety (no leakage, volatilization, flammability)

* Improved electrochemical performances (higher energy density for increased EV autonomy, capacity and voltage output)

* Lower specific cost (€/ kWh)(packaging opportunity because of the downsizing and the compactness if the technology, materials use and manufacturing)

Figure 3-18 presents the evolutions stages between the conventional Li-Ion battery with liquid electrolyte and the all solid-state components, which is convenient for bipolar cell concepts, inducing downsizing of the battery packs. The hybrid electrolyte (Jellify Polymer) is an intermediate technology.

<figure><img src="image_24.png" /><figcaption>Figure 3-18: Evolution of the batteries technology: opportunities for compactness</figcaption></figure>
The main gain of compactness is done by reduction of the dimensions of the anode and the electrolyte separator. The solid state batteries have Li Metal Anode with high specific current of 3860 mAh/g. As the electrolyte is solid none separator is needed for the electrolyte. Figure 3-19 displays the comparisons of the dimensions of the battery types.

<figure><img src="image_25.png" /><figcaption>Figure 3-19: Comparison of the dimensions for different battery types</figcaption></figure>

There are still a lot of challenges, fundamentals, and production and integration points, remaining to bring the solid-state battery technology to the market. The most important fundamental problems to be solved are (Figure 3-20):

* Ionic conductivity of the electrolyte,

* Improvement of the solid- solid interface (resistive Solid-Electrolyte Interphase (SEI) between Li and the electrolyte, the life span, grain boundaries lead to increased resistance of the cell)

* Dendrite growth: inhomogeneous Li deposition

* Stability: solid electrolyte react with active materials and need to be protected

<figure><img src="image_26.png" /><figcaption>Figure 3-20: Fundamentals issues of the solid-state batteries</figcaption></figure>

There are three main families of solid electrolytes, presented in Figure 3-21: solid polymer electrolyte, ceramic electrolyte and hybrid composite. The liquid electrolyte (Li salt in organic solvents) is given for comparison.
<figure><img src="image_27.png" /><figcaption>Figure 3-21: Classification of the solid electrolyte types</figcaption></figure>

The solid-state battery technology is promising but it is still far from market maturity. In the near future the Li-Ion battery technology with liquid electrolyte is remaining the main technology for the electrification of the vehicles and it needs still to be improved and well managed when integrated in the vehicles (packaging, electric connections, thermal cooling and controlling (Battery System Management).

### Conclusions of the study “Optimal designs of electric vehicles for a long-range mobility”

The relations between the battery capacity, the battery energy density and the range of the electric vehicles are investigated in Appendix C.2. The designs for different mobility services (urban and long ways distance electric mobility) of the electric vehicles are defined and the time interaction with the charging infrastructure is analyzed. Information and connectivity solutions that inform in real time the drivers for the availability and the location of the charging infrastructure, including the possibility to book and pay for charge points in different places is an important part of the solution. Combined with fast, i.e. high power charging capacities, pure electric vehicles would be able to serve long range needs. The analysis of the environmental Pareto curves on NEDC illustrates as well the relation between the economic and the environmental performances of the solutions. Electric vehicles can be used for long way mobility (>400 km), if they have a consequent quantity of battery on board (between 100 and 150 kWh). The density of the battery is also important for the autonomy of the electric vehicles. The important capacity of the batteries induces the need of the high power chargers to allow the fast charging of the batteries. The pick power demands induce to investigate adequate links for such electric vehicles with the electricity grid. The multi-objective optimization can be used in an enlarged energy system, including the electric vehicles and the electric grid.

### 3.3.2 An energy balance evaluation in Li-Ion battery packs under high temperature operation

Electric vehicles and hybrid electric vehicles are expected to increase their market penetration and to bring evolution concerning the main technologies for energy storage and conversion, the drive train components and the energy management. Lithium battery systems become a major component in vehicle drivetrains. In this research, the investigation of the energy balance evolution for Li-Ion battery system is described. The accessory environment of lithium battery systems is important for an accurate thermal and electrical management and the respect of the performances. A method to determine the energy balance through experimental investigations is proposed. Different configurations of Al and Cu busbars were used during the research. The evaluation of the energy balance is related to the energy dissipated and the exergy available in the battery system. The influence of different configurations of interconnections on the energy balance is shown.

In this study the energy balance of a battery package made from 18650 type cylindrical cells is investigated. The energy balance was calculated for a discharge process to avoid the influence of a power supply on heat transport during the charge process. The calculation and measurements were conducted for a realistic scenario. The maximum discharge current 2C-3C was used in order to keep the
temperature of the cells below 650°C. Battery modules were equipped with aluminum (Al) busbar or copper (Cu) busbar. The calculation of the energy balance was made with the proposed electro-thermal model of a battery module. The validation of the calculation was made through the measurements.

The first part of the study gives a proposal of a method to evaluate the quantity of energy produced under the effect of discharge process. An energy balance was built for the system characterization. This energy was evaluated for different types of materials of the busbars. The second part is dedicated to the experimental tests. In this part different configurations of busbars are evaluated.

### 3.3.2.1 Energy balance in Li-ion battery system

Li-ion battery systems are a very attractive technology for energy storage systems (ESS) for EVs and HEVs. They provide very high energy density and they enable the storage of high capacity in relatively compact cells. Based on the prediction, the market demand for the battery will rise very quickly. Based on the cylindrical cell it is possible to build modular systems of the battery packages. These battery packages can be easily connected in order to build big ESS. However, the operating temperature needs to be maintained within a specified range. This is needed to keep the electrical performance in a nominal range and to slow down the aging process of the cells.

In order to determine the energy balance, the amount of energy in the battery package has to be determined. The formula for the amount of total energy $E_{tot}$ in a battery pack with b cells is shown in equation (3-9):

$$E_{tot} = b \cdot V_n \cdot C \qquad (3-9)$$

where, b – number of the cells in a battery pack, $V_n$ – nominal voltage of the Li-ion cell, C – capacity of the Li-ion cell.

However, this theoretical amount of energy is not available in normal conditions. To obtain the total energy the cell must be fully discharged, which leads to the destruction of the Li-ion battery.

The total accumulated energy $E_{tot}$ can be divided into six parts of energy as shown in Figure 3-22.

The first two parts are:

* heat $Q_{cell}$, which is generated in the cells due to the internal resistance of cells $R_{wc}$ and the chemical reactions in these cells,

* heat $Q_{cab}$ generated in all cables and metallic connections due to their resistance $R_c$ and $R_{con}$ used to connect the battery package with the load,

* the $Q_{dis}$ is the dissipated heat due to convection from the cells and the busbars,

* the fourth part is the $Q_{bus}$, and this is a Joules heat generated in the busbar,

* the fifth part in an energy EL that is delivered and effectively used by the load,

* the last part is the $E_{BMS}$, which is an energy delivered to Battery Management System (BMS).

First four parts are frequently considered as the lost energy, which reduces the efficiency of energy conversion in a battery pack, and limit the total amount of the exergy. In addition, these energies make the whole package warm up which is usually a highly undesired effect. The heat dissipated to ambient form metallic connections and cables is very small and it can be neglected.
<figure><img src="image_28.png" /><figcaption>Figure 3-22: Energy distribution in the energy storage system</figcaption></figure>

<figure><img src="image_29.png" /><figcaption>Figure 3-23: Energy flow in a battery pack</figcaption></figure>

The heat $Q_{cell}$ is generated in every cell of the battery pack. The Joules heat is generated in the busbars, cables and metallic connections between the cell and busbar as shown in Figure 3-23. Some of that heat is dissipated from the cells and busbars to ambient. The cells inside the battery package have a different condition for heat dissipation hence the cells inside the battery package can exchange the heat $Q_{Ncell}$ between themselves.

The equation (3-10) for heat generation inside the battery was proposed by Bernardi [117]:

$$ \dot{Q}_{cell} = -IV - \sum_{l} I_{l} T^{2} \frac{d \frac{U_{l,ocv}}{T}}{dT} + \sum_{l} \frac{d}{dt} \left[ \int_{v_j} \sum_{i} c_{i,j} RT^2 \frac{\partial}{\partial T} \ln \left( \frac{\gamma_{i,j}}{\gamma_{i,j}^{avg}} \right) dv_j \right] + \sum_{j,j \neq m} \sum_{i} \left[ \left( \Delta H_{i,j \to m}^{0} - RT^2 \frac{d}{dT} \ln \frac{\gamma_{i,m}^{avg}}{\gamma_{i,j}^{avg}} \right) \frac{dn_{i,j}}{dt} \right] \quad (3-10) $$

The first term in equation (3-10) is the electrical power produced in the battery, the second term is the sum of producible reversible work and entropic heating from the reaction. The third term is the heat produced from mixing, and the last term is the heat from material phase change.
This formula can be simplified to the form of equation (3-11) when the enthalpy-of-mixing and phase change terms are not considered [117] [118].

$$ \dot{Q}_{cell} = IV - \sum_{l} I_{l} \left[ U_{l,ocv} - T \partial \frac{U_{l,ocv}}{\partial T} \right] + M_{c} c_{pc} \frac{\partial T}{\partial t} \quad (3-11) $$

Recently, a new proposal of equation (3-11) has been presented [119] into the following equation (3-12):

$$ \dot{Q}_{cell} = R_{wc} i_{cell}^2 - i_{cell} T \frac{\Delta S}{n_e F} \quad (3-12) $$

where $R_{wc}$, $i_{cell}$, $n_e$, $F$ and $\Delta S$ denote internal resistance of a cell, current of a single cell, number of electrons participating in the reaction, Faraday constant and entropy change. The values of $\Delta S$ and $R_{wc}$ for cylindrical batteries SONY-US18650G3 was estimated by Inui et al., [120].

In the battery package the heat is not only generated in the cells. The Joules heat generation occurs in busbars and also in all metallic connections and wires. The Joules heat in a battery pack can be calculated with equation (3-13):

$$ \dot{Q}_{bus} + \dot{Q}_{cab} = i_{BP} \cdot V_{BP} = i_{BP}^2 \cdot (R_{bus} + R_c) \quad (3-13) $$

where $Q_{bus}$ is the Joules heat generated in the busbars and $Q_{cab}$ is sum of the Joules heat generated in cables $Q_c$ and metallic connections $Q_{con}$, $i_{BP}$ is a current in battery pack, $V_{BP}$ is a voltage of a battery pack, $R_c$ is a resistance of cables and metallic connections in a battery package.

In a battery package some part of $E_{tot}$ will be lost. This energy, called $Q_{dis}$ is a heat dissipation to the ambient from the cells and the busbars and it can be described with equation (3-14):

$$ \dot{Q}_{dis} = \dot{Q}_{Lcell} + \dot{Q}_{Lbus} \quad (3-14) $$

where $Q_{Lcell}$ and $Q_{Lbus}$ are the heats dissipated from the cells and the busbars.

One of the most important parameters of a battery is the energy efficiency which is often [40] defined as the ratio of a provided electrical energy $E_L + E_{BMS}$ to the $E_{tot}$ of the fully-charged battery as presented in equation (3-15).

$$ \eta_{cell} = \frac{E_L + E_{BMS}}{E_{tot}} = \frac{\int (R_{load} + R_{BMS}) \cdot i_{cell}^2(t) dt}{V_n \cdot C} \quad (3-15) $$

where $R_{load}$ is the resistance of a load.

The amount of energy required for the BMS is hard to predict, since different constructions of the BMS consume different amount of electric energy. In further investigation presented this energy will be neglected.
### 3.3.2.2 Electro-thermal model of a single cell in a battery pack

#### Model formulation

The heat change $Q$ of a battery package with $b$ cells can be calculated with a proposed heat accumulation model described by equation (3-16):

$$ \dot{Q} = b\dot{Q}_{cell} + \dot{Q}_{bus} + \dot{Q}_{cab} - (b\dot{Q}_{Lcell} + \dot{Q}_{Lbus}) \quad (3-16) $$

If the temperature of all cells in the battery package is uniform the equation (3-11) and equation (3-16) can be transferred to another form (3-17) to easier designate the terms conducted with the heat accumulation in a cell. The first term on the right side of equation (3-17) is related to the heat generated in battery package $Q_{GEN}$. The second term is related to the heat dissipation from the cells and busbars to the air.

$$ \frac{\dot{Q}}{S_{BP}} = \frac{\dot{Q}_{GEN}}{S_{BP}} - [h_c(T_c - T_A) + h_b(T_b - T_A)] \quad (3-17) $$

where, $S_{BP}$ is a surface of a battery package, $h_c$ is the heat transfer coefficient of a cell, $h_b$ is the heat transfer coefficient of a busbar, $T_c$ and $T_A$ are the cell and ambient temperature.

The heat generation takes place in three elements of the battery pack: in the cells, in the busbars and in the metallic connections and cables. Therefore $Q_{GEN}$ in a battery pack can be expressed with equation (3-18):

$$ \dot{Q}_{GEN} = \dot{Q}_{cell} + \dot{Q}_{bus} + \dot{Q}_{cab} \quad (3-18) $$

It is known [121], [122], [123] that heat generation in a single cell is a sum of the reversible heat of the electrodes $Q_{rev}$ due to entropy change, the irreversible heat $Q_{irr}$ which corresponds to all types of heat generated in the cell as a result of the electrochemical reaction $Q_r$ and ohmic heating $Q_{ohmic}$, as shown in equation (3-19).

$$ \dot{Q}_{cell} = \dot{Q}_{irr} + \dot{Q}_{rev} = \dot{Q}_r + \dot{Q}_{ohmic} + i_{cell}T \frac{\partial U_{ocv}}{\partial T} \quad (3-19) $$

From equation (3-13), equation (3-18) and equation (3-20) the total heat generation in a battery package can be calculated:

$$ \dot{Q}_{GEN} = b(\dot{Q}_r + \dot{Q}_{ohmic}) + i_{BP}T \frac{\partial U_{ocv}}{\partial T} + i_{BP}^2 R_c + i_{BP}^2 R_{bus} \quad (3-20) $$

The $Q_{irr}$ can be also presented in the following form [118], [121], [124]:

$$ \dot{Q}_{irr} = i_{BP}(U_{ocv} - V) \quad (3-21) $$

where, $V$ is the terminal voltage of the cells.
Based on equation (3-17), equation (3-20) and equation (3-21) the heat accumulated in a battery package can be calculated according to equation (3-22):

$$ \dot{Q} = i_{BP}(U_{ocv} - V) + i_{BP}T \frac{\partial U_{ocv}}{\partial T} + i_{BP}^2(R_c + R_{bus}) - [h_c S_c(T_c - T_A) + h_b S_b(T_b - T_A)] \quad (3-22) $$

It can be assumed that the heat accumulation of the battery package is the heat accumulation of all cells $b \cdot Q_{Acell}$ and heat accumulation of busbars $Q_{Abus}$ as shown in equation (3-23) hence the heat accumulation of a battery package can be calculated from equation (3-24).

$$ \dot{Q} = b \dot{Q}_{Acell} + \dot{Q}_{Abus} = \frac{b M_c c_{pc}}{\partial t} (T_c - T_A) + \frac{M_b c_{pb}}{\partial t} (T_b - T_A) \quad (3-23) $$

$$ \frac{b M_c c_{pc}}{\partial t} (T_c - T_A) = (i_{BP}(U_{ocv} - V) + i_{BP}T \frac{\partial U_{ocv}}{\partial T}) + i_{BP}^2(R_c + R_{bus}) - [h_c(T_c - T_A)S_c + h_b(T_b - T_A)S_b + \frac{M_b c_{pb}}{\partial t} (T_b - T_A)] \quad (3-24) $$

## Model assumptions and calculation

To calculate the electro-thermal model the following constants had to be assumed: heat transfer coefficient of the side walls of the cells $h_c$, heat transfer coefficient of copper busbar $h_{b\_Cu}$, heat transfer coefficient of aluminum busbar $h_{b\_Al}$, specific heat of li-ion cells $c_{pc}$ and busbar $c_{pb}$. Based on the literature the following values were assumed: $h_c = 7.9 \text{ W/m}^2\text{K}$ [120], [125], [126], $c_{pc} = 800 \text{ J/kgK}$ [127], [128], $h_{b\_Al} = 20 \text{ W/m}^2\text{K}$ [129], [130], $h_{b\_Cu} = 15 \text{ W/m}^2\text{K}$ [130], [131], specific heat of Al busbars $c_{pb\_Al} = 900 \text{ J/kgK}$ [129] and specific heat of Cu busbar $c_{pb\_Cu} = 385 \text{ J/kgK}$ [129].

Enthalpy-of-mixing and phase change terms are not considered. In addition, all cells were carefully selected before the tests and they had designated capacity and internal resistance. The difference of cells SoC level was less than 1%. Moreover, the nominal voltage of the whole battery package was measured just before the test to obtain the same conditions during measurements.

For the calculations the battery package made of sixteen 18650 type cylindrical cells connected in parallel was assumed. The cells are connected with two busbars made of Al or Cu placed on top and bottom of the cells.

<figure><img src="image_30.png" /><figcaption>Figure 3-24: Temperature inside the battery package, in °C</figcaption></figure>

The temperature inside the battery package is uniform and there is no heat exchange between the cells as shown in Figure 3-24, only natural convection via side walls of the cells and surfaces of the busbars
was taken into account. The temperatures of the busbars are the same as the cell temperature. It was decided not to separate $R_{load}$ and $R_c$, because in real conditions the cells must still be connected with the load by cables. The heat flow in battery package is shown in Figure 3-25. The heat is dissipated via natural convection from the busbars and cells. Joules heat is generated in busbars, cables and metallic connection between cells and busbars. Each cell generates the same amount of heat $Q_{cell}$.

The symmetry of a battery package is taken into account and therefore only half of the battery package is shown.

<figure><img src="image_31.png" /><figcaption>Figure 3-25: Heat flow in a battery package</figcaption></figure>

To calculate the $Q_{bus}$ in the symmetry of the busbar (Figure 3-25) was taken into account as shown in equation (3-25).

$$Q_{bus} = 2 \int_{0}^{t_{off}} \left( \begin{aligned} & (2i_{cell}(t))^2 0.5R_{bus} + (4i_{cell}(t))^2 0.375R_{bus} \\ & + (6i_{cell}(t))^2 0.25R_{bus} + (8i_{cell}(t))^2 0.125R_{bus} \end{aligned} \right) dt \quad (3-25)$$

where $t_{off}$ is the time of discharge process of the battery package.

<figure><img src="image_32.png" /><figcaption>Figure 3-26: Symmetry of the busbar</figcaption></figure>

Equation (3-26) can be used to determine the sum of energy $E_L$ and $Q_{cab}$.

$$E_L = \int_{0}^{t_{off}} R_{load} \cdot i_{BP}^2(t) dt \quad (3-26)$$