For the model calculation the temperature gradient between the cells and the ambient as well as between the busbars and the ambient were taken as presented in Figure 3-27.

<figure><img src="image_33.png" /><figcaption>Figure 3-27: Temperature gradient in battery pack with Al busbar a), Cu busbar b)</figcaption></figure>

Based on (3-25), (3-36) and characteristic shown in Figure 3-27 the energy balance for battery package was calculated. The obtained results are presented in Table 3-4.

<table><caption>Table 3-4: Energy balance calculation for the battery pack</caption><thead><tr><th>Energy type</th><th>Cu Busbar</th><th>Al Busbar</th></tr></thead><tbody><tr><td>16&middot;Q<sub>Acell</sub> [kJ]</td><td>23.6</td><td>25.1</td></tr><tr><td>Q<sub>Abus</sub> [kJ]</td><td>0.6</td><td>1.6</td></tr><tr><td>16&middot;Q<sub>Lcell</sub> [kJ]</td><td>17.5</td><td>20.5</td></tr><tr><td>Q<sub>Lbus</sub> [kJ]</td><td>4.3</td><td>6.5</td></tr><tr><td>Q<sub>GEN</sub> [kJ]</td><td>46.0</td><td>53.7</td></tr></tbody></table>

Based on the model calculation it can be stated that in the battery package with Cu busbar 46 kJ of heat will be generated, while in the battery package with Al busbar $Q_{GEN} = 53.7$ kJ. The battery package with the Cu busbar will accumulate almost 53% of this heat and the rest will be dissipated. In the battery package with Al busbar around 50% of $Q_{GEN}$ will be accumulated. The different values of $Q_{GEN}$ result from lower resistance of copper busbars.

### 3.3.2.3 Electro-thermal model validation

#### Measurement setup and procedure

The proposed electro-thermal model was validated with the real measurements. For the evaluation of the discharging parameters of Li-Ion cells in battery modules, the SONY-US18650G3 cylindrical cells with nominal capacity of $C = 3120$ mAh, rated voltage $U_{ocv} = 4.20$ V, rated internal resistance $R_{wc} = 13$ mOhm and average mass of $M_c = 46.6$ g were used. A module of 16 batteries connected in parallel (16P1S) was adopted for the tests. The cells were placed in a 2x8 package with an air gap of 2 mm. Thus the heat exchange conditions with the surroundings are similar for all cells. The configuration dedicated for the tests has been equipped with 4 connection terminals as shown in Figure 3-28.
<figure><img src="image_34.png" /><figcaption>Figure 3-28: The electrical circuit of the battery module with the parasitic resistances in steady-state operation conditions</figcaption></figure>

In Figure 3-28 the cell potentials have been marked as V1-V16, internal cell resistances as $R_{wc}$, resistances of welded cell-busbar connections as $R1 \div R32$ and resistance of busbars $R_{bus}$ measured as terminal-to-terminal (T1-T2, T3-T4) connection.

The electrical connections of the cells in the 16S1P system were made using hybrid busbars made of copper and aluminium, respectively, equipped with nickel pads dedicated to welded connections as shown in Figure 3-29. Table 3-5 lists the busbar parameters.

<figure><img src="image_35.png" /><figcaption>Figure 3-29: Front view of the Cu busbar with 16 welding spots</figcaption></figure>

All cells used to make the module were selected so the electrical parameters $R_{wc}$ and $X_{wc}$ were from a range $1.02 \text{ m}\Omega < R_{wc} < 1.12 \text{ m}\Omega$, $0.45 \text{ m}\Omega < X_{wc} < 0.55 \text{ m}\Omega$. Before the tests, the cells in the battery package were charged to a voltage of 4.20 V with the current 3.0 A (constant current/constant voltage – CC/CV) at room temperature 23°C and they were discharged in the same way till the voltage 2.60 V with the Chroma BTS 17011. Charge and discharge procedures were repeated three times in order to prepare the cells for the tests.
<table><caption>Table 3-5: Parameters of busbars applied for the tests</caption><thead><tr><td>Parameter</td><td>Copper Busbar</td><td>Aluminium Busbar</td></tr></thead><tbody><tr><td>Thickness [mm]</td><td>0.50</td><td>1.50</td></tr><tr><td>Width [mm]</td><td>32.25</td><td>32.15</td></tr><tr><td>Length [mm]</td><td>150.50</td><td>158.50</td></tr><tr><td>Resistance Terminal-to-Terminal [mΩ]</td><td>0.492</td><td>0.677</td></tr><tr><td>Number of welding spots</td><td>16</td><td>16</td></tr><tr><td>Total mass of busbar[ kg ]</td><td>0.028</td><td>0.027</td></tr></tbody></table>

Dissipation parameters and thermal dependencies were determined during the discharging of the battery package. Cell discharging was carried out with the measurement system based on Keysight 34972A multi-channel scanning multimeter with 34901A scanning module. The temperature profiles were measured during the discharge process with the discharge current 2C-3C. The load resistance has been selected to prevent the load current exceeding 3C value and in the absence of forced cooling system and to keep the temperature of the battery module below 65°C. During the discharge tests, temperature measurements of the cells and the busbars were carried out. In Figure 3-30 the points of temperature measurement has been presented.

<figure><img src="image_36.png" /><figcaption>Figure 3-30: Distribution of temperature measurement points on the battery package</figcaption></figure>

The distribution of the measurement points resulted from the symmetry of the packet relative to X and Y axis. The ambient temperature T0, the temperature of the T1, T2 and T3 cells and the temperature of the T5, T6 and T7 busbar were recorded simultaneously with the measurements of electrical parameters. For temperature measurement, Keysight K-type thermocouples and the Keysight 34972A multi-channel scanning multimeter with 34901A scanning module were used.

Discharge tests were carried out in an explosion proof chamber with the 0.25m³ volume without air exchange and forced cooling. Discharge test procedures were performed with battery charge monitoring up to the voltage $U_{ocv}$ = 4.18 ± 1% load cut-off system at $V_{cutoff}$ = 2.60V ± 1%. Temperature measurements at T0 ÷ T7 points, $V_{BP}$ voltage and $I_{BP}$ current were carried out synchronously with the frequency of 2 samples per minute until $V > V_{cutoff}$.

### 3.3.2.4 Results and discussion

Temperature as a function of time of the discharge process is shown in Figure 3-31. For the battery package with Al busbar (Figure 3-31a) during the initial phase of discharge busbar near the terminal was getting hot the fastest. It was caused by the highest current density in this place. At the end of the discharge the temperature of the cells was around 5°C bigger than for the busbars and it can be explained as better heat dissipation from the busbars. The Al busbar can be treated as thermal bridge for the cells and therefore the temperature inside the battery package is almost uniform.
At the beginning of the discharge of the battery pack with Cu busbar temperature measured in point T1 also raised quicker. However lower resistance of Cu busbar in comparison of Al busbar caused that finally busbar had lower maximal temperature during the discharge, as shown in Figure 3-31b. It is also the reason why the cells in battery pack had lower temperature.

<figure><img src="image_37.png" /><figcaption>Figure 3-31: Characteristics T(t) measured for the battery pack with Al busbar a), with Cu busbar b)</figcaption></figure>

Table 3-6 shows the measured energy balance for both types of the battery packs. In both cases they had the same initial value of $E_{tot}$. The battery package with Cu busbar allowed 5.5kJ more of electric energy to be delivered to the load than the battery package with Al busbar. The $Q_{GEN}$ in the battery package with Cu busbar was more than 10% lower in comparison to $Q_{GEN}$ in the battery package with Al busbar.

<table><caption>Table 3-6: Balance of energy in battery system</caption><thead><tr><td>Type of busbar</td><td>E<sub>tot</sub> [kJ]</td><td>E<sub>l</sub> [kJ]</td><td>Q<sub>GEN</sub> [kJ]</td></tr></thead><tbody><tr><td>Copper</td><td>647.0</td><td>598.4</td><td>48.6</td></tr><tr><td>Aluminium</td><td>647.0</td><td>592.9</td><td>54.1</td></tr></tbody></table>

The measurement validates the developed model of a battery package. The difference between the calculated and the measured $Q_{GEN}$ is less than 6% for the battery package with Cu busbar and 1% for the battery package with Al busbar. The small difference between the calculated and measured values of $Q_{GEN}$ is caused by the inaccurate assumption. The theoretical values of the coefficient taken for model calculation can be slightly different than realistic values. As shown in Figure 3-31 the temperatures of the cells and the busbars are not the same and that caused different heat dissipation from cells and busbars.

<table><caption>Table 3-7: Balance of energy in battery system</caption><thead><tr><td>Type of busbar</td><td>Q<sub>GEN</sub> [kJ] from exp.</td><td>Q<sub>GEN</sub> [kJ] from model</td></tr></thead><tbody><tr><td>Copper</td><td>48.6</td><td>46.0</td></tr><tr><td>Aluminium</td><td>54.1</td><td>53.7</td></tr></tbody></table>
Conclusions

The cylindrical batteries allow to build a very wide range of different battery package. Such battery package can be further connected in a modular systems e.g. for the big energy storage systems. The investigation of a heat generation in modular system is very important. 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 18650 type 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 the Cu busbar it is possible to reduce $Q_{GEN}$ by 10%.
# Chapter 4 Extension of the vehicle energy systems: the vehicles as grid related systems to the energy grids for optimal energy services

The recharging of the vehicles and their connection to the energy grid became an important question. This chapter considers the recharging ecosystem and the design of the electric vehicles grid related energy systems for optimal mobility service. The idea is to research recharging strategies that extend the autonomy of the electric vehicles. The electric vehicles can be used 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, optimize the number of chargers, and so reduce the investment costs that are needed. The electric vehicles are considered as vehicles 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. The recharging operation at home is a frequent operation. An innovative robotized solution for recharging is investigated for the recharging task at home. This innovation is called the “robot charger”. The household is considered as the cluster for energy integration where energy services for mobility and household needs (electricity providing 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 environmental multi-objective optimization is used for the energy services integration. The results show a large-scale reduction of emissions and efficiency improvement of the energy integrated system in comparison of the conventional system, where energy for mobility and household are provided on a separate way.

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

In this research, the energy system is extended and includes in its boundaries the energy storage and conversion of vehicles and their connections to the energy 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 systems are classified according to their power. Figure 4-1 defines the charging systems as slow and fast charging systems. The power determines the time to charging of the battery. The time and the occupation rates of the chargers are as well evaluated. The optimality is researched for long trip drives on electric mode. The results illustrate a method to rethink the usage and the energy management on the vehicle during the driving. As well it is researched how to adapt the recharging capacities and their location on the grid, by using a holistic approach. The target is to propose a high quality service for the electric mobility for long way drives.
<figure><img src="image_38.png" /><figcaption>Figure 4-1: Definition of the recharging systems</figcaption></figure>

The novelty of the present study is the modelling and the optimization of the design and the operation parameters of the extended vehicle energy system. This system includes the vehicles and their interactions with the electricity grid. The optimization manages the minimization of the vehicles waiting time on the charging stations. On that way the relation to the energy grid, is considered, as well. Methods, techniques to analyse, improvement and optimizations of energy systems have to deal with not only the energy consumption and economics of the electric vehicles, but also with the availability and the distribution of the charging infrastructure. The target is to propose strategies to manage the recharging plans of the electric vehicles for long way trips.

### 4.1.1 Minimization of the waiting time

The model of the recharging is described in Appendix B.

#### Problem definition

The vehicle is considered as a part of the electric grid, when is it connect to charge its high voltage battery. For an optimal operation, the battery state of charge is considered variable between 30% and 80% of the total battery capacity. In some cases the vehicle should fulfill the entire battery, in some other cases a given percentage of the battery. The problem of the energy demand and supply of the electric vehicles on the highway has to be modelled and optimized. Figure 4-2 introduces the graphical representation of the highway and the recharging infrastructure.

<figure><img src="image_39.png" /><figcaption>Figure 4-2: Highway with charging stations installations</figcaption></figure>

The charging stations characteristics located on the road are given in Table 4-1:

<table><caption>Table 4-1: Initial charging stations characteristics</caption><thead><tr><td>Localization of the station (km)</td><td>Power output (kW)</td><td>Number of chargers</td></tr></thead><tbody><tr><td>60</td><td>50</td><td>1</td></tr><tr><td>150</td><td>50</td><td>1</td></tr><tr><td>200</td><td>125</td><td>1</td></tr><tr><td>334</td><td>50</td><td>1</td></tr><tr><td>408</td><td>125</td><td>1</td></tr><tr><td>501</td><td>50</td><td>1</td></tr></tbody></table>
The charging stations are located on different spots and the distance between them is not equivalent. The electric vehicles have different battery capacities stored on board (100 kWh, 60 kWh and 41 kWh). These capacities correspond to future electric vehicles deployed on the markets. The recharge of the vehicles on the highway is considered to occur between 15% and 80 % of the State of charge (SoC) of the high voltage battery. The battery can be fully recharged in the home installation, which is with charging powers lower than 50 kW. The vehicles have to quit the highway with at least 20% of the state of charge (SoC). To recover this condition of minimum SoC when leaving the highway, the vehicles have to stop on the closest recharging station.

The flows of the electric vehicles entering on the highway are characterized with the following types, described in Table 4-2.

We assumed in this research that the vehicles speed and energy consumption are constant during the drive. Those assumptions are not too strong, as the related approximations (few minutes) are negligible compared to waiting times (around half an hour).

Even if batteries charging power are given as constants, these powers depend on many factors, such as the battery temperature, state of charge and state of health, but also availability of the grid power. The charging profile can then be very complex and not reproducible, but some characteristics remain:

* Charging power is almost constant and equal to the max until the SoC reaches 80%

* It is easier to charge a battery when its SoC is low

This charging power model is applicable for SoC between 0 to 80%. For all cars, the charging power, when the SoC is 0%, is equal to the maximum charging power. It then slowly and linearly decreases when the SoC increases. The slope depends on the vehicle. We will also assume that users will recharges only up to 80% of SoC.

<table><caption>Table 4-2: Vehicles types for the flows constitution</caption><thead><tr><th>Parameters</th><th>Vehicle type 1</th><th>Vehicle type 2</th><th>Vehicle type 3</th></tr></thead><tbody><tr><td>Battery capacity [kWh]</td><td>100</td><td>60</td><td>41</td></tr><tr><td>Minimal SoC [-]</td><td>15</td><td>15</td><td>15</td></tr><tr><td>Maximal SoC [-]</td><td>80</td><td>80</td><td>80</td></tr><tr><td>Power of charge</td><td>125</td><td>350</td><td>44</td></tr><tr><td>Electric consumption [kWh/100km]</td><td>18</td><td>15</td><td>15</td></tr><tr><td>Max speed [km/h]</td><td>130</td><td>130</td><td>110</td></tr><tr><td>Final SoC required (Quitting the highway) [-]</td><td>20</td><td>20</td><td>20</td></tr></tbody></table>

The decision variables for the powertrain design are defined in Table 4-3:

<table><caption>Table 4-3: Decision variables for powertrain design</caption><thead><tr><th>Decision variables for design</th><th>Range</th></tr></thead><tbody><tr><td>Number of charging stations [-]</td><td>[1-6]</td></tr><tr><td>Number of chargers per charging station [kW]</td><td>[1-150]</td></tr><tr><td>Charged Power [kW]</td><td>[0-350]</td></tr><tr><td>SoC per vehicle [%]</td><td>[20-80]</td></tr></tbody></table>

### 4.1.2 Results of the optimization

The optimization of the waiting time is performed for a flow of 10 electric vehicles, driving on the highway (4-1):

$$ \min(\text{Time}_{\text{waiting}}(x)), x \in X_{\text{decision\_variables}}, 10 \leq \text{Vehicles} \quad (4-1) $$
The flow is composed of 10 vehicles. The random function of Matlab is used to distribute the vehicles, their battery capacity and state of charge, the numbers of in-/out-comes from the highway and the time of their entrance. The random generated traffic configuration is summarized in Table 4-4. The highway disposes with 6 chargers, disposed according to Figure 4-2.

<table><caption>Table 4-4: Electric vehicles flow on the highway – 10 vehicles</caption><thead><tr><td>Vehicles number</td><td>Battery capacity</td><td>Number of the Incomes [-]</td><td>Number of the Outcomes [-]</td><td>Initial SoC (entering to the highway)</td><td>Time of entering (h)</td></tr></thead><tbody><tr><td>1</td><td>100</td><td>7</td><td>10</td><td>70</td><td>6,49</td></tr><tr><td>2</td><td>60</td><td>1</td><td>10</td><td>96</td><td>7,50</td></tr><tr><td>3</td><td>41</td><td>3</td><td>10</td><td>90</td><td>8,15</td></tr><tr><td>4</td><td>41</td><td>2</td><td>9</td><td>100</td><td>8,62</td></tr><tr><td>5</td><td>60</td><td>1</td><td>3</td><td>82</td><td>9,10</td></tr><tr><td>6</td><td>100</td><td>3</td><td>4</td><td>58</td><td>10,19</td></tr><tr><td>7</td><td>41</td><td>1</td><td>5</td><td>58</td><td>11,19</td></tr><tr><td>8</td><td>41</td><td>2</td><td>10</td><td>80</td><td>11,23</td></tr><tr><td>9</td><td>60</td><td>5</td><td>7</td><td>96</td><td>11,75</td></tr><tr><td>10</td><td>100</td><td>1</td><td>10</td><td>64</td><td>11,90</td></tr></tbody></table>

The results of the minimization of the waiting times of the vehicles are given in Figure 4-3. For this configuration of the traffic with 10 vehicles and 6 chargers on the road, the vehicles do not need to wait to recharge their batteries. The available charging infrastructure is enough to cover the recharging needs. Figure 4-3a represents the evolution of the State of charge (SoC) of each vehicle as a function of the time on the highway. When the vehicles are traveling, the SoC decreases. The charging periods of the highway stay are marked by the increasing of the SoC of the vehicles. The charging periods have different time durations, and deferent slopes. That is directly related to the battery capacity (Table 4-2) and the power of the charger (Table 4-1). There are different cases of trips. The trips represented on the Figure 4-3a are all optimal. They present minimal waiting time, minimal number of stops and minimal charged energy from the grid. According to the trip that they have to do, and their initial SoC, some vehicles enter, drive and quit the highway without any stops. This is the case of vehicle 1 and vehicle 9. The slope of discharge of their batteries is deferent, because the different capacity of the batteries on board. Both of the vehicles drive at 130 km/h of speed. The vehicle 1 has 70 kWh of energy in the battery when enters on the highway and quits it with 40 kWh. There is no need to stop and recharge the batteries on the highway. The vehicle 9 enters with 57 kWh of energy in the battery and quits it with 24 kWh of battery, which represents 40% of the battery capacity. Thus, in the case of vehicle 9, there is no need as well to stop and recharge on the highway. The vehicles have to stop in average 2 or 3 times to recharge, according to their battery capacity and the long distance trip that they have to perform. Figure 4-3a shows as well that the random flow of 10 vehicles stays on the highway for a total time of 11 hours. From illustrations on the Figure 4-3b one can see that none of the vehicles wait for an available charger. They arrive and can start to recharge without having queue length. Figure 4-3c shows that the queue length is zero for all the chargers on the highway.

The time spent on the highway for each vehicle depends on the trip time, the waiting time to access the charger and the recharging time. Figure 4-3d illustrates the ratio between the stops time to the trip time for every vehicles. The ratio is defined by (4-2):

$$r = \frac{Time_{waiting} + Time_{charging}}{Time_{trip}} [-] \quad (4-2)$$
<figure><img src="image_40.png" /><figcaption>Figure 4-3: Results from the optimization: flow of 10 vehicles, minimization of the waiting time</figcaption></figure>

Figure 4-3d shows that four vehicles are characterized by zero waiting and charging time. One vehicle waits 10% of its trip time to recharge. Other two vehicles are waiting 20% of their trip time for recharging. The maximum of the ratio is reached for one vehicle that spends 25% of its trip time to recharge. This vehicle has battery capacity of 40 kWh and stays for 500 km on the highway and has to stop for recharge at least 3 times.

In order to test the performances of the optimization method the number of the vehicles in the highway flow is increased to 30. Optimization of the waiting time is performed for a flow of 30 electric vehicles, driving on the highway (4-3):

$$ \min(\text{Time}_{\text{waiting}}(x)), x \in X_{\text{decision\_variables}}, 30 \text{ Vehicles} \qquad (4-3) $$

The flow is composed of 30 vehicles. The random function of Matlab is used to distribute the vehicles, their battery capacity and state of charge, the numbers of in-/out-comes from the highway and the time of their entrance. The random generated traffic configuration is applied. The highway disposes with 6 chargers, disposed according to the Figure 4-1. The results of the optimization converge on the results, illustrated in Figure 4-4. The vehicles stay 12 hours on the highway. Their batteries are recharged between 20% and 80% of SoC on the highway, with the high power chargers. Some of the vehicles enter on the highway with fully recharged batteries, for that reason their SoC is superior to 80%. In the case of a flow of 30 vehicles and 6 chargers, some of the vehicles have to wait for available chargers when they stop to the charging infrastructure. The waiting times are illustrated with the flat curves in Figure 4-4a. For better visibility of the optimization results, the descriptive statistics are used in the Figure 4-4b. The box plot diagrams of the vehicles waiting times are used for every charger station. The box plot diagram in descriptive statistic is a standardized way of displaying the distribution of data based on the five-number summary: minimum, first quartile, median, third quartile, and maximum. In the simplest box plot the central rectangle spans the first quartile to the third quartile (the interquartile range or IQR). A segment inside the rectangle shows the median and "whiskers" above and below the box show the locations of the minimum and maximum (Station 1 and Station 4, (Figure 4-4 b)). When the "whiskers" are absent means that the interval of data is important and the minimum and maximum are plotted with markers (Station 2, Station 3 and Station 6). From Figure 4-4 b one can see the maximal waiting times for every station. For example for the maximal waiting time on the charger on the station 1 is 0,62 h, the maximal waiting time on the charger on the station 2 is 0,9 h (which also the maximal time for the entire flow) and 0,62 h for the charger on Station 4.
<figure><img src="image_41.png" /><figcaption>Figure 4-4: Results from the optimization: flow of 30 vehicles, minimization of the waiting time</figcaption></figure>

The repartition of the maximal waiting time can be explained by the fact that the chargers on Station 1, Station 2 and Station 4 had the lowest power of 50 kW. They are as well located in the beginning and the middle of the highway, and the number of vehicles is important on that section. The charger 6 has as well the same power of 50 kW but is located at the end of the highway, so the vehicle flow is not important to cause important queue of vehicles. The chargers on Station 3 and 5 have the power of 125 kW and allow charging the batteries on a rapid way.

The queue length as a function of the time is presented in Figure 4-4c. One can see that the maximal queue length is composed of 2 vehicles. The waiting times on the chargers are different because the different vehicles have different capacities of the batteries.

Figure 4-4d illustrates the time repartition from the vehicles point of view. The ratio of the waiting time to the trip time and the ratio of the waiting and charging time to the trip time is illustrated per number of vehicles. One can notice that the maximal ratio of waiting time to the trip time is 0,2. The largest number of vehicles 18, never stops to recharge. The rest of the vehicles waiting are one or two, according as well to the representation in Figure 4-4d. The histograms represented in blue include as well the recharging time to the waiting time. The maximal ratio is noted to 0,4. This means that two vehicles spend 40% of their trip time to wait and recharge. The rest of the values are divided between 0 and 0,4, with maximal number of vehicles 7, sending 15% of their trip time to wait on the queue and recharge their batteries.

## Conclusions

The discussed study cases of the vehicles flows (10 and 30 vehicles), illustrates the method and the simulation model, proposed to investigate the long-range mobility with electric vehicles. The results of the study cases show on a didactic way the impact of the vehicles flow density and the consideration of the model of the number of vehicles, the queue lengths to access the chargers on the stations and the time to recharge the batteries of the vehicles. The method allows identifying the frequency of recharging and the usage of the chargers. This analysis is useful to reduce the waiting times and recharging times of the vehicles. The placement of additional chargers can be tested. The investment cost of the charging stations can be considered in the decision criteria. The localization of the chargers has to be minimal from investment cost point of view. Techno-economic multi-objective optimization can be introduced to show the trade-off between the optimal number of chargers that have to be introduced to reduce the waiting times, and the total investment cost of the infrastructure.
## 4.2 Coordination interest for the optimization of electric vehicles long distance trips

This research presents a method to coordinate electric vehicles recharging during long distance trips. It relies on communications between vehicles and infrastructures. The communications are called V2X. The objective is to enhance the use of the infrastructure by improving the distribution of vehicles between the different charging stations. The objective is to reduce the waiting time. The performance of this system is compared to an uncoordinated situation and a situation with optimal vehicle distribution. A case study on a 550 km highway is conducted. The results show a 10% reduction in the time spent in stations thanks to the communication. This situation is tested for a heavy traffic.

In this research, we are motivated by the means that must be implemented in order to coordinate the choices of EVs quick charge, especially during long distance trips. By allowing electric vehicle drivers to avoid queues, it shows great potential timesaving for users and better use of the charging infrastructure, limiting consequently the necessary investments. However, these means must be designed in such a way that they can be easily implemented.

In the following sections, the deployed method is firstly presented including the model and the assumptions. The algorithm used for the optimal solution research is detailed. Thereafter the proposed communication scenarios are described, with its structure and its calibration. The model is applied to a simulation of the vehicles flow on a 550 kilometers highway.

### 4.2.1 Minimization of the waiting time

$$ T_{Trip,n} = T_{Driving,n} + \sum_{j=1}^{N} T_{Station,j,n} \qquad (4-4) $$

$$ T_{Station,j,n} = T_{Wait,j,n} + T_{Charge,j,n} + T_{Other,j,n} $$

Where:

$T_{Wait,j,n}$ = Waiting time for an available charging point, when the station is full (in minutes)

$T_{Charge,j,n}$ = Time required to store the intended amount of energy (in minutes)

$T_{Other,j,n}$ = Constant representing time needed for all other operation: decelerating, accessing the station, launching the charging session (here set to 5 min)

We sum up the optimization definition by:

$$ \begin{array}{ll} \min_{x} & f(x, y) \\ \text{u.c.} & \forall t, \forall n, \text{SoC}_{\text{min},n} \le \text{SoC}_n(t) \\ & \forall n, \text{SoC}_n \le \text{SoC}_{\text{max},n} \text{ after recharge} \\ & \forall n, \text{SoC}_{\text{exit},n} \le \text{SoC}_{\text{exit required},n} \end{array} \qquad (4-5) $$

We want to minimize $f$ as a function of the decision variables $x$, as defined in (4-5) under constraints of State of Charge (SoC). Variables $x$ are the scheduled energy stored ($E_{n,j}$)$_{\forall n,j}$. $SoC_n(t)$, $SoC_{min,n}$ and $SoC_{max,n}$ respectively represents state of charge of vehicle $n$ at time $t$, minimum and maximum. $SoC_{exit,n}$ and $SoC_{exit required,n}$ represent respectively the SoC at highway exit obtained and required. The variables $x$ of this function are continuous.
For the optimum to be reached, it is necessary to have, for each electric vehicle:

$$ \sum_{i}^{N} E_{n,i} = E_{\text{Required},n} \qquad (4-6) $$

In general, it is preferable to minimize the number of stops, it saves $T_{other}$. However, it may sometimes be useful for an EV to make an additional stop to avoid a queue.

### 4.2.2 Electric vehicles communication scenarios

#### Communication issues

The quality of a communication system depends not only on its performance but also on its ability to be easily deployed. Figure 4-5 presents all the constraints that are considered.

<figure><img src="image_42.png" /><figcaption>Figure 4-5: Overview of possible communication flow and related constraints</figcaption></figure>

Firstly, such a system relies on wireless connections between vehicles, charging stations and a central server. This communication is called V2X. These connections can be based on mobile network or WLAN. The first technology operate for long distances and the second one for local communication (less than few kilometers). The risk of dead zones and the latency of these communications make us eliminate solutions requiring continuous or rapid exchanges.

A centralized server gathering the requests of all users could make it possible to find optimal solutions. However, its implementation, reliability and required computing power can be significant hurdle, especially if the system deployment achieves a large scale. A distributed system, without a centralized server, eliminates this problem.

Users can be also reluctant to share their personal data, such as destination, average speed or state of the vehicle. Finally, constraints of robustness and cohabitation must also be considered.

Figure 4-6 displays the communication flow chart.
## Proposed communication schema

Considering all constraints mentioned in the previous paragraph, Figure 4-5 describes the innovative communication schema that is proposed. Communications between EVs and charging stations use mobile networks. This process, from the vehicle point of view, follows five steps:

1. The electric vehicle (EV) calculates several possible combinations of stops, with different stations and amounts of energy to store that permit to reach it destination. Local CPU resources do this computation.

2. The electric vehicle (EV) requests the scheduled waiting time at charging stations at a specific arrival time, for all combinations.

3. The EV sends a notification to selected stations, containing arrival time and estimated charging time.

4. The vehicle starts again the cycle composed of the steps 2 and 3, every period T.

The vehicle will first do this cycle when starting the trip, before entering the highway. It will then refresh according to a period T. It is noted $T_{adv,n}$ the time between the beginning of the trip and the moment when the vehicle enters the highway. Stations will compute expected waiting times using notifications sent by all the vehicles.

From the station point of view, the system is composed of a device that must receive request of waiting time estimation and notification of recharge, and compute scheduled waiting times.

## Used algorithms

Different algorithms are used in this system. First, on the vehicle side, it is used the previous differential evolution algorithm to find a recharging combination that minimizes travel time. Other recharging combinations are evaluated. The combinations to be evaluated that have the same number of stops and the same amount of charged energy as the previous solution are chosen. These potential solutions are selected if they result in an increase in total travel time of less than 10%. Then on the station side, the calculation simply consists in keeping an updated list of scheduled recharges. This list contains arriving times and estimated charging times. The station can then evaluate the queue throughout the day and deduce waiting times.

The proposed system is easily feasible. First, the required computing power is not a problem. On the vehicle side, the complexity of the calculation is similar to the navigation technologies. It is therefore possible to use the same computer resources as those of the navigation system. On the station side, the amount of calculation to be performed depends on the number of vehicles on the road. However, as these calculations are not very complex, they can be performed by an affordable computer system. Secondly, it is a distributed system. This allows it to be deployed over large territories, such as a country or Europe, and to many vehicles, without the risk of saturating a central server. Finally, this solution does not require the share of personal data.

### 4.2.3 Results and cases studies

#### Case study

The generated data represents the situation of a highway during a day with 100 electric vehicles. The road has one way, 11 entrances and exits and is 553 km long. It includes six charging stations, each with three charging points of 50, 100 or 125 kW maximum available power. Figure 4-1 displays their implantation. There are three types of vehicles in this simulation: urban, sedan and luxury.

Table 4-5 gives their respective characteristics.
The model is based on daily highway flow modelling on the data found in [132].

Figure 4-7 shows the average daily flow of vehicles entering the French A6 motorway in the Ile de France region during the year 2017. These are the data of highway entrances equipped with counting loops. It indicates that the flow is almost constant between 8 a.m. to 9 p.m. It is to be noticed that the traffic during the night is more than three times lower than during the day.
As we want to study crowded situations, we will focus on the situation during the day. Therefore, the flow is modeled with a uniform distribution of departure time over a period, between 8 a.m. to 9 p.m. Randomly entry and exits number of vehicles are chosen, while imposing a minimum distance of 250 km. It is also used a uniform distribution for the SoC at departure, in the range 50%-100%. SoC at arrival (when the car exits the highway) is required to be above 30%, and SoC when entering a charging station to be above 15%. Electric vehicles are charged to a maximum of 80% or below.

<table><caption>Table 4-5: Studied vehicles characteristics</caption><thead><tr><th>Car Type</th><th>Urban</th><th>Sedan</th><th>Luxury</th></tr></thead><tbody><tr><td>Battery (kW.h)</td><td>50</td><td>60</td><td>100</td></tr><tr><td>Maximum charging power (kW)</td><td>50</td><td>100</td><td>125</td></tr><tr><td>Consumption (kW.h/km)</td><td>0.15</td><td>0.18</td><td>0.18</td></tr><tr><td>Driving Speed (km/h)</td><td>110</td><td>130</td><td>130</td></tr><tr><td>Generation probability</td><td>0.3</td><td>0.6</td><td>0.1</td></tr></tbody></table>

<figure><img src="image_43.png" /><figcaption>Figure 4-6: Communication flow chart</figcaption></figure>
Trips characteristics

<figure><img src="image_44.png" /><figcaption>Figure 4-7: Average daily incoming vehicle flow</figcaption></figure>

The following figures present the characteristics of the EV trips generated by the assumptions presented in the previous paragraph. In order to illustrate only the driver's needs, we do not consider waiting times at the stations in this section. Figure 4-8 shows two histograms: the distribution of trip distances and trip times, considering recharge times. It shows the diversity of the routes studied and the fact that they are mainly between 400 and 550 km.

<figure><img src="image_45.png" /><figcaption>Figure 4-8: Generated vehicles trip characteristics</figcaption></figure>

Figure 4-8 shows the histogram of the charging time over total trip time ratios for the different vehicles studied, according to their type. It also shows the impact of vehicle characteristics on their ability to make long journeys. It is interesting to note that the size of the battery does not affect the previous formula. However, it comes into play when the charge level is different between entrance and exit, which explains the dispersion of EVs in the histogram: those starting with a full battery will have less need to charge during the trip.
## First study case

We performed these first simulations with 100 vehicles during one day. Three situations are compared:

* Without coordination: EV drivers have no information about choices made by other EVs. Each driver optimizes his charging schedules separately, reproducing the choices that a driver can make when only knowing the charging stations position and power.

* Communication: Strategy defined in the previous section, in the case where all vehicles use the system. We set the time period T and $T_{adv}$ at 10 minutes.

* Global optimization: Situation in which all information is centralized and choices are made in an optimal way, considering waiting times when making choices. This vehicle repartition is close to the optimal one.

Figure 4-9 shows the average waiting time at the different stations for the three situations. The communication and even more the global optimization can reduce average waiting times. This is made possible by a better distribution of vehicles between stations. It is to be noted that the decrease in waiting time at station 3, the busiest station, does not result in an increase in waiting time at the other stations. The charging infrastructure is therefore better used.

<figure><img src="image_46.png" /><figcaption>Figure 4-9: Average waiting time comparison</figcaption></figure>

## Conclusions

The simulations were performed with 100 vehicles a day and averaged them over 15 days. In order to qualify a situation as satisfactory for users, a quality criterion was defined. The infrastructure is considered as sufficient if it meets the following quality criterion:

* 90% of EVs entering a charging station will have less than 5 minutes to wait.

* All waiting times are less than half an hour.

The following figures show the results of the comparison between the three scenarios. Figure 4-10 shows the cumulative distributions function of waiting times at the different stations for the three scenarios. The dotted line represents the quality criterion defined above. The communication and even more the global optimization can reduce waiting times for all drivers entering charging stations. Thus, for station 3, the busiest, there is a decrease in the maximum waiting time from 74 to 40 and then 20 minutes (respectively for the three scenarios) and an increase in the proportion of refills for which the waiting time is zero from 41% to 60% and then 81%. It can be also noticed the balancing of waiting times between stations.

Table 4-6 shows the average waiting and charging times for the three scenarios. There is a slight increase in recharging time and a decrease in waiting times. This shows the choices made by some people to recharge at lower power to reduce their waiting time. This represents a total saving of around 9% of the total time spent in stations thanks to communication, out of a potential decrease of 13% with overall optimization.
<figure><img src="image_47.png" /><figcaption>Figure 4-10: Waiting times at charging station, 3 charging points per station</figcaption></figure>

<table><caption>Table 4-6: Average charging and waiting time for the three scenarios</caption><thead><tr><th></th><th>No coordination</th><th>Communication</th><th>Global optimization</th></tr></thead><tbody><tr><th>Average charging time (min)</th><td>46.0</td><td>47.3</td><td>47.7</td></tr><tr><th>Average waiting time (min)</th><td>15.6</td><td>8.8</td><td>5.8</td></tr><tr><th>Total (min)</th><td>61.6</td><td>56.1</td><td>53.5</td></tr><tr><th>Saving compared to No C.</th><td>-</td><td>9%</td><td>13%</td></tr></tbody></table>

Figure 4-11 shows the same comparison in a more crowded situation. There are the same number of cars per day, but only two charging points per station. The decrease in the area above the curves is even more pronounced than in the previous situation, particularly for charging station number 3. The time spent in stations is then reduced by 46% and 55% respectively with communication and overall optimization, compared to the uncoordinated scenario.

In conclusion, we can note two things about the performance of the proposed communication solution: the good distribution of vehicles, reducing the time spent in stations to a level close to the ideal distribution, as well as the increased efficiency in the event of high traffic.

<figure><img src="image_48.png" /><figcaption>Figure 4-11: Waiting times at charging station, 2 charging points</figcaption></figure>
A comparison of its performance against an uncoordinated situation and one with overall optimization shows a 9% gain in time spent in stations out of a potential of 13%. In addition, the system is ever more useful in crowded situations, with a time gain of 46% out of a potential of 55%. In the future, it will be interesting to study the robustness of such a system when not all travelers use it.

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

This research presents an innovative recharging service for the high voltage batteries of the electrified vehicles, provided by a robot. An innovative mobile robot, equipped with energy power system, does the recharging task. The navigation function is central in the design of the mobile robot. The navigation system must create a safe path planning to the vehicle and then ensure an accurate positioning of the mobile robot and safe navigation in its environment. The Localization and Mapping are the fundamental keys to provide the mobile robot with autonomous capabilities. The research presents a recent development and its experimental validation, in the Simultaneous Localization and Mapping method to serve the robot navigation, coupled with an advanced cognitive system. The proposed navigation system uses advances in Mobile Robot Localization and Mapping methods and a LiDAR sensor. The cognition uses deterministic algorithms to fulfil the positioning accuracy, the smart cost of the equipment, and easy implementation requirements for industrialization. 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.

### 4.3.1 Mobile robot for recharging

#### 4.3.1.1 Why a mobile robot?

Currently, mobile robot applications go through a tremendous increase due to the miniaturization of computer devices, the progress in the information treatment and algorithms. Mobile robot can perform autonomous task and evolve in a dynamic and unknown environment, which make it flexible and performing. In our case, the robot presented in Figure 4-12 has to deal with different goals: it must be able to reach and serve several parking spaces. To complete its mission, the robot has to detect the car, localized the receiver underneath the car, create and follow a safe path to reach the coil, positioning itself precisely and then start the power transfer after having compensate the air gap. The most important ability of a mobile robot is the navigation.

<figure><img src="image_49.png" /><figcaption>Figure 4-12: Numerical model of the mobile robot</figcaption></figure>
The robot navigation includes three specific faculties [77]:

* Be able to perceive, integrate and evolve in the environment by avoiding obstacles

* Self-localization

* Be able to reach goal locations

To do so the navigation system of the mobile robot must include a robot positioning system, path planning and map building [77]. Four different steps allow the navigation of the robot inspired of the human behavior: Perception, Localization, Cognition and Motion.

The objective of the research is to make the functional definition of the navigation system of the mobile robot in a simple and reliable way, to ensure the proper functioning of the robot and the realization of its service. The presented literature review helped to define mobile robotics, to identify important concepts and scope on the relevant techniques and methods to apply for the “Robot Charger” case. The research done will be useful for several reasons:

* It introduces several algorithms of different type with relevant simulations and experiences in mobile robot navigation in localization and cognition. It gives open source modules that can be used in the Robot Charger system.

* Several challenges for the development emerged from this study:

* The Localization should rely more on the LiDAR than on Dead-reckoning sensors (Exploration of SLAM or alternative techniques).

* Parking places usually do not have special geometry that would be detected by the LiDAR. In addition, the environment change every time since the car will not park in the same way or, in the case of several parking spaces; there will be an irregular presence of cars.

This research contributes to design a cost effective localization platform respecting the requirements of the “Robot Charger” in terms of accuracy and detection of obstacles. This platform will be integrated into the Smart Cost Navigation System.

## 4.3.2 Research work

### 4.3.2.1 Design of a cost effective navigation system for a mobile robot

A multitude of Navigation system has been studied in the literature and each of them shows good results. Even though, it is an evidence that the quality of the result depends mainly on the mathematical tools combined with the computing power that is granted as well as the quality of the sensors used. In our case, we want a cost effective navigation system. The navigation system is required to work with low cost materials and sensors. Accordingly, one of our main constraints for the design is an available low computational power and sensors with enough good performance. Even though, the navigation system must be efficient enough to allow the robot to fulfil its tasks.

#### Perception with lidar and encoders

From the literature review, we know there are two types of positioning systems called relative and absolute. In addition, each positioning system can be realized using different sensor families and give specific properties. The combination of one of each type is necessary to get a good tracking performance. Indeed, relative positioning systems offer good accuracy in the short term, but tend to drift while absolute position system that refers to the environment can correct the drift. For the relative positioning system we use the robot's encoders, it gives the speed and the sense of rotation of each wheel. Combined with the dynamic models of the robot, the distance travelled and the direction can be computed. For the absolute positioning system, the choice of a "map matching" system comes from the fact that the environment of the robot is unknown and there will be no landmarks. Furthermore, this system works well with the method SLAM. The use of the camera or LIDAR to build this system is common, but even if the device is not expensive the disadvantages of the camera is the computing power needed to process the images. On the contrary, the LIDAR is a range finder sensor.
The process of the data is simpler. Furthermore, a high performance LIDAR is very affordable today. For the following, we use the low cost 360-degree laser range scanner RPLIDAR A2 M8. It can measure a distance up to 12 meters (accuracy~1%), which is fitting with the requirement in the precision and the size of the environment. Moreover, thanks to the 360° measurement, the robot will have a total vision of its environment and will be able to navigate safely in every direction.

## SLAM: Different approaches

After the perception comes the localization. The robot has to evolve in an unknown environment. Thus, the use of the method "Simultaneous Localization and Mapping" is necessary: the robot will be localized while it builds the map gradually. Several types of SLAM have already been developed using different combination of mathematical tools from the Iterative Closest Point to the particle filter, and use different representations such as grid-based or feature-based map. All these parameters influence the performance of the SLAM and the computational cost that is an important criterion in our case. We tested several open-source SLAM such as MRPT project [133], DP SLAM [134] and BreezySLAM and CoreSLAM [135]. All these different methods are able to locate the robot with enough accuracy to reach its goal. However, the outcome of our research is to design a smart cost platform and simple code to be understandable. Then, we choose to run our SLAM on a single board computer because the price of the component needed to run the program must be as low as possible. The Breezy SLAM seems to be the most adapted solution to reach our objectives. Furthermore, the simplicity of the code will make easier possible modifications and tuning of the parameters.

### 4.3.3 Implementation of a smart cost localization system

#### 4.3.3.1 The prototype platform of the mobile robot

The Breezy SLAM has a LIDAR mode, an odometry mode and a LIDAR + odometry mode. The interface to set the parameters is in python language and the parts of the algorithm that need high computational speed are in language C. Furthermore, Map-building and the Particle filter are two different parts that can be modified without create interference; adding a new particle filter without compromising the mapping part of the code is possible. We implemented the breezy SLAM on Nitrogen 6X to test the Lidar mode. The Nitrogen 6X is a single board computer from the brand boundary device with four CPU cores and a CPU Clock of 2 GHz with Linux OS. We run several tests to set the parameters and get the optimum result. Robotic platform used is a three omnidirectional wheel mobile robot. We tested on it the mode Odometry + Lidar. The mobile robot equipped with the Lidar is controlled directly from an Android application. An Arduino commands and controls the three motors, it receives the order from the user via Wi-Fi thanks to a Wi-Fi Shield. The Nitrogen is running the SLAM program while receiving sensor data from the Lidar and from the encoders. The map and the tracking of the robot is displayed on an internet server (Figure 4-13).

<figure><img src="image_50.png" /><figcaption>Figure 4-13: Structure and communication of the mobile robot with SLAM</figcaption></figure>
## Algorithm

The CoreSLAM is a simple method that can deal with ambiguous situations and is able to integrate the odometry since we can implement a non-linear error model. The basic operation of the CoreSLAM is based on a method called Incremental Maximum Likelihood (IML), at every moment the algorithm research the best likelihood match between the current observation and the current map. The IML is not a really a "simultaneous" method and build a single map incrementally without keeping another version compare to SLAM studied before. At the same time, the IML is simpler than other methods. The steps are the following (Figure 4-14):

* (a), (b) The robot start to create a first mapping of its environment

* The robot moves, but the map stays unchanged. Via the odometer's data and its previous position, the algorithm calculates the velocity to make a first estimation of the new position (c).

* The particle filter (Monte Carlo algorithm) creates several hypothetic positions around the first estimation and then calculates for each one a "score" of likelihood (score explain below) between the map and the current scan. The RMHC algorithm finds the particle with the lowest score, which correspond to the real position of the robot (d).

* The new position is updated on the map and then the map is updated (e), (f).

<figure><img src="image_51.png" /><figcaption>Figure 4-14: Representation of the different steps of the algorithm</figcaption></figure>

The Monte Carlo algorithm generates the hypothesis (particle), its ease of implementation makes it a very used algorithm. The accuracy of the position and the computation load increases with the size of the particle set used. The setup of the particle set is also an advantage because it gives flexibility on the computational resource compared to other algorithms.

The score system is a simple operation that is executed thousands of times for one localization. It uses the value of pixels in grey-level to attribute a score, it goes from 0 (black pixel) to 255 (white pixel). Each hit point from the current laser scan is compared to the latest map accordingly to the frame of a particle then a sum of all the values determine the score of this hypothesis pose. A high score means that a lot of hit point land in a white part of the map thus the hypothetical position is unlikely to be the correct position. On the contrary a hypothesis with low score means that the scan fit well the map thus it is more likely the real position of the robot. The Random Mutation Hill Climbing (RMHC) is a local search algorithm; it helps to find the hypothesis particle with the best likelihood. Most of the code is in Python, except three functions with a high computational time that are in C language: the function "Distance Scan to Map" that determine the likelihood of a given position, "Map Laser Ray" that integrate a new scan to the map and the RMHC algorithm.
## Parameters

As explained previously, several parameters can be set in order to optimize the SLAM according to the components used, here a recap and details of each one:

*   **Samples:** It corresponds to the minimum number of samples that delivers the LIDAR in one scan. It goes from zero to 250 samples per scan. It influences the positioning accuracy. More measurement means more precision. Ideally, we will set the parameters to 250, but the more samples per scan the more the computational cost is high. Thus, a high rate could give an unchanging position or an empty map.

*   **Quality:** It is the integration speed of the scan on the map; it can be tuned from zero to 255. It can influence the localization capabilities because its influence on the quality of the map on the display.

*   **Size of the map:** This parameter is important because it defines the maximum size of the map that can be discovered by the robot. The program does not re-scale the map. The size to enter is in meters and, in the original code. The robot first position is in the middle of this map. To define this parameter, we must take into account the area to be mapped by the robot.

*   **Pixels of the map:** Define the number of pixels on the map, its influence on the quality of the resulting map. This parameter must be set accordingly to the computation power. Too many pixels are computationally expensive; on the contrary, fewer pixels can degrade the position estimation.

*   **Resolution of the map:** It is the ratio between the size of the map by the number of pixels, it defines the quality of the map but also the quality of the positioning. A low resolution (high size of the map and a low number of pixels) gives poorer results as well as for the resulting map than for the quality of the positioning

*   **Number of iterations:** This parameter is linked to the RMHC algorithm. It sets the number of solutions that will be explored. A higher number will significantly increase the accuracy of the position and the computational time.

*   **Alpha:** Correspond to the slippage probability of the robot in the non-linear error model of the particle filter.

## Maps

To represent the surrounding environment of the robot, the CoreSLAM uses a grey-level map (Grid based map) containing three types of data:

*   Obstacles: the value of the pixels is 0 (black)

*   Navigable area: the value of the pixels is 255 (white)

*   Undiscovered area: the value of the pixels is 128 (grey)

For the integration of the laser ray on the map, it uses a Bresenham algorithm to draw the laser ray as a straight line on the map with the pixels. One particularity of the map is the representation of obstacles: Indeed obstacles are represented as digging holes with a fixed width with the lower point corresponding to the position of the obstacle whereas most of mapping techniques use a single point. The “hole” corresponds directly to the peakiness of the likelihood function and it leads to a better efficiency of the particle filter. Then the map can be interpreted as a surface with attractive holes (Figure 4-15). The default robot position is in the middle of the map, while the robot is moving the map is progressively built. The obstacles are clearly identifiable on the map above, the SLAM adds to the map every object detected as a wall with a fixed thickness corresponding to the likelihood function. Notice that the “noise” in the lower part of the map is due to the low number of samples (100) and scan integration speed to high.

### 4.3.3.2 Qualification of the performance

To qualify the performance of our localisation system, we performed several tests to determine its behaviour in narrow/wide environment, how it handles dynamic obstacles, but also the accuracy in
detecting movement (tracking). The maximum of iteration has been set to 1000. Beyond this number, intermediate tests show that there is no longer noticeable improvement. The slippage probability has been set to 10 %, thus 10 % of the particles will stay at the initial position while the others will move accordingly to the data from the odometry. We test it by simulating a slippery: we raise the robot so that the wheels cannot touch the ground and give the order to go forward, the odometry inform the robot that it is moving while the robot is motionless: The position on the map of the robot remains fixed.

<figure><img src="image_52.png" /><figcaption>Figure 4-15: Mapping of the mechanical labotary. a) Quality: 50 / Size: 15 b) Quality: 1 / Size: 20</figcaption></figure>

The parameter “hole width” is set to 300 mm, then every obstacle will have a larger of 0.3 m on the map. Finally, after several tests we set the integration speed of the scan (quality) to the minimum (1 out of 255). The resulting map is clean and stable. This is the optimal setting to build a map. We succeed to map the mechanical laboratory at PSA (Figure 4-15).

On the second map (Figure 4-15b), we observe clearly the obstacles (Black line with white line on both sides) and the obstacle-free area compare to the first map (Figure 4-15a). The drawback to the low integration speed is that building the map takes longer and the integration on the map of fine obstacles take more time. Even though, we see that BreezySLAM supports every movement of the robot and keep the map stable even during the rotation of robot which is the most tricky movement in SLAM. In addition, the algorithm treats moving obstacles. If the obstacle moves, its representation on the map moves also. We also highlighted some weakness of our system during these tests. The LIDAR can send wrong information of depth depending on which surface it reaches and with which angle. Sometimes the laser ray can go through the material as for a glass or semi-opaque glass material. Also in environment with no obstacle in the scope of the LIDAR or an environment with a specific pattern as a corridor, the robot has a tendency to lose its position. The algorithm will not detect any change in the environment and will therefore believe in a slippage. In order to qualify the accuracy of the localization, we perform these tests: tracking accuracy at different speeds, calculation of the distance and angle errors in the localization. The first test consists of move the robot at different speeds over the same distance. It will highlight the influence of the speed of movement on the tracking of the robot’s position. The test runs as follows: Two parallel lines are installed on the ground three meters apart. The robot will travel this distance at three different speeds: 100 mm/s, 200 mm/s, 400 mm/s. When the robot reaches the last line, the position calculate by the breezy slam is saved. Then we can compare the estimation of the position at the time t (the moment when the robot reaches 3 meters) (Table 4-7).

<table><caption>Table 4-7: Tracking of the robot</caption><thead><tr><th>Speed (mm/s)</th><th>100</th><th>200</th><th>400</th></tr></thead><tbody><tr><td>Estimation of the position (mm)</td><td>2881</td><td>2959</td><td>1777</td></tr><tr><td>Difference with actual value at time t (mm)</td><td>119</td><td>41</td><td>223</td></tr></tbody></table>
As, we observed the SLAM is more reactive on the tracking at a good speed (200 mm/s), it is coming from the latency: at low speed, it needs more scans to detect the movement. At the speed 400 mm/s, the robot does not have enough time to build the map correctly, thus the SLAM loses the tracking of the robot. The second test will shows the distance and angle errors after a different kind of displacements. We will compare the real position of the robot (distance and angle) with the estimated position of the SLAM. The robot will make the following moves (with a speed of 100 mm/s):

* Combination Rotation / Translation: Move 4 meters, rotation of 90 degrees, move 2 meters.

* 2 types of translation: Move 3 meters. Move 50 mm.

* Rotation: Rotation of 450 degrees.

<table><caption>Table 4-8: Localization errors</caption><thead><tr><th>Type</th><th>Combination</th><th>Translation (3m)</th><th>Translation (50 mm)</th><th>Rotation</th><th>400</th></tr></thead><tbody><tr><td>Distance Error (mm)</td><td>22.4</td><td>19,88 mm</td><td>5,75 mm</td><td>-</td><td>1777</td></tr><tr><td>Distance Error (%)</td><td>0.5 %</td><td>0,7 %</td><td>11,5 %</td><td>-</td><td>223</td></tr><tr><td>Angle Error (°)</td><td>0.5</td><td>-</td><td>-</td><td>4°</td><td></td></tr></tbody></table>

## Conclusions

The SLAM is stable when the robot is motionless. The position remains at 0 most of the time, with occasionally a derivation up to around 6 mm in X or/and Y. Furthermore, even with two mobile obstacles (Humans) around the robot, the position does not vary. According to Table 4-8, the localization error remains stable around or below 2 cm. This meets the requirements. The orientation of the robot that the SLAM calculates is also really close to the real orientation. Map was built gradually during each runs. Thus, when the robot performs three rotations while building the map, this increases the errors of localization because the data from the LIDAR during a rotation is more difficult to compute than during a translation. Thus, the BreezySLAM is powerful enough for our needs, it could be used for the “Robot Charger”.

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, we successfully build a reference map of an area (Figure 4-16). In addition, the drawbacks of this platform are highlighted: Errors in the map when the ray of the LIDAR reaches a glass or semi opaque glass, not detecting thin obstacle and can be lost when there is a lack of obstacles (reference) up to 10 meters around the robot or when there is a long pattern (corridors). Even though, according to our 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 steps to use this localization system in a real application could be done. First, the robot charger performs a first recognition in the new environment to build a reference map. Then, the map becomes a reference map for the localization of the robot during its operation. It will continue scanning the environment to detect new obstacles. The next step is to implement a cognitive system in order to navigate in the environment according to the map.
<figure><img src="image_53.png" /><figcaption>Figure 4-16: Reference map of the robot</figcaption></figure>

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

This study is a published article and is presented in Appendix D. The conclusions are below.

The total GWP impact of the base case scenario is estimated to 50590 kg CO₂ eq. The multi-objective optimization for environomic design of the new vehicle- house system is performed for the definition of the environomic design of the driving and the residential mode. In the case of the energy supplied in France the optimization sizes the SOFC-GT module for the mobility needs. The rest of the energy needs of the house are covered by the grid supply. Optimal design of the range extender vehicle in dual mode is proposed for 200 km of autonomy, investment cost of 28430 € and 3808 €/year of total driving yearly cost. The base case scenario has lower investment (20900 €) and total driving yearly cost (3229 €/year).

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.
<figure><img src="image_54.png" /></figure>
# Chapter 5 Energy storage and conversion systems for energy vectors: fuel cell electric vehicles, hydrogen and their ecosystem

This chapter presents an innovative propulsion system with high efficiency, using fuel cells. The hydrogen production is considered for different energy vectors and their ecosystem.

Hydrogen is considered as clean energy, but several questions related to its production, its large-scale storage and distribution remain. The current state of the art of fuel cell vehicles is the use of pressurized hydrogen gas at 700 bar, stored on the board of the vehicles and converted into electricity by a membrane fuel cell (PEM) technology. Storage and 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 the form of bioethanol and to convert the bioethanol to hydrogen on the board the vehicle by using a solid oxide fuel cell (SOFC).

This chapter presents the concept of electric vehicle equipped with serial range extender to extend the vehicle autonomy. The 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. An alkaline fuel cell and a solid oxide fuel cell are investigated for the use of a variety of liquid fuels instead of traditionally used hydrogen in others types of fuel cells. The energy balance of the different types of fuel cells powered by bio-ethanol is investigated. Three variants for the 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. From the results it is visible that the most efficient configuration is the variant 3 and concerns the direct reforming of the bio-ethanol by the solid oxide fuel cell. The variant 3 delivers 15.15 MJ of electric energy output per kilogram of bio-ethanol, used in the fuel cell.

The catalyst system for the bio-ethanol steam reforming is investigated on experimental way. It is needed a high active and selective catalytic system requiring 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% of 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 exist couple 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 $Al_2O_3$, $ZnO$ or $CeO_2$ or $ZrO_2$. 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 a well-to-wheels balance on the energy efficiency and the CO₂ emissions is done. The balance concerns different vehicles propulsion systems. The impact on the energy efficiency and the emissions is discussed.

The investigated concept presents the advantage to extend the vehicle basic electric range thanks to this highly efficient converter. A second advantage is the reforming of a liquid fuel on board which avoids the storage of hydrogen under elevated pressure. This proposal suggests 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. The SOFC operates at high temperature (800 °C to 1000 °C). Figure 5-1 presents the overall layout system based on SOFC. This kind of fuel cell can use a variety of fuels instead of traditionally used hydrogen in others types of fuel cells. It can be powered by CH₄, C₂H₅OH, CO or others fossil fuels. This is very advantageous from the application point of view of such fuel cells on board of automobiles. In this case we have not any problems with the purity of the produced hydrogen which is strongly required to power PEMC stack system. That is why we need a high active and selective catalytic system requiring 100% of fuel conversion. This system will be applied in the micro reactor directly before the SOFC fuel cells stack system (Figure 5-1).

<figure><img src="image_55.png" /><figcaption>Figure 5-1: Vehicle propulsion system with an innovative range extender system [136]</figcaption></figure>

The total conversion of C₂H₅OH is essential from the economic point of view. 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. Figure 5-2 presents the possible reaction which takes place during the steam reforming of ethanol reaction. As we can easily observed the selection of the best catalyst to this process is important to obtain the final product mixture (CO₂ and H₂).

<figure><img src="image_56.png" /><figcaption>Figure 5-2: Reactions involved in the ethanol steam reforming.</figcaption></figure>

One of the best candidates is the ethanol. The ethanol can be obtained from sugar beet, sugar cane and other plants. Ethanol and especially bio-ethanol is convenient for reforming in the reactor before the fuel cell and has the advantage to be a renewable fuel, produced from biomass, as illustrated from Figure
5-3. The main advantage of the concept is to obtain a long range, with easily supply of liquid fuel that it has a carbon neutral life cycle.

<figure><img src="image_57.png" /><figcaption>Figure 5-3: Carbon neutral cycle of the e-Bio Fuel Cell vehicle</figcaption></figure>

Figure 5-4 presents the working principle of the e-Bio Fuel Cell system. The system produces hydrogen from ethanol- blended water through reformer. The main reaction is $C_2H_5OH + 3H_2O \rightarrow 6H_2 + 2CO_2$. The $H_2$ produced in the reformer reacts with the oxygen from the air in the SOFC and then produces electricity and heat. The electricity is used to charge the high voltage battery of the vehicle. The heat is reused in the reformer to help the reforming of bio-ethanol to water. Thus, the system has a high efficiency of 65%.

<figure><img src="image_58.png" /><figcaption>Figure 5-4: Working principle of the e-Bio Fuel Cell system</figcaption></figure>

The SOFC is the best fuel cell for the concept. It presents the following advantages to be used to power electric vehicles:

* Long lifetime

* Low costs of the electrodes

* Gases produced in SRE process can directly be used to power fuel cell stack

* High fuel efficiency

* Various compounds could be used to power fuel stack even natural gas

* Chemical stability of the solid electrolyte

* Low cost of raw materials for the electrolyte production

The disadvantages of using SOFC to power electric vehicles are:

* High operating temperature

* Long start-up

* Mechanical and chemical compatibility issues
## 5.1 Energy balance of the production and the conversion of hydrogen in the fuel cells

The results in part 5.1 and 5.2 are coming from the research collaboration between PSA and the Chemical department of the Lodz University of Technology (Poland).

The choice of bio-ethanol is favorable by the fact that it is non-toxic compared to methanol. It is easily available. It is easily converted under atmospheric pressure and there is a large possibility to obtain it from biomass. Bioethanol has the property to be stored easily in liquid form, and to be safely used. Bio ethanol is as well a renewable energy source.

It is possible to obtain different configurations of energy vectors (fuels) and types of fuel cell. The alkaline fuel (AFC) cell is powered by hydrogen. The solid oxide fuel cell (SOFC) can use liquid fuels, directly powered by reforming the fuels in an integrated reformer. According to the type of fuel, the processes that the fuels pass through are different (Figure 5-5).

<figure><img src="image_59.png" /><figcaption>Figure 5-5: Schematic diagram of the various type of fuel cell powered by different feedstocks</figcaption></figure>
One can observe from Figure 5-5 that to use the fuels for the alkaline fuel cell the fuels have to pass through different steps in order to reduce the content of CO after the reforming stage. The alkaline fuel cell needs pure hydrogen, because of the risk of contamination of the electrodes by CO. The SOFC has the advantage to accept the hydrogen from after the reforming or to be directly powered by the different fuels, without any transformation of the fuels. Figure 5-5 defines three different energy systems between fuels and converters. The energy balance of the three systems, with different chemical processes of the fuel adaptation, will be investigated to understand and to compare the efficiency of conversion of the fuel adaptation steps of the three scenarios. This balance concerns the chemical energy available after the chain of chemical processes needed to adapt the fuels (vaporization, reforming, drying, PSA and H₂ generation) for the fuel cells inputs. For the calculation of energy balance, the thermodynamics of the processes and the chemical reactions are used. The efficiency of the different types of fuel cell is considered. The energy output of 1 kg of ethanol is after that available for conversion in the fuel cell to obtain electricity. Table 5-1 presents the comparisons of the values of the energy balance for the different configurations (variants) presented in Figure 5-5. From the results is the visible that variant 3 presents the configuration with the highest efficiency. This concerns the direct reforming of the bio-ethanol by the SOFC.

<table><caption>Table 5-1: Comparison of the values of the energy balance for the different configurations</caption><thead><tr><th></th><th>Vaporiser<br>kJ/mol<br>fuel</th><th>Reformer<br>kJ/mol</th><th>Dryer<br>kJ/mol</th><th>PSA<br>(CO<sub>2</sub><br>separation)<br>kJ/mol</th><th>Process of<br>H<sub>2</sub><br>generation<br>MJ/kg</th><th>Fuel cell<br>Input<br>MJ/kg<sub>EiOH</sub><br>(chemical)</th><th>Fuel cell<br>efficiency<br>%</th><th>Fuel Cell<br>output<br>MJ/kg<sub>EiOH</sub><br>(electric)</th></tr></thead><tbody><tr><td>Variant 1<br>(C<sub>2</sub>H<sub>5</sub>OH+H<sub>2</sub>O)</td><td>-466.45</td><td>-337.98</td><td>39.31</td><td>-21.85</td><td>-7.78</td><td>20.06<br>(AFC)</td><td>60%<br>(AFC)</td><td>12.04<br>(AFC)</td></tr><tr><td>Variant 2<br>(C<sub>2</sub>H<sub>5</sub>OH<br>+H<sub>2</sub>O)</td><td>-466.45</td><td>-342.10</td><td>------</td><td>----------</td><td>-8.08</td><td>20.10<br>(SOFC)</td><td>65%<br>(SOFC)</td><td>13.06<br>(SOFC)</td></tr><tr><td>Variant 3<br>(C<sub>2</sub>H<sub>5</sub>OH+O<sub>2</sub>)</td><td>------<br>---</td><td>------<br>---</td><td>------<br>---</td><td>----------<br>------</td><td>0.00</td><td>23.30<br>(SOFC)</td><td>65%<br>(SOFC)</td><td>15.15<br>(SOFC)</td></tr></tbody></table>

Table 5-2 summarizes the advantages and the disadvantages of the different conversion configuration, presented in Figure 5-5.

<table><caption>Table 5-2: Advantages and disadvantages of the different configurations (variants)</caption><thead><tr><th></th><th>Advantages</th><th>Disadvantages</th></tr></thead><tbody><tr><td>Variant 1</td><td>Low cost fuel cell.<br>Compatible to all types of fuel cells.</td><td>The lowest overall system efficiency.<br>Multiple chemical processes.<br>Three catalytic converters (at least one<br>of them contain Pt catalyst).<br>System load can not be adjusted on-<br>line.<br>Overall large dimensions difficult to<br>use in a small size vehicles.</td></tr><tr><td>Variant 2</td><td>Overall system efficiency is slightly higher in<br>comparison to multistep process.<br>System load can be adjusted on-line in the<br>limited range.<br>It is not necessary to remove CO from the H<sub>2</sub><br>stream.<br>Only one catalytic reactor is used.<br>Low requirements on the type of fuel.</td><td>Only SOFC cell could be used.<br>Limited number of start/stop cycles.<br>Limited lifetime of SOFC system.</td></tr></tbody></table>
<table><tr><td>Variant 3</td><td>The highest overall system efficiency.<br/>The most compact configuration.<br/>System load can be adjusted on-line in the limited range.<br/>It is not necessary to remove CO from the H<sub>2</sub> stream.<br/>Only one catalytic reactor is used.<br/>Low requirements on the type of fuel.</td><td>Only SOFC cell could be used.<br/>Limited number of start/stop cycles.<br/>Limited lifetime of SOFC system.</td></tr></table>

The variant 3, which presents the direct reforming of bio-ethanol, is the most efficient combination. In conclusion, it is proposed the working principle of the bio ethanol fuel cell for range extender unit for vehicle application. This unit delivers 23.3 MJ of chemical energy per kilogram of bio-ethanol. After conversion of the bio-ethanol in the SOFC fuel cell module 15.15 MJ of electric energy per kilogram of bio-ethanol is obtained.

The 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.

It presents the following advantages to be used to power electric vehicles:

* Long lifetime

* Low - costs of the electrodes

* Gases produced in SRE process can directly be used to power fuel cell stack

* High fuel efficiency

* Various compounds could be used to power fuel stack even natural gas

* Chemical stable of the solid electrolyte

* Low cost of raw materials for the electrolyte production

The disadvantages of using SOFC to power electric vehicles are:

* High operating temperature

* Long start-up

* Mechanical and chemical compatibility issues

The next part investigates the best catalyst system to integrate to the reformer to maximize the hydrogen yield and the stability of the conversion reaction from bioethanol to hydrogen.

## 5.2 Hydrogen from (bio)-ethanol steam reforming, selection of the catalysts:

For future carbon- neutral hydrogen production, the catalytic ethanol steam reforming (ESR) using bioethanol is seen as a promising solution. Ni- based and Co- based catalysts have been studied as a cost attractive solution for large-scale hydrogen production. However these non-noble transition metals catalysts present several difficulties including deactivation and activity by formation of coke and methane. Minimizing coke and methane formation requires an appropriate promoter or second metal, in addition to precise control of particle size and oxidation state of the active metal and acid-base and redox properties of support by changing the composition. Highly dispersed small metal particles are suitable for the active site with coke resistance, which can be prepared using an appropriate preparation method and/or support with good metal-support interaction. For example for Co catalyst, the oxidation state and redox properties of the supported Co strongly affect catalytic activity and durability. The oxidation- redox properties should be controlled by allowing or modification by promoter and/or support. Ethylene, regarded as a coke precursor, is formed mainly over the acid site of support. Therefore, basic support is effective for coke suppression. Redox active supports with oxygen storage capacity and oxygen mobility contribute to removal of deposited coke, change in metal oxidation state, and thereby improve coke resistance.
The main target of the research is to obtain a high active, selective and stable catalytic system for steam reforming of ethanol (ESR). The research is experimental and contains the following steps and durations:

* Preparation of the support material

* Preparation of mono and bimetallic catalysts by wet impregnation method

* Determination of the optimal process conditions

* Optimization of the catalysts composition and reaction conditions

* Determination of the susceptibility for carbon deposition

* Specific surface area measurements. Selective surface area measurements

The purpose is to obtain the highest possible yields of H₂, using ethanol steam reforming and adapted catalyst for the reactions. The mechanism of ethanol steam reforming is displayed in Figure 5-6 and the main reactions included in the ESR are described by equations (5-1) to (5-13).

<figure><img src="image_60.png" /><figcaption>Figure 5-6: Mechanism of ethanol steam reforming [137]</figcaption></figure>

$$C_2H_5OH + 3H_2O \rightarrow 2CO_2 + 6H_2 \qquad (5-1)$$

$$C_2H_5OH + 3H_2O \rightarrow 4CO + 6H_2 \qquad (5-2)$$

### Thermal decomposition

$$C_2H_5OH \rightarrow CO + CH_4 + H_2 \qquad (5-3)$$

### Ethanol dehydrogenation reaction

$$C_2H_5OH \rightarrow CH_3CHO + H_2 \qquad (5-4)$$

### Ethanol dehydration

$$C_2H_5OH \rightarrow C_2H_4 + H_2O \qquad (5-5)$$

### Acetaldehyde decarbonylation

$$CH_3CHO \rightarrow CO + CH_4 \qquad (5-6)$$

### Ethylene hydrogenation

$$C_2H_4 + H_2 \rightarrow C_2H_6 \qquad (5-7)$$

### Coke formation
### C₂H₄ polymerization

$$C_2H_4 \rightarrow \text{coke or } C_2H_4 \rightarrow 2C + 2H_2 \qquad (5-8)$$

### Methane pyrolysis

$$CH_4 \rightarrow C + 2H_2 \qquad (5-9)$$

### Boudouard reaction

$$2CO \rightarrow C + CO_2 \qquad (5-10)$$

### CO₂ formation

$$CH_4 + 2H_2O \rightarrow CO_2 + 4H_2 \qquad (5-11)$$

$$C_2H_4 + 4H_2O \rightarrow 2CO_2 + 6H_2 \qquad (5-12)$$

$$CO + H_2O \leftrightarrow CO_2 + H_2 \qquad (5-13)$$

The selected research conditions are at P – atmospheric pressure and T – 400-600°C.

The typical catalytic systems are composed from metal catalyst and supports:

* Metal catalysts: Co, Ni, Pd, Pt, Rh, and Ru

* Supports: (SiO₂, Al₂O₃, CeO₂, ZrO₂, MgO, SiO₂-Al₂O₃, SiO₂ – MgO, AC)

There are noble and non-noble metals catalyzing the reactions and the reactions and their efficiency depends on the operating conditions as temperature and pressure.

From the operating conditions for ethanol steam reforming (ESR) for noble and non-noble catalysts data reported in the tables in the literature, it is visible that the reactions conditions are different. Noble catalysts need lowest temperatures for reactions conditions. On the other side, the conversion efficiency depends as well from the different types of catalysts. They have as well different selectivity properties for reactions products as CH₄, CO, CO₂, C₂H₄ and liquid. As result, the final H₂ yield of the ESR is different and it depends of catalyst choice.

From the data reported, catalytic performance appears to be influenced not only by the operational conditions employed, but also the nature of the metal phase and support used. Nobel metal-based catalysts frequently exhibit better activity when compared to non-noble metal catalysts; however, these catalysts are very expensive. As a less expensive alternative, cobalt-based catalysts have been shown to have high activity and selectivity toward hydrogen, due to their capacity for CeC bond cleavage. Generally, acidic supports, such as Al₂O₃, are known to favor the dehydration reactions to ethylene and diethyl ether, followed by polymerization of ethylene to form coke. Basic supports, such as MgO, favor dehydrogenation and condensation reactions producing acetaldehyde and acetone.

The presence of these undesirable products, generated from these reactions, hinders the overall hydrogen production reducing the catalyst selectivity towards H₂. However, better catalytic performance with good stability and high hydrogen selectivity can be expected over supports with redox properties, such as CeO₂. This oxide has been commonly used as catalyst or catalyst support, as well as an effective promoter for catalysts, in the ethanol steam reforming process. This oxide has high oxygen mobility and storage capacity, and can act as a local source or sink for oxygen involved in reactions taking place on its surface. Notably, cerium oxide is known to enhance the reducibility, stability and the dispersion of the supported metal.

It is not obvious to select the best catalyst. This is question of compromise, between the main indicators of performance of the catalytic systems. A radar of the generic performances of the catalytic system is
displayed in Figure 5-7. It is very important to master the process of production of the catalyst to respect the below shown performances.

<figure><img src="image_61.png" /><figcaption>Figure 5-7: Main performances of the catalysts (bimetallic catalyst)</figcaption></figure>

The main goal of the present research is to optimize the conditions of the chemical process (temperature, the composition of the gas mixture, stability test) and to select the composition of the catalyst for the ethanol steam reforming (ESR) and the hydrogen production for SOFC application. The ESR is integrated to the SOFC System and the heat operating conditions are between 600-1000°C.

As catalytic systems are selected:

* Monometallic Ni-based catalysts

* bimetallic Pd-Ni catalysts

supported on $Al_2O_3$, $ZnO-Al_2O_3$, $CeO_2-Al_2O_3$

The catalysts are produced according to a production process in laboratory conditions. They are tested as well in laboratory conditions. The following part describes the experimental work conduct for a research work for one-year period.

## 5.2.1 Experimental work

The applied investigation methods are Brunauer–Emmett–Teller (BET) theory - specific surface area, chemisorption method, and catalytic activity measurements. BET theory aims to explain the physical adsorption of gas molecules on a solid surface and serves as the basis for an important analysis technique for the measurement of the specific surface area of materials. The BET theory applies to systems of multilayer adsorption and usually utilizes probing gases that do not chemically react with material surfaces as adsorbates to quantify specific surface area. Nitrogen is the most commonly employed gaseous adsorbate used for surface probing by BET methods.

### $N_2$ adsorption-desorption

Nitrogen adsorption isotherms of catalyst and supports were obtained at – 196 °C using a Sorptomatic 1900 (Carlo Erba Instruments Figure 5-8). The samples were previously outgassed at 300 °C and equilibrated under vacuum for at least 4 h before measuring the adsorption-desorption isotherm. Specific surface areas were evaluated from the measured monolayer capacity (Brunauer–Emmett–Teller method) using the range of relative pressure from ~ 0.05 to 0.33 and the value for nitrogen cross-section 0.162 $nm^2$. The pore size distribution was calculated by the Dollimore and Heal (DH) method from the desorption branch of the isotherm.
<figure><img src="image_62.png" /><figcaption>Figure 5-8: Photo of 1900 Sorptomatic apparatus 51</figcaption></figure>

### 5.2.1.1 Preparation of the catalysts

Firstly the support is prepared according to the steps presented in Figure 5-9. After that, the steps in Figure 5-10 are applied to prepare the monometallic catalyst.

<figure><img src="image_63.png" /><figcaption>Figure 5-9: Catalyst support preparation scheme</figcaption></figure>
<figure><img src="image_64.png" /><figcaption>Figure 5-10: Monometallic supported catalysts preparation scheme</figcaption></figure>

Catalysts were prepared using a wet impregnation method. The Al₂O₃ monoxide was prepared by precipitation method from an aqueous solution of Al(NO₃)₃·9H₂O and using a concentrated ammonia solution as a precipitating agent. The obtained precipitate was filtered off under vacuum, washed with deionized water until the filtrate became neutral, dried at 120 °C for overnight, and calcined at 400 °C for 4 h under air. As a carrier a commercial Alumina Fluka 507 C was used. In the first step on alumina surface the CeO₂ or ZnO was deposited by impregnation method from appropriate nitrates (Ce or Zn). The loading of Zn (Ce) was 20%wt. Samples were dried and finally calcined in air atmosphere. To obtain oxidic form of carrier samples were dried and finally calcined in air at 400°C. After that Ni was deposited by wet impregnation method from nickel nitrate water solution. In the next step the catalysts precursors were dried and calcined at 400°C. Prior to process catalysts were reduced in hydrogen stream at 400 °C to transform nickel oxide to metallic nickel. The nominal nickel loadings were 5 and 20%.

### 5.2.1.2 Characterization

The materials were characterized by BET surface area measurements to measure the specific surface area and porosity of the prepared supports: Al₂O₃, Al₂O₃-20%Ce, Al₂O₃-20%Zn. The selected catalysts were determined by the BET method based on low temperature (−196°C) nitrogen adsorption method in a Sorptomatic 1900 Carlo-Erba apparatus (Figure 5-8). Samples before the measurements were degassed at 350°C.

Table 5-3 shows the results of the surface area measurements and the average pore radius of the prepared supports and catalysts.

<table><caption>Table 5-3: Characterization of the samples (supports and catalysts) with BET analysis</caption><thead><tr><th>Support</th><th>BET surface area<br>[m²/g]</th><th>Average pore radius [nm]</th></tr></thead><tbody><tr><td>Al₂O₃</td><td>100</td><td>2.79</td></tr><tr><td>Al₂O₃ - 20%Ce</td><td>76</td><td>2.64</td></tr><tr><td>Al₂O₃ - 20%Zn</td><td>67</td><td>2.56</td></tr><tr><td>5%Ni/Al₂O₃</td><td>99</td><td>2.91</td></tr><tr><td>20%Ni/Al₂O₃</td><td>66</td><td>2.59</td></tr><tr><td>5%Ni/Al₂O₃ - 20%Zn</td><td>55</td><td>2.63</td></tr><tr><td>20%Ni/Al₂O₃ - 20%Zn</td><td>40</td><td>2.65</td></tr><tr><td>5%Ni/Al₂O₃ - 20%Ce</td><td>68</td><td>2.67</td></tr><tr><td>20%Ni/Al₂O₃ - 20%Ce</td><td>54</td><td>2.72</td></tr></tbody></table>
Table 5-3 shows that the adding of 20% of Ce and Zn (in oxidic form) on the Al₂O₃ support reduces the surface area of the catalyst support. On the other hand, adding Ni on the Al₂O₃ support to 5% or 20% decreases as well the specific surface area. The introduction of 5% of Ni increases the average pore radius of the Al₂O₃ support to 2.91 nm for almost the same specific surface area of 99 m²/g. The addition of 20% of Ni to the Al₂O₃ support decreases the specific surface area more than the 5% Ni to 66 m²/g and the average pore radius to 2.59 nm. All other combinations of bimetallic supports and Ni introduction lead to small specific surface area and average pore radius in comparison of the initial Al₂O₃ support. The lowest specific surface area was observed for the catalyst systems containing the highest amount of nickel oxide (20 wt%). Such result can be due to the blockage of support pores by the deposited metal particles.

Figure 5-11 represents the evolution of the specific surface to the average pore radius for the different catalysts and catalyst supports combination. One observes the same tendency as in Table 5-3.

<figure><img src="image_65.png" /><figcaption>Figure 5-11: BET measurements</figcaption></figure>

### 5.2.1.3 Activity evaluation

A test of the activity of the catalysts is done. A sample 20%Ni/Al₂O₃ is selected and tested in two configurations. The formation of H₂, CO and CO₂ is investigated in the interval of temperature from 50°C to 700°C.

**Configuration 1:** 20%Ni/Al₂O₃ p = 1Bar, C₂H₅OH: H₂O = 1:4.5, T = 50 - 700°C, after red. at 500°C catalyst mass mₖₐ₅ = 0.2 g

Before activity tests the catalyst was reduced in hydrogen stream at 500°C for 3 hours (the reduction temperature was chosen on the base of literature studies). During the process, the reduction of NiO take place according the scheme: NiO + H₂ → Ni + H₂O

**Configuration 2:** 20%Ni/Al₂O₃ p = 1Bar, C₂H₅OH: H₂O = 1:4.5, T = 50 - 700°C, calcined at 400°C catalyst mass mₖₐ₅ = 0.2 g

For preliminary activity tests experiments (Figure 5-12, Figure 5-13) respectively were done using MS-spectrometer as a detector with the heating ramp rate 10°C/min. Based on above mentioned experiment the optimal temperature range for activity measurements were chosen, and we decided to study activity in the temperature range 250-600°C.
<figure><img src="image_66.png" /><figcaption>Figure 5-12: Activity test of 20%Ni/Al₂O₃, configuration 1</figcaption></figure>

<figure><img src="image_67.png" /><figcaption>Figure 5-13: Activity test of 20%Ni/Al₂O₃, configuration 2</figcaption></figure>

One can observe from Figure 5-12 and Figure 5-13 that the activation of the catalyst occurs from early for the configuration 1 at 250°C. The activation of the catalyst in the second configuration occurs for higher temperature: 400 °C.

In the next step is investigated the influence of the reaction mixture on the ethanol conversion value at 300°C and 350°C for the catalyst in configuration 1. The catalyst is redoxed at 300°C or 500 °C. The water to ethanol propotion varies and is investigated the way this impacts the conversion of the ethanol water mixter as a function of the temperature:

20%Ni/Al₂O₃ p = 1Bar, C₂H₅OH: H₂O = 1 : 3, 1 : 4.5, 1 : 6, T = 300, 350°C, after reduction, performed at 300 or 500°C, catalyst mass mₖₐ = 0.2 g

The conversion ratio of ethanol and the H₂ selectivity of the catalyst, for different ethanol to water ratio are shown in Figure 5-14 and Figure 5-15.

<figure><img src="image_68.png" /><figcaption>Figure 5-14: Ethanol conversion ratio</figcaption></figure>

<figure><img src="image_69.png" /><figcaption>Figure 5-15: Hydrogen selectivity for the catalyst</figcaption></figure>

The total 100% conversion ratio of ethanol is reached for 350°C, for catalyst reduced at 500 °C and for all ethanol to water ratios. At 300°C the conversion ratio of the lower water content and the lower temperature reduction, are low due to incomplete reduction of NiO at 300°C or partial oxidation of active phase during process by water vapor.

Figure 5-15 shows that the best selectivity of H₂ is obtained at 350°C and for 1:6 ethanol to water ratio. According to expectations, increase of water content shifts balance of the reaction to hydrogen and carbon dioxide.

Further is investigated the influence of the reaction temperature and Ni concetration in ethanol steam reforming (ESR).
The conditions of the tests are the following:

p = 1bar, C₂H₅OH: H₂O = 1:4.5, T = 280 - 600°C, after reduction 500°C, catalyst mass mₖₐt = 0.2 g

<figure><img src="image_70.png" /><figcaption>Figure 5-16: influence of the reaction temperature and Ni concertation in ethanol steam reforming (ESR) process</figcaption></figure>

In Figure 5-16 the effect of nickel loading on the catalyst efficiency is shown. One can conclude that Ni increases the catalysts efficiency significantly. At the same process conditions, a significant increase in activity is observed after increasing metal content in the catalysts, for 20% of Ni.

### 5.2.1.4 Influence of the Ni concetration and Zn addition in ESR process

The conditions of the tests are the following:

p = 1bar, C₂H₅OH: H₂O = 1:4.5, T = 350 - 600°C, after reduction 500°C, catalyst mass mₖₐt = 0.2 g

<figure><img src="image_71.png" /><figcaption>Figure 5-17: Influence of the Ni concentration and Zn addition in ethanol steam reforming (ESR) process</figcaption></figure>

From Figure 5-17 we can conclude that the highest content of Ni allow to obtain higher ethanol conversion ratio and higher H₂ yield at low temperatures from 250 °C, for 20%Ni/Al₂O₃. The catalyst with 5% of Ni activates at 350°C. At 500 °C the performances of the both catalyst are equivalent. From the analysis of Figure 5-17 is visible that addition of 20 % of Zn contributes to the improvement of the ethanol conversion and the H₂ selectivity for the temperatures 300°C to 500 °C. For temperature of 500 °C the selectivity of H₂ decreases for the both Ni catalyst, with the addition of Zn in the Al₂O₃ substrate.
The influence of the Ni concentration and Zn addition on the H₂ selectivity in ESR process for different temperatures is displayed in Figure 5-18.

<figure><img src="image_72.png" /><figcaption>Figure 5-18: The influence of the Ni concentration and Zn addition on the H₂ selectivity in ethanol steam reforming (ESR) process</figcaption></figure>

It is visible that at 500°C the 5%Ni Al₂O₃ catalyst loses in hydrogen selectivity. To improve this the Cerium Ce is tested as substitute of the Zn.

The Influence of the Ni concentration and Ce addition in SRE process are shown in Figure 5-19. The conditions of the test are the following:

p = 1Bar, C₂H₅OH: H₂O = 1:4.5, T = 350 - 600°C, after reduction 500°C, catalyst mass mₖₐt = 0.2 g

<figure><img src="image_73.png" /><figcaption>Figure 5-19: Influence of the Ni concentration and Ce addition in SRE process in ethanol steam reforming (ESR) process</figcaption></figure>

The addition of Ce activates the catalyst at higher temperature from 350°C, to reach 100% ethanol conversion, but present the advantage to increase the hydrogen selectivity, especially for temperatures 500°C – 600°C.
<figure><img src="image_74.png" /><figcaption>Figure 5-20: H₂ selectivity for monometallic Ni catalyst for ESR process</figcaption></figure>

Figure 5-20 shows the Ce additive improves H₂ selectivity for temperatures higher than 350°. At 400 °C, the H₂ selectivity is sensitive to additives of the substrate. At 400°C, the highest concentration of Ni and the Ce additive in the catalyst combination 20Ni%Al₂O₃20%Ce shows the highest hydrogen selectivity of 70%. At 500° the 5%NiAl₂O₃20%Zn losses hydrogen selectivity performance. The substitution of the Zn by Ce for the low concentration of 5% Ni catalyst improves H₂ selectivity until 70%. At 600°C, all catalyst variants including the low Ni 5% concentration obtain high H₂ selectivity, over 70%. Low concentration 5% Ni catalyst with Ce additive in the Al₂O₃ support covers the needs of bio-ethanol conversion and hydrogen selectivity for temperatures up to 400°C.

**Bimetallic catalysts help to :**

* Determination of the promotion effect of Pd on the catalytic activity

* Optimization of the palladium content in the investigated systems

* Determination of the content of carbon deposit which was formed during ESR process

Palladium is introduced to the low concentration Ni catalyst with Ce additive to the Al₂O₃ support. Two low concentrations of palladium are used: 0.1% and 1%. The catalysts are evaluated and the results presented in Figure 5-21.

<figure><img src="image_75.png" /><figcaption>Figure 5-21: influence of the catalyst composition on the H₂ selectivity in ESR process – monometallic and bimetallic catalysts</figcaption></figure>

The introduction of small concentration of palladium to the Ni based catalysts improves the H₂ selectivity. For 300°C and 400°C the highest concentration of 1% of palladium presents highest selectivity, but stays lower to the 20% Ni catalyst concentration. For 500°C, 0.1% Pd bimetallic catalyst presents the highest selectivity value. At 600°C, the bimetallic catalysts present excellent selectivity values. They are in the same range as the 20% Ni with Zn additive. Finally, for 600°C the palladium brings stability of H₂ selectivity for high temperature, as shown by the stability tests results at 600°C (Figure 5-22 and Figure 5-23).
<figure><img src="image_76.png" /><figcaption>Figure 5-22: Stability test in ESR process, p = 1Bar, C₂H₅OH: H₂O = 1:4.5, T = 600°C, after red. 500°C, mₖat = 0.2 g 5%Ni/Al₂O₃-20%Ce</figcaption></figure>

<figure><img src="image_77.png" /><figcaption>Figure 5-23: Stability test in ESR process, p = 1Bar, C₂H₅OH: H₂O = 1:4.5, T = 600°C, after red. 500°C, mₖat = 0.2 g, 0,1%-Pd-5%Ni/Al₂O₃-20%Ce</figcaption></figure>

The lowest concentration of 0.1% of palladium in the bimetallic 5% Ni catalyst is sufficient to bring optimal H₂ selectivity. The CO selectivity curve for the two selected catalysts is presented on Figure 5-24.

<figure><img src="image_78.png" /><figcaption>Figure 5-24: CO selectivity of 5%Ni/Al₂O₃-20%Ce and 0,1%-Pd-5%Ni/Al₂O₃-20%Ce catalysts</figcaption></figure>

The following conclusions can be drawn:

* 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 are CO, CO₂ and H₂.

* The highest selectivity to hydrogen formation are observed at both 500 and 600°C.

* The optimal ratio between ethanol and H₂O is 1:4.5. The increase of the molar ratio between these two components does not cause the increase of the ethanol conversion and hydrogen yield. The mixture ratio is closed to the stoichiometric.

* The activation of the catalytic systems in reductive mixture result in increase of their catalytic activity in ethanol reforming process.

* The high stability and high selectivity to hydrogen formation for mono and bimetallic systems was confirmed.

* Promotion effect of palladium on hydrogen selectivity in ESR process at high temperature range (500-600°C) was confirmed. The same effect was observed in low temperature process.
## 5.3 A well-to-wheels balance of the different energy storage and conversion technologies for sustainable mobility solutions

### 5.3.1 Emissions and energy efficiency

The most important driver for electric mobility (beyond climate protection) is the ability to operate locally emissions free, contributing to the air quality in cities, and with less noise. Although it should be reminded that noise reduction benefits from electrified vehicles are limited to up to 30 km/h, according to the ERTAC report [1]. Electric and electrified vehicles are particularly attractive since they represent vehicles that offer the most efficient use of regenerative energy. Although electric vehicles are more efficient, the CO₂ emissions are strongly dependent on the electric mix, used for the battery charging and the electricity production. This balance is prepared in the frame of the PSA- Airbus workshops, where advanced energy technologies are discussed.

The evolution of the well-to-wheels CO₂ emissions of battery electric vehicles (BEV), Fuel Cell Electric Vehicles (FCEV) in comparison with the conventional powertrains is shown in Figure 5-25 and in Figure 5-27 and described in much detail in the energy mix tables.
<figure><img src="image_79.png" /><figcaption>Figure 5-25: W-t-W total CO₂ emissions for different powertrain solutions for vehicles</figcaption></figure>

<caption>Figure 5-26: Energy mix of France for LCA analysis</caption>
<figure><img src="image_80.png" /><figcaption>Figure 5-27: W-t-W total CO₂ emissions for different powertrain solutions for vehicles</figcaption></figure>

<caption>Figure 5-28: Energy mix of Europe for LCA analysis</caption>
Figure 5-25 shows the CO₂ emissions in well-to-wheels perspective and for different powertrains configurations: ICE powered vehicles, different types of hybrid electric vehicles, electric vehicles, and as well fuel cell electric vehicles. As it is a well-to-wheel CO₂ emissions perspective it is necessary to consider the electricity production mix and the way of production of hydrogen. Figure 5-26 shows the considered electricity production mix, which considers the French case, with electricity produced from nuclear at 76%. This electricity mix presents low CO₂ emissions. Two different configurations of fuel cell vehicles is as well considered: full power fuel cell vehicles and fuel cell as a range extender in the case of serial hybrid electric vehicle. One can see that the hydrogen production way is as well considered in the analysis: methane steam reforming, water electrolysis with the electricity from the grid, considering the electricity production mix and H₂ production with electrolysis, using renewable energy from the grid. A sensitivity analysis is given in the Figure 5-27, where the powertrain configurations are considered with different electricity mix – The average European electricity production mix. Figure 5-28 presents the average European production mix (2014). This mix contains different energy vectors (coal, natural gas, renewable energy, nuclear, etc.), with the repartition shown in Figure 5-28.

The evolution of the total well-to-wheel (WtW) CO₂ emissions is given as a function of the different powertrains (on board energy storage and conversion) configurations. There are min and max values, because of the variation of the efficiency of the onboard energy storage and conversion technologies combinations. The average value represents the average technology for each category.

In terms of WtW CO₂ emissions (Figure 5-25 and Figure 5-26), gasoline internal combustion engine (ICE) vehicles are evaluated to 123 g CO₂/km. As the Diesel internal combustion engine (ICE) presents better onboard conversion efficiency that the gasoline ICE, the Diesel engine vehicles are evaluated to 109 g CO₂/km. The ICE vehicles are not related to the electricity grid. The introduction of Plug-In hybrid electric (PHEV) technology connects the vehicles to the electricity grid. The WtW CO₂ emissions of those vehicles are dependent of the electricity production mix. For the case of low carbon electricity production mix (Figure 5-25 Figure 5-26), the PHEV reduce their WtW CO₂ emissions twice in comparison with the gasoline ICEV to 57 g CO₂/km. This is due to the improvement of the Tank-to-Wheel (TtW) efficiency of the energy storage and conversion due to the hybrid electric technology. At the same time the use of the PHEV with the European electricity mix (EU Mix) shows 90 g CO₂/km. The last value is higher than 57 g CO₂/km. The same trend of electricity mix dependence is observed for the battery electric (BEV). The composition of the electricity production mix reflects directly the WtW CO₂ emissions. Used with low carbon electricity (Figure 5-25) the electric vehicles emit 15 g CO₂/km and 0 g CO₂/km is electricity from renewables is used. If the BEV are used with the average EU mix, the emissions are 76 g CO₂/km. This value is 5 times higher in comparison to the BEV emissions in the French contexts (Figure 5-25) and is it as well higher in comparison of the PHEV used in the low carbon electricity production mix. The emissions of the battery electric vehicles (BEV) vehicles remain lower than the internal combustion engine (ICE) vehicles. In the case of full power FCEV (130 kW of PEM fuel cell as prime mover), the production of hydrogen from methane steam reforming is not carbon free. Thus, the emissions of full power fuel cell electric vehicles (FCEV), powered with H₂ from methane steam reforming (MSR) are 125 g CO₂/km. This value is equivalent to the CO₂ emissions of the ICE vehicles. The way to produce hydrogen impacts strongly the WtW emissions. The hydrogen produced from water electrolysis, powered with low carbon electricity (French mix, Figure 5-25) reduces the CO₂ WtW emissions with more than 50% to 51 g CO₂/km. The use of renewable electricity for the electrolysis is the best configuration for almost zero CO₂ emissions, 3 gCO₂/km. On the other hand, if the hydrogen (H₂) is produced from water electrolysis but powered with European electricity production mix (EU Mix) (Figure 5-28), the emissions are higher - 245 gCO₂/km. This value is twice higher than the use of H₂ produced from methane steam reforming and respectively higher than the ICE vehicles. The clean production of H₂ is important for the reduction of the WtW CO₂ emissions. The introduction of the fuel cell as a range extender for the electric vehicles (5-30 kW of fuel cell and 20 kWh of Li-Ion battery) leads to reduction of the size of the fuel cell and respectively the consumption of the hydrogen. This has as well a benefit for the total cost reduction. The fuel cell technology remains an expensive technology and the implementation of fuel cell module as a serial range extender for electric vehicles allows maintaining a good range of performances and acceptable cost.
The lower H₂ consumption in this configuration of range extender explains the lower CO₂ emissions, for the hydrogen obtained from methane steam reforming (MSR) and from water electrolysis. The electricity to charge the high voltage battery is as well considered. BEV with range extender of fuel cell emits 44 gCO₂/km and 24 gCO₂/km if the hydrogen is produced from water electrolysis. The part of electricity to charge the battery of the electric vehicles is accounting and the WtW emissions are 11 gCO₂/km. The impact of the electricity production mix in this case is very strong. This is visible especially for the hydrogen produced by water electrolysis. For the hydrogen produced from water electrolysis using European electricity, and used in a range extender fuel cell vehicle where we obtain 115 gCO₂/km. This is in the same order of magnitude that the ICE vehicles. For a fuel cell range extender vehicle using hydrogen produced from the water electrolysis and French electricity mix we obtain 24 gCO₂/km.

## Conclusions

As main conclusion, the emissions depend of the efficiency of the conversion chain. The total WtW CO₂ are strongly influenced by the electricity production mix. Fuel cell and electric powertrains technologies 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 the global WtW CO₂ efficient solution.
# Chapter 6 Conclusions

This report presents my research, related to the field of energy storage and conversion, with applications in the vehicles energy systems. The research is based on modeling, optimization but also experiments. The work is organized around three main research domains. The conclusions are organized according to the main research domains, followed by general conclusion, opening the perspectives of the research.

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

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 – organic Rankine cycle (ORC).

The main conclusion coming from the driving cycles evaluation is that 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.

The optimal environomic 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 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. 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.

A 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 actuators and the robustness of the control law are important parameters for the integration of the actuators in the engine cylinder head. The electromagnetic actuators give the possibility to manage the engine gas exchange on a very performant way. 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 requirements during
the valve landing is an important step and insures the respect of the noise criteria and the mechanical
durability of the system.

The cylindrical batteries allow to build a very wide range of different battery package. Such battery
package can be further connected in a modular systems e.g. for the big energy storage systems. The
investigation of a heat generation in modular system is very important. 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 18650 type 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 QGEN 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.

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

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 tests results, the tracking performance is 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
environomic 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. 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.

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

Optimal conditions and catalysts are researched for hydrogen production from bio-ethanol using ethanol steam reforming mechanism. In the studied concept the ethanol steam reforming is integrated in a SOFC in a serial hybrid architecture.

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.

The 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. This unit delivers 23.3 MJ of chemical energy per kilogram of bio-ethanol. In conclusion, it is proposed the working principle of the bio ethanol fuel cell for range extender unit for vehicle application with 15.15 MJ of electric energy output per kilogram of converted bio-ethanol.

It presents the following advantages to be used to power electric vehicles:

* Long lifetime

* Low costs of the electrodes

* Gases produced in SRE process can directly be used to power fuel cell stack

* High fuel efficiency

* Various compounds could be used to power fuel stack even natural gas

* Chemical stable of the solid electrolyte

* Low cost of raw materials for the electrolyte production

Disadvantages of using SOFC to power electric vehicles:

* High operating temperature

* Long start-up

* Mechanical and chemical compatibility issues

Well-to-Wheel (WtW) CO₂ emissions depend of the efficiency of the conversion chain, but 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 hydrogen production. The production of electricity or hydrogen from renewable resource is a viable solution for the WtW CO₂ efficient solution.

## 6.4 General conclusion of the research work

The design of efficient energy systems for vehicles is complex and presents a challenge for the engineers. This task requires the development of design tools taking into account energetic, economic and environmental aspects simultaneously. The target for the automotive engineers is to assess the potential of the different technology options for vehicle energy systems and to propose to the decision makers the solutions presenting interest for future research and development. The main interest is to include this systematic approach in the early design stage of the vehicles development process and thus to optimize the financial efforts in terms of R&D and industrial investment. The vehicles are part of the global energy system and deliver energy services. The new extended limits of the system have to be considered in the method.

The generic method for environomic design of vehicle grid related energy systems is summarized in Figure 6-1.

<figure><img src="image_81.png" /><figcaption>Figure 6-1: method for environomic design of vehicle grid related energy systems</figcaption></figure>

The future technologies for sustainable mobility are the electrified vehicles (electric and hybrid electric vehicles) and fuel cell propulsion vehicles. The fuel cell vehicles have to use green hydrogen or liquid fuels that are rich to hydrogen such as the bioethanol. The electricity used for mobility needs has to be green and produced from renewables. Hydrogen is as well used as energy storage for renewable intermittent energies. The methanation permits to link the natural gas grid and the hydrogen grid. The enlarged energy grids are the stockers for the intermittent energies. The electric machines and the fuel cell are the converters on vehicle onboard to move the vehicles on a sustainable way. For that, it is needed to apply systematic method for evaluation. The research perspectives set the future axes of the research.
# Chapter 7 Research perspectives

## 7.1 Goals and philosophy

The general goals of the research program are based on energy, processes and environment core science and defined as an investigation of integrated processes and energy conversion systems that integrate renewable energy resources for urban environments. The integrated energy systems supply multiple energy services and are a key issue for our energy future. New materials are also needed for the energy harvesting, conversion, storage, efficiency, as well for the environmental protection and reparation. The development of advanced computer aided methodologies combining thermodynamics and optimization techniques help engineers in the development of more efficient and more sustainable processes and energy conversion systems that allow important energy and emissions savings. The research field of “Energy, processes and environment”, applied to urban energy systems, combines process integration techniques, exergy analysis, process modelling and thermo-economic optimization. Process synthesis methodologies are also used, as an application and development of computer aided process engineering methods: data treatment, simulation, process integration, mixed integer linear and nonlinear programming, and multi-objective optimization. The research field of urban energy systems combines multiple disciplines, on the current state of energy related research including impacts of energy use and conversion on critical resources such as water, or on climate change. Life cycle assessment methods are used for the environment impacts estimations.

The research program is proposed to be established on the described field of “Energy, processes and Environment” by developing concepts and solutions for energy integration in the urban, industrial and also the transportation sectors. Experimental tests on the advanced technologies for energy integration can be performed in the laboratories for the validation of the models. Energy planning activities can be also generated and developed as an additional axis in the research program.

## 7.2 Research axes

An application example is the energy integration in the urban systems. The activities in the field of urban systems concern large-scale integration of energy systems using networks (heating, cooling, hydrogen, water, CO2), considering simultaneously the available (renewable) energy resources, the industrial processes and the buildings requirements. The system boundary extension is a natural evolution of the process system design application

Another example of application of the energy integration is the transportation sector.

The research focuses on a smart, green and responsible transportation for sustainable mobility. The mobility is important for the economic and social development of the population, but also has to respect the environment. The challenge for the future is to imagine and develop sustainable mobility in smart clusters. The transport means can be considered as autonomous or grid related. The energy grids are as well related. Figure 7-1 illustrates the integration of the energy grids of electricity, hydrogen and natural gas. For example, for the last 20 years the cities have been challenging the mobility paradigm, generated around cars as principle mode of transport. Many efforts from the research and the industry came to improve the efficiency of the vehicles (all types), their comfort and security. The modern vehicles are characterized by low emissions and diversification of the powertrain for the energy integration
adaptation. Today the vehicle paradigm is also being challenged by climate change and current natural resources deployment.

<figure><img src="image_82.png" /><figcaption>Figure 7-1: Technologies for storage and conversion of hydrogen between energy grids. Integration of energy grids and services.</figcaption></figure>

The energy grids of the different energy vectors are connected and integrated on a large-scale energy system, with different production, storage and distribution capacities (Figure 7-1).

The number of electric vehicles and hybrid electric vehicles increase in the mobility market. The range and the energy storage of the vehicle on board are important. The recharging of the vehicles and their relation to the energy grid became an important question. The energy system boundary can be extended to the vehicles and the grid system, including the chargers and it needs to be optimized. There is a wide spread use of new information and communication technologies for all means of transport. The real time information and the on demand transportation know rapid development. There is a change of focus from the development of the products –vehicles to development of infrastructures, related with the improvement and the integration of the management of the mobility services. Electric and hybrid electric vehicles and other advanced powertrain vehicles are promoted for usages in the cities and the extension of the charging points network is needed. The environmental and economic sustainability of the services is also required. The wish of the clusters to integrate sustainable mobility is clear and as conclusion the development and use of electric and hybrid electric vehicles especially for city mobility are recommended. However a real business model does not exist. Due to the high investment expenditure, new approaches to solve the problem are needed.

The research in the field of transportation engineering and transportation systems could be focused on the development of integrated approaches and testing of business models for the local production and distribution of energy (fuels, electricity, hydrogen) to create conditions for market take-up in urban and sub-urban areas, infrastructure deployment and adequate vehicles equipment. The approaches will conclude the system integration of the infrastructure with a large vehicle fleet.

## 7.3 From the method to the tool

The prospective method can serve to evaluate different options for the vehicles and the recharging (refueling) technologies related to the density of the recharging installation and the investment capacities. The connections with other industrial sectors are as well presented.
The optimization and simulation tool has to fulfill the following requirements:

* Enough flexible to simulate a wide range of conversion technologies with different level of detail

* Integrate a dynamic profile simulation and estimation of the system performances and resistance efforts

* Possibility to include fuels options, as energy vectors

* Define the size of the equipment

* Include economic models to deduce the cost of the equipment

* Include environmental life cycle assessment (LCA) based models to deduce the environmental impacts

* Give operation strategies possibilities

The tool can be used for scenario generation to research affordable and sustainable solutions for mobility.

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 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

## 7.4 Applications

The methodology is applicable in large domains of industrial fields: energy efficiency, process system design and energy systems design. The transportation category is related to all those categories.

The following applications can be developed:

* Assessment of the services that electric vehicles could provide to the electricity network

* Assessment of the strategies for the use of renewable energies in sustainable and profitable mobility scenarios.

* Assessment of the scenarios for the production, storage and distribution of clean hydrogen from biomass
The biomass is one of the main energy vectors for the production of clean hydrogen. The experimental research on the discussed concept of serial range extender with SOFC with integrated steam reformer can be continued. The main axes are presented below.

Bio-ethanol and conversion of the hydrogen:

* The future researches for the SOFC with integrated bio-ethanol steam reformer are:

* determination of the conditions of usage ethanol as a fuel for a SOFC fuel cell,

* developing new material for a SOFC dedicated for ethanol oxidation,

* testing SOFC materials (activity, stability etc.)

* testing the SOFC efficiency in the case of use ethanol as a fuel

The energy resources and the metal resources are scarce. A global optimization of the energy and the material demand is needed. The circular economy describes the usage, the recycling and the recirculation of the materials. The main question that the automotive industry has to solve is the development of adapted clean technologies. The automotive industry has to propose sustainable products and sustainable services for mobility. The automotive industry focuses on the development of multiple technologies, convenient for the different local markets and regulations. There is the need to plan and to develop key technologies and key activities.

In conclusion, globalization, urbanization, digitalization, scarcity of resources, renewable energies, and the growing demand for affordable mobility are leading to changed, much more dynamic market requirements and new business models.
# Appendix A Methodology

## A.1. Multi objective optimization and energy integration

### A.1.1. Multi-objective optimization

The optimization and simulation tool to be used in this thesis work has to fulfill the following requirements:

* Enough flexible to simulate a wide range of conversion technologies with different level of detail

* Integrate a dynamic profile simulation and estimation of the system performances and resistance efforts

* Possibility to include alternative fuels options

* Define the size of the equipment

* Include economic models to deduce the cost of the equipment

* Include environmental LCA based models to deduce the environmental impacts

* Give an operation strategies possibilities

The general computational framework of the environomic optimization (Figure A-1) has already been done and described in [116].

<figure><img src="image_83.png" /><figcaption>Figure A-1: Computational framework of environomic optimization.</figcaption></figure>

The computational platform combines the energy flow models and process/ design integration techniques to model the interactions between unit operations. The energy is exchanged between the unitary processes or design units. In a first time, the energy flow model calculates the energy transformations in the process/ design unit for a specific configuration and operating conditions. The resulting mass and energy flows are used to generate the energy integration model, with optimization of the heat and power production, by minimizing the total exergy and operating cost.

The thermodynamic states and flow rates of the energy-flow and energy integration models are used in a post- calculation phase to size the equipment, to estimate the cost and to evaluate the performance of the process or design configuration. The performance indicators can be used in the frame of multi- objective optimization framework, in which an evolutionary algorithm is implemented.
A set of decisions variables addressing technology choices and operating conditions of the process or
design units and utility system can be provided for this.

The environmental model consists in a life cycle inventory of the processes or design units. For each thermo-economic configuration of the system the life cycle impacts are evaluated. The life cycles impact can be also used as objective function for the optimization.

The basic methodology has for objective to implement systematically the vehicle LCA model to show the environmental life cycle performance and the economic models will be used to estimates the cost and the investments. The objectives are conflicting and can be considered in its multi-objective, environomic (i.e. energetic, environmental and economic) optimization.

A superstructure including the optional technological solutions and the potential resources is built and the thermo-economic models of these components are developed. First the three different sub-systems composing a vehicle system have to be simulated separately. These include:

* The mobility profiles and the energy services for the vehicle

* The energy resources

* The conversion technologies

Each model of the resource and of the technologies has to be included in the superstructure.

These sub-systems are then integrated using process integration techniques to build the complete energy conversion system. The process integration model is solved as a Mixed Integer Linear Programming (MILP) sub-problem, which decision variables are the utilization rates of the technologies in the superstructure during the different operating conditions.

The thermo-economic performances and Life Cycle Impact Assessment (LCIA) of the integrated system are calculated, based on the process operating conditions and on the system design.

The multi-objective Mixed Integer Non-Linear Programming (MINLP) problem is then solved using an evolutionary algorithm.

The general computational framework has already been adapted for vehicle applications in [2] and [3]. The superstructure contains a vehicle simulation models, validated with experiments. The models contain dynamic and thermal layouts. The cost equations are integrated in the economic model. The energy integration model uses the results from the dynamic and thermal flows calculations. The optimizer in OSMOSE is based on a genetic algorithm. This optimization technique is multi-modal and gives local optimums. The organic Rankine cycle (ORC) is considered as a utility for the energy integration. The ORC design is optimized according to techno-economic objectives. The optimization consists in four major parts – a master multi objective optimization (MOO), a thermo economic simulation (TES), a slave optimization (energy integration - EI). The techno-economic evaluation (TEE) is the last part. The life cycle impact assessment (LCIA) indicators are used as optimization objectives. They are implemented in the master optimization, according environmental optimization.

**Master Optimization:** The types and the size of the equipment are defined in the decision variables. An evolutionary algorithm (EMOO) solves the master optimization with 3 objectives: minimization of the fuel consumption, minimization of the investment cost and minimization of the environmental impacts (Figure A-2).

**Thermo-economic simulation:** This simulation occurs in a second step. The thermodynamic and economic state of the selected equipment is calculated.

**Economic evaluation:** The techno-economic evaluation (TEE) uses the selected superstructure in the master level and the result of the slave optimization phase, to calculate objective functions of the master optimization – cost, and CO₂ emission.

The results of multi-objective optimization converge to the Pareto frontier curve.
<figure><img src="image_84.png" /><figcaption>Figure A-2: Structure for multi-objective optimization</figcaption></figure>

**Slave optimization:** The energy integration calculates the best operating strategy of selected equipment by solving a mixed integer linear model (MILP). The aim is to minimize the total cost under the energy balance and the heat and power cascade constraints. The input data used in the energy integration includes the values of the master decision variables, the resources, the power dynamic profile and the comfort profile.

The energy integration is done in the second stage of the approach and this is a slave optimization based on mixed integer linear programming. The energy integration defines the potential of heat recovery and deduces the choice and the sizing of the adapted utilities. In the energy integration problem the engine and the vehicle cabin are defined as processes "cold" and "hot" streams between which a heat exchange has to occur. The minimum energy requirement of the system is then estimated and a utility is used to close the energy balance and estimate the energy recovery potential.

The utility system pertinent for energy recovery – for example the organic Rankine cycle (ORC) is defined in the energy integration module. In the EI the level of temperatures (evaporation and superheating) of the ORC are optimized to obtain the maximal net power output. The optimization is done for each operating point and each working fluid and comfort demand. The outcomes of this optimization are the ORC loop sizing (working fluid flow, sizes of the turbo machines and exchanged heat in the evaporation and the condensation) the efficiency and the net power output. The solutions converge on a Pareto curve. The number of the solution is identified by an ID number. Different ID of solutions of the Pareto curves are detailed in the chapters of results.

### A.1.2. Energy integration

The process optimization proposes a structured methodology to identify, characterize and quantify the waste heat of a system. The process integration (PI) methodology dates back to the 1970s and the first developments are in the heat integration.

The methodology is adapted and explained for vehicle and illustrated in Figure A-3.
<figure><img src="image_85.png" /><figcaption>Figure A-3: Architecture of the multi-objective optimization tool for vehicle applications</figcaption></figure>

The energy integration is the slave optimization part of the OSMOSE Superstructure. The energy integration calculates the best operating strategy of selected equipment by solving a mixed integer linear model (MILP) (Figure A-4). The aim is to minimize the total cost under the energy balance and the heat and power cascade constraints. The input data used in the energy integration includes the values of the master decision variables, the resources, the power dynamic profile and the comfort profile. To do the energy integration optimization a multi-periods representation of the operating profile is needed, reducing the number of variables and the calculation time. Figure A-4 illustrates the energy integration steps for vehicle energy system.

<figure><img src="image_86.png" /><figcaption>Figure A-4: Energy integration and multi-periods model</figcaption></figure>
# A.2. Economic models

## A.2.1. Vehicles economic model:

The cost of the vehicle is evaluated for every iteration as a function of the size and the efficiency of the energy converters and energy storage technologies. The cost of the equipment is directly related to the size of the components. In this study the efficiency improvement of the hybrid electric vehicle powertrain through PI is researched. The energy integration methodology is based on the heat integration, which targets the maximum heat recovery in the energy system. The approach consists in mapping all hot and cold streams in the system and displays the heat composite curve. The methodology gives the advantage to provide a global picture of the heat exchanges in the system. It also provides the MER – minimum energy requirement. MER is the minimum amount to be introduced or extracted from the system by means of cold or hot utilities. As a result, the utilities are selected and sized. Process integration techniques and energy analysis are used to measure the useful potential of the waste heat

The Table_A-1 summarizes the cost equations.

<table><caption>Table_A-1: Equations for the economic model [116]</caption><thead><tr><th>Components</th><th>Costs [€]</th><th></th></tr></thead><tbody><tr><td>Converters</td><td></td><td></td></tr><tr><td>Electric motor</td><td>30 [€/kW]*P<sub>EM</sub> [kW]</td><td>(1)</td></tr><tr><td>Thermal engine</td><td>15 [€/kW]*P<sub>TE</sub> [kW]</td><td>(2)</td></tr><tr><td>Fuel cell</td><td>1500 [€/kW]*P<sub>FC</sub> [kW]</td><td>(3)</td></tr><tr><td>Hybrid pneumatic system</td><td>6000 [€]</td><td>(4)</td></tr><tr><td>Storage system</td><td></td><td></td></tr><tr><td>Battery</td><td>600*[€/kWh]*q<sub>bat</sub><sup>0.2477*log(bat<sub>specifmass(battpe)</sub>+0.5126)</sup> [kWh]</td><td>(5)</td></tr><tr><td>Supercapacitor</td><td>15 [€/kW]*P<sub>SC</sub> [kW]</td><td>(6)</td></tr><tr><td>Body</td><td></td><td></td></tr><tr><td>Nominal cost (car shell)</td><td>17.3*car_shell mass[kg]-3905.4 [€]</td><td>(7)</td></tr><tr><td>Vehicle use in France 2013</td><td></td><td></td></tr><tr><td>Electricity household</td><td>0.14269 [€TTC/kWh]</td><td></td></tr><tr><td>Electricity industry</td><td>0.07768 [€TTC/kWh]</td><td></td></tr><tr><td>Gasoline</td><td>1.645 [€/L]</td><td></td></tr><tr><td>Diesel</td><td>1.451 [€/L]</td><td></td></tr><tr><td>Emissions</td><td></td><td></td></tr><tr><td>CO2 emissions</td><td>Taxes system bonus/malus</td><td></td></tr></tbody></table>

The cost of the electric motor considers the cost of the power unit. The battery cost depends on the battery type and the energy storage capability of the material. The fuel cell cost considers the cost of its adaptation in the powertrain. The price of the hybrid pneumatic system is considered to be 6000 euros and this value is including the hybrid air system adaptation in the vehicle and the powertrain and is based on the industrial experience on the hybrid propulsion systems and their adaptation in the vehicle.

The nominal cost represents the vehicle shell cost. The vehicle shell is the completely equipped vehicle without the powertrain components. This linear correlation (7), (Table_A-1) takes into account the price of the parts and the manufacturing cost of the vehicle shell. The margin of the carmaker is included in the price. The correlation is obtained by official commercial vehicles prices for customers for different vehicle classes. This correlation illustrates the link between the increasing cost and the increasing size and mass of the vehicle. For each calculation, a new vehicle mass is calculated and updated with the mass of the defined powertrain.
A simplified vehicle objective cost function is constructed, composed of the vehicle powertrain cost and the vehicle body cost during the production phase.

The economic indicators are defined by the following equations:

### Investment cost – CAPEX (Capital Expenditure)

This is corresponding to the investment price that the customer has to pay for a given vehicle definition. For low CO₂ emission, the environmental bonus is deduced from the investment cost of the customer.

The investment cost is given and named below as Cost<sub>vehicle</sub> in €.

$$ Cost_{vehicle} = Cost_{powertrain} + Cost_{car\_shell} - Bonus\_CO_2 \qquad (A-1) $$
in €

### Operating cost – OPEX

The operating cost is calculated based on 150000 kilometers on the different driving cycles.

$$ Cost_{operating} = Cost_{energy} * Energy_{consumption} * \frac{150000}{100} \qquad (A-2) $$
in €, Energy<sub>consumption</sub> in kWh/100 km

### Total mobility cost

The total mobility cost is composed from the investment and operating costs.

$$ Total_{cost\_mobility} = CAPEX + OPEX \text{ in €} \qquad (A-3) $$

### Actualized yearly cost

The actualization of the cost is defined on 10 years investment basis using an actualization rate of 4%. Then the yearly actualized cost (YAC) is computed for different vehicles according to the following formula.

$$ YAC = \frac{OPEX}{n} + CAPEX * \square \qquad (A-4) $$
$$ \square = \frac{i * (1 + i)^n}{(1 + i)^n \square 1}, i = 0.04, n = 10 $$

With OPEX- operating cost, CAPEX the investment price, i the actualization rate (4%, n the investment time (10 years).
## Economic model for waste heat recovery

As economic indicator, the total price of the ORC is defined. Here just the investment cost is considered. This cost is a linear function to consider the parts produced on large units' volume. The investment cost is the sum of the following equations (A-5) to (A-11):

$$Cost_{evaporator} = 18.84 Area_{evaporator} + 14.49, \text{ Euro, area in m}^2 \quad (A-5)$$

$$Cost_{condenser} = 4.01 Area_{condenser} + 4.96, \text{ Euro, area in m}^2 \quad (A-6)$$

$$Cost_{pump} = 0.083 flowrate + 165, \text{ in Euro, flowrate in kg.s}^{-1} \quad (A-7)$$

$$Cost_{turbine} = 0.015 Power_{turbine} - 30, \text{ in Euro, power in kW} \quad (A-8)$$

$$Cost_{pipe} = 26, \text{ Euro} \quad (A-9)$$

$$Cost_{fluid} = 10, \text{ Euro} \quad (A-10)$$

$$Cost_{ORC} = Cost_{evaporator} + Cost_{condenser} + Cost_{pump} + Cost_{turbine} + Cost_{pipe} + Cost_{fluid}, \text{ Euro} \quad (A-11)$$

## A.3. Environmental model: Life cycle assessment

The LCA method is applied as an indicator for the vehicle energy system design. The results of LCA are used to communicate on ecological labels and to demonstrate the CO₂ print improvement due to the introduction of new technologies like the electrified powertrains. The literature shows that the functional unit for LCA vehicle study is to transport persons on 150000 km for 10 years [116] and this functional unit is also used for the study.

This study refers to the 3 main indicators, used from the most part of the automotive industry, and applied by PSA – the GWP 100 years, the eutrophication, the acidification. The CML01\_short indicator is selected in the OSMOSE LCA model. The ozone depletion is also part of the CML01\_short indicator, which has 4 categories. They are summarized in the Table\_A-2.

<table><caption>Table\_A-2: Listing of impact categories implemented in cml01\_short</caption><thead><tr><td>Category</td><td>Definition</td><td>Unit</td></tr></thead><tbody><tr><td>Acidification potential, average European</td><td>H<sub>2</sub>CO<sub>3</sub> formation in oceans and on land by dissolving CO<sub>2</sub> in water</td><td>kg SO<sub>2</sub>-eq</td></tr><tr><td>Climate change, GWP 100a</td><td>Significant and lasting change in the statistical distribution of weather patterns over an extended period of time (100 years)</td><td>kg CO<sub>2</sub>-eq</td></tr><tr><td>Eutrophication potential, generic</td><td>Environmental response to addition of nitrates and phosphates. (CML01)<br/>-Mass of algae, phytoplankton ➡<br/>-Biodiversity desirable fish species ➡</td><td>kg PO<sub>4</sub>-eq</td></tr><tr><td>Stratospheric ozone depletion, ODP steady state</td><td>Ozone depletion</td><td>kg CFC<sup>-11</sup>-eq</td></tr></tbody></table>
The study is based on the life cycle assessment methodology steps, detailed largely in [138].

The methodology applied in the LCA module of OSMOSE is summarized in Figure A-5.

<figure><img src="image_87.png" /><figcaption>Figure A-5: Steps of the life cycle analysis, selection of the environmental impacts and categories</figcaption></figure>

The EcoInvent processes, equivalent to processes used by PSA with Gabby, for a vehicle with equivalent mass and composition to 3008 HY4 D-Segment serial vehicle, are identified.

The life cycle of a product, a system or a service contains three distinct successive phases: the production phase, the use phase and the end-of-life phase. The unitary processes and flow diagram for vehicles are defined in Figure A-6.

The functional unit for the LCA is the total distance driven throughout the vehicle's lifetime - 150000 km [139].

<figure><img src="image_88.png" /><figcaption>Figure A-6: Vehicle unitary processes flow chart – system definition</figcaption></figure>

The inventory in the production phase is composed of:

* the mass and material balance for each part of the vehicle sub-systems

* the corresponding manufacturing process in the EcoInvent® database

* the data about the manufacturing processes in the vehicle production plant (welding, painting, assembly)

The EcoInvent® database is used for sourcing of the unitary processes and the raw materials for the production of the parts. Seven substructures constitute the vehicle, on the way to identify the powertrain components: electric machine, low voltage battery, high voltage battery, power unit, thermal engine, gearbox, vehicle body (car shell).
# Appendix B Models

## B.1. Simulation models: EV, HEV, SOFC

The model components are modeled using differential equations. The links between the energy storage and conversion technologies constitute the powertrain architecture. The energy flow is computed from the wheels to the energy sources. This is called the backwards method to estimate the energy consumption (Figure_B-1).

<figure><img src="image_89.png" /><figcaption>Figure_B-1: Generic backwards simulation model</figcaption></figure>

**Driving cycle:** a predefined time - speed profile is the input of the model. Along the cycle, the profile defines the gear to be engaged.

**Vehicle model:** the vehicle model computes the traction force at the wheels $F_t$ at every time step of the speed profile. The power demand is so defined. The vehicle model takes into account the rolling resistance and the aerodynamic resistance. The uphill force is considered in case of driving a slope.

$$m * \frac{dv}{dt}(t) = F_t(t) - F_{resistance}(t) \quad \text{in N} \qquad (B-1)$$

The resistance force can be divided on four sub-forces:

The tire friction:

$$F_r = m * g * c_r \quad \text{in N} \qquad (B-2)$$

Where g is the Earth's gravitational constant in m/s², m is the mass of the vehicle and in kg $c_r$ is the rolling friction coefficient.

The wheel bearing friction

$$F_{bearing} = m * g * \mu * \frac{d}{D} \quad \text{in N} \qquad (B-3)$$

Where $\mu$ the bearing coefficient, D the wheel diameter in m is and the d the bearing bore diameter in m.
The aerodynamic losses

$$F_a = \frac{1}{2} * \rho_a * C_a * A * (v + v_{wind})^2 \text{ in N} \qquad (B-4)$$

Where $\rho_a$ is the air density in kg/m³, $C_a$ is the aerodynamic drag coefficient in [-], A is the vehicle front area in m², v is the vehicle speed and in m/s, $v_{wind}$ is the frontal wind speed in m/s.

The uphill force

$$F_{uphill} = m * g * \sin(\alpha) \text{ in N} \qquad (B-5)$$

Where $\alpha$ is the slope in rad.

Using the vehicle speed $v$, the traction force $F_t$ and the wheel radius one can compute the wheel torque and rotation speed and then determine the power required to move the vehicle.

**Transmission model:** A transmission is used between the wheels and the energy converter to adapt the speed and the torque levels. The major part of the vehicle transmissions are 5 and 6 speed manual gear boxes. The gear ratio is imposed just in the NEDC. More complex transmission such as continuously variable transmission (CVT) is also modeled. The advantage of the CVT is to choose the optimal fuel consumption gear ratio for every given drive profile. This is very suitable for customer's drives profiles.

**Energy converters:** The energy converter transforms the energy (chemical or electrical) from the energy storage unit into mechanical power. As the dynamics of these converters can be complex the modelling is simplified using efficiency maps. These maps are obtained by measurement on test bench and usually represent the conversion efficiency as a function of the load and the speed demand of the primary shaft. An example is given in Figure B-2:

<figure><img src="image_90.png" /><figcaption>Figure B-2: Efficiency map of a diesel engine</figcaption></figure>

Thus given that the speed and the torque of the primary shaft are known, the power that the energy storage unit has to provide is computed by a simple interpolation in the efficiency map.
## B.1.1. Electric vehicle model

Electric vehicles are characterized by an electric energy conversion chain upstream of the drivetrain. This energy conversion chain consists of a battery (or another electricity storage system) and the electric motor with its controller. The autonomy of the electric vehicle is directly related to the density and the size of the high voltage battery. The time required for charging is usually not negligible and surely it is larger than the typical refueling time of ICE based vehicles. Recently introduced passenger cars mostly use permanent magnets synchronous machines as traction motor, with peak power ranges from 50 to 80 kW. Lithium-ion battery energy typically ranges from 15 to 25 kWh. For combined driving cycles, the top speed and range of such cars now comfortably reach 135 km/h and 160 km autonomy. The electric consumption is typically measured in kWh/100 km or Wh/km. This depends on the efficiency of the powertrain components, mainly of the electric machine, the battery and the transmission. The electric machine acts in motor and generator mode and during braking the kinetic energy is recovered and stored in the high voltage battery as electricity. Typical values of the energy consumption of modern electric cars are 10-15 kWh/100 km for combined driving cycles. The electric vehicle is modeled with the quasi-static approach. While in ICE-based powertrains the power flows are mechanical and thermal in electric vehicle, the electric flow plays a fundamental role. The constituents ("power factors") of electric power are voltage and current. Since the former can only have positive values, it plays an analogous role to that of speed in mechanical power. The current can switch direction to represent both power flows from (positive current) or to the storage system (negative current). The negative current occurs during the regenerative braking. Thus current is the electric analogue of torque and force. The causality of power factors of an EV is listed in Figure B-3.

<figure><img src="image_91.png" /><figcaption>Figure B-3: Electric vehicle quasi-static model – BT- battery, PB – power link, EM- electric machine, GB- gear box, V- vehicle.</figcaption></figure>

The main characteristics of the electric vehicle model are given in Table B-1:

<table><caption>Table B-1: Electric vehicle main characteristics [116]</caption><thead><tr><th>Sub-System</th><th>Characteristics</th><th>Value</th></tr></thead><tbody><tr><td>Vehicle</td><td>Nominal mass [kg]</td><td>1195</td></tr><tr><td>Gear box</td><td>IVT efficiency [-]</td><td>0.9</td></tr><tr><td>Energy</td><td>Electricity</td><td></td></tr><tr><td>Electric motor</td><td>P<sub>EM</sub> [kW]</td><td>125</td></tr><tr><td rowspan="2">Battery</td><td>Type</td><td>Li-ion</td></tr><tr><td>Capacity [kWh]</td><td>19</td></tr></tbody></table>

## B.1.2. Hybrid electric vehicle dynamic model

The model is based on mechanical and electrical flows, modelled in SIMULINK. The temperature maps of the internal combustion engine are constructed from engine test bench measurement maps. The level of the model is quasi-static. The vehicle follows dynamic profiles. Time-speed driving profiles are generated from a library of driving cycles. The model has a loop control structure. The control is done on the required mechanical power needed to follow the dynamic cycle.

The main characteristics of the hybrid electric simulation model are given in Table B-2.
<table><caption>Table_B-2: D- Class hybrid electric vehicle characteristics [116]</caption><thead><tr><th>Sub-System</th><th>Characteristics</th><th>Value</th></tr></thead><tbody><tr><td>Vehicle</td><td>Nominal mass [kg]</td><td>1660</td></tr><tr><td rowspan="3">Gear box</td><td>CVT efficiency [-][91]</td><td>0.84</td></tr><tr><td>MGB efficiency [-]</td><td>0.95</td></tr><tr><td>6 gears</td><td></td></tr><tr><td rowspan="8">Engine</td><td>Displacement [l]</td><td>2.2</td></tr><tr><td>Number of cylinder</td><td>4</td></tr><tr><td>Rated power [kW]</td><td>120</td></tr><tr><td>Max. speed [rpm]</td><td>4500</td></tr><tr><td>Max. Torque [Nm]</td><td>380</td></tr><tr><td>Idle speed [rpm]</td><td>800</td></tr><tr><td>Idle fuel consumption [l/h]</td><td>0.33</td></tr><tr><td>Deceleration Fuel cut-off</td><td>Yes</td></tr><tr><td rowspan="3">Fuel</td><td>Type</td><td>Diesel</td></tr><tr><td>Density [kg/l]</td><td>0.84</td></tr><tr><td>Lower heating value [MJ/kg]</td><td>42.5</td></tr><tr><td>Electric motor</td><td>Power [kW]</td><td>27</td></tr><tr><td rowspan="2">Battery</td><td>Type</td><td>Ni MH</td></tr><tr><td>Capacity [kWh]</td><td>1.2</td></tr></tbody></table>

The model is based on a commercial D class diesel hybrid electric vehicle [116]. Some adaptations due to the optimization predisposal and the non-normalized driving cycle's evaluations are done.

Figure_B-4 illustrates the generic units that are modeled in the vehicle powertrain and the backwards approach to estimate the energy consumption.

<figure><img src="image_92.png" /><figcaption>Figure_B-4: Quasi-static model of the parallel thermal electric hybrid</figcaption></figure>
The simulation model is based on parallel hybrid architecture. Hybrid- electric vehicles differ also according to the degree of hybridization of the powertrain and the battery capacity. In the optimization part the battery capacity is one of the decision variables, thus the solutions are classified according to the functional classification in Figure_B-5.

<figure><img src="image_93.png" /><figcaption>Figure_B-5: Functional classification of HEVs in term of degree of hybridization and battery capacity [140]</figcaption></figure>

**Driving cycle:** the input of the model is a predefined discrete time and speed profile. The profiles used in this study are described in the study cases.

**The vehicle model** uses the traction force on the wheels, in order to find the power demand. It takes into account the rolling and aerodynamic resistance, the vehicle mass and the uphill force if driving on a slope.

**A transmission model** is used between the wheels and the energy converter to adapt the speed and the torque levels. The model has a manual gear box used for the simulations on harmonized driving cycles, with imposed gear ratios. For the usage driving cycles, when the gear ratio are unknown a CVT is used for the estimation of the optimal gear ratio for each point of the drive cycle.

The energy converter transforms the energy (chemical or electrical) from the energy storage into the mechanical power. The dynamics of such converters can be complex. Their modeling is simplified using efficiency maps, obtained from measurements from test benches.

The electric motor is considered in the model is a synchronous AC motor. The electromagnetic equations are not modeled, a black box approach based on motor efficiency being preferred. The inputs of the electric motor are its shaft's rotation speed $w_{EM}$ in rpm and torque $T_{EM}$ in Nm and the output is the power demand $P_{EM}$ in kW (Figure_B-6). Thus one can write for the positive and negative traction cases.

$$P_{EM}(t) = \frac{\omega_{EM}(t) * T_{EM}(t)}{\eta_{EM}(\omega_{EM}(t), T_{EM}(t))} \quad \text{for } T_{EM} \ge 0 \qquad (B-6)$$

$$P_{EM}(t) = \omega_{EM}(t) * T_{EM}(t) * \eta_{EM}(\omega_{EM}(t), T_{EM}(t)) \quad \text{for } T_{EM} \le 0 \qquad (B-7)$$
The efficiency values $\eta_{EM}(\omega_{EM}(t), T_{EM}(t))$ for $T_{EM} \ge 0$ are obtained from the electric motor efficiency map of the QSS toolbox [141]. As the efficiency of the electric motors is usually not measured in generator mode, it is proposed in [140] to approximate it by mirroring the power losses.

$$ \eta_{EM}(\omega_{EM} - |T_{EM}|) = 2 - \frac{1}{\eta_{EM}(\omega_{EM}, T_{EM})} \qquad (B-8) $$

The efficiency map is illustrated in Figure B-6:

<figure><img src="image_94.png" /><figcaption>Figure B-6: Two-quadrant electric motor efficiency map</figcaption></figure>

The model of the combustion engine is based on the fuel consumption map. The inputs of the model are the engine shaft speed $\omega_{ICE}$ and torque $T_{ICE}$ and the output is the break specific fuel consumption BSFC. An example of a fuel consumption map is given in Figure B-7:

<figure><img src="image_95.png" /><figcaption>Figure B-7: Fuel consumption map of a 2.2 liters Diesel engine</figcaption></figure>

The engine fuel consumption map is not scaled numerically. A new map is imported for each displacement volume.
Hybrid electric vehicle contains an electric motor powered from the high voltage battery, which may be assisted by supercapacitors when short power boosts for accelerations are demanded. In order to increase the vehicle autonomy, a thermal internal combustion engine is added. In this study a parallel hybrid electric powertrain is modeled. Its operating modes are the following:

* The internal combustion engine can drive the vehicle at high speed or when the battery charge is low.

* The electric motor can drive the vehicle when the battery is sufficiently charged to meet the power demand.

* The vehicle is driven by both simultaneously. The electrical motor and the ICE are connected to the drive shaft using a power split device.

* During deceleration phases the electric machine acts as generator and the electricity is stored in the high voltage battery. This mode is called regenerative braking.

The developed model is a parallel hybrid with diesel engine, electric motor and high voltage battery. The choice of diesel hybrid electric vehicles is done because of available data and models on commercial hybrid electric vehicles. The model can choose engines map for different displacements. The electric motor map and the battery are adapted from the QSS Tool Box [141]. Since in the quasi static model, the efficiency is interpolated in the electric machine map using $\omega_{EM}$ and $T_{EM}$, the electric motor is scaled, by multiplying the rotation speed and torque vectors of the map with the appropriate scaling factor:

$$f_{scaling} = \frac{P_{EM}}{P_{ref}} \qquad (B-9)$$

where $P_{ref}$ is the rated power of the electric motor for the original efficiency data.

In parallel hybrid vehicles, the power split device connects the combustion engine and the electric machine to the drive shaft. These devices can be generally torque or speed couplers. The torque sent towards the transmission is expressed as a weighted sum of electric motor torque and the combustion engine torque.

$$T_s = k_1 T_{EM} + k_2 T_{ICE} \qquad (B-10)$$

$$\omega_s = \frac{\omega_{EM}}{k_1} = \frac{\omega_{ICE}}{k_2} \qquad (B-11)$$

$k_1$ and $k_2$ are the structural parameters of the torque coupler

Respectively for the speed coupler, the rotational speed of the shaft, connected to the transmission is as the weighted sum of the electric motor and the combustion engine shaft speeds.

$$\omega_s = k_1 \omega_{EM} + k_2 \omega_{ICE} \qquad (B-12)$$

$$T_s = \frac{T_{EM}}{k_1} = \frac{T_{ICE}}{k_2} \qquad (B-13)$$

The design of the coupler is not the focus of this study, therefore the simplest solution is chosen with the following assumption shown in equation (B-14).

$$k_1 = k_2 = 1 \qquad (B-14)$$
### B.1.3. Waste heat recovery model :

Hybrid electric vehicles have two or more prime movers as energy converters (internal combustion engine (ICE) or fuel cell and electric machine) and power sources (fuel tank, hydrogen tank and electrochemical battery) on their board. The main motivation for hybrid electric vehicles (HEV) development is the efficiency increase of the powertrain in comparison to the thermal (gasoline or Diesel) engines powertrain. HEV offer as well long ranges of autonomy, comparable to thermal vehicles. HEV vehicles recover the kinetic energy during braking. However, the ICE still has thermal losses due to the waste heat when is operating. In this case, the recovery of the waste heat is possible by using an external cycle- the Rankine cycle. The ICE energy balance is showing that there are two main sources of heat: the water jacket and the exhaust gases [7], [8]. The levels of the temperature between the water jacket and exhaust gases are different from 80°C to 900°C. To value the low heat temperature, an organic fluid is proposed to be used as working fluid.

<figure><img src="image_96.png" /><figcaption>Figure B-8: ORC system to recover the waste heat of the ICE.</figcaption></figure>

An ORC presented in Figure B-8 will be investigated as principal utility for the energy integration. Its operation will be optimized for the operation points of the ICE, adapted for HEV. As shown in Figure B-8 the turbine is linked to an electric generator and so electricity is produced from the waste heat of the engine. The electricity can be then used for the battery filling or to be sent the electrical network of the system, driving different accessories, devices around the engine or the consumers on the vehicle board (Figure B-9). The ORC receives the heat from the water circuit and the gases of the exhaust of the engine. The turbine out is related to the high-speed generator which produces electricity. The electricity is converted into DC and to the adapted voltage level and enters into the high voltage battery. The functional architecture is presented in a Mild hybrid electric vehicle is presented in Figure B-9.

<figure><img src="image_97.png" /><figcaption>Figure B-9: Functional architecture of the ORC integration in a Mild HEV Architecture.</figcaption></figure>
### B.1.4. Actuator Nonlinear Model

The model determination of the actuator requires the study of its mechanical part and its electrical part. Thus, the nonlinear model of the actuator consists of 3 interconnected sub-parts (Figure_B-10), one relating to the electrical part of the system, one of its electromechanical part and one relative of its mechanical part.

<figure><img src="image_98.png" /><figcaption>Figure_B-10: Block diagram of the nonlinear model of the actuator</figcaption></figure>

The electrical part takes into account the electric circuit of the actuator. From the voltage across the actuator ($U_{act}$) and the electromotive force of the actuator (voltage $U_V$), it is possible to express the current $I$ passing through the actuator.

The Electromechanical Part models the connection between the electrical and mechanical part of the actuator. By considering the limited operating domain of the valve, the model of the force produced by the electromagnetic actuator defined by (1) can be reduced to a fifth order polynomial. The Mechanical Part allows a simplified modelling of the mechanical part of the actuator (Figure_B-11).

<figure><img src="image_99.png" /></figure>

<figure><img src="image_100.png" /><figcaption>Figure_B-11: Mechanical part of the actuator and lift profile Zref to be ensured for a mechanical valve train and an engine speed of 6000 rpm</figcaption></figure>

Newton's second law allows taking the balance of the forces having an influence on the movement of the valve:

$$F_{\text{act}}(t) - F_{\text{frict}}(t) - F_{\text{pres}}(t) - F_{\text{visc}}(t) + mg = m \frac{d^2 Z(t)}{dt^2} \qquad (B-15)$$

where $m$ is the total mass of the moving part of the actuator, $mg$ is its weight, $F_{frict}$ is the friction force at the valve guide, $F_{visc}$ is viscous force and $F_{pres}$ is the force exerted by the pressure difference between the gases in the cylinder and the exhaust or intake gases. The direction of the current defines the sign of the force of the actuator. When the current is positive, the actuator force opens the valve, the negative current closes the valve.
Experimental testing bench

For an easy study of the actuator and rapid implementation and validation of the control law, which is
to be designed, a dSpace prototyping electronic unit has been chosen. Figure_B-12 presents the block
diagram of the actuator control set-up that is implemented. Taking into account several aspects which
affect the experimental set-up, a sampling time T_s = 50µs is chosen. The control software receives the
position sensor voltage through and generates current commands for the power-electronic unit which
converts the requested current to the control voltage U act which is applied to the actuator by means of a
PWM technique with 20 kHz frequency. Due the new vehicle battery specification limit, the magnitude
of the control voltage is limited to 48 V and the current to 90 A.

<figure><img src="image_101.png" /><figcaption>Figure_B-12: Block diagram of the actuator control implementation set-up</figcaption></figure>

**B.1.5. Recharging model for batteries:**

Figure_B-13 presents the study case that considers the modelisation. A fleet of electric vehicles enters on the highway. The vehicles have different levels of charging of the batteries and different capacities, all vehicles want to perform long drives (n> 500 km). The road has different gates with numbers (1 to 6) (Figure_B-13), which allow the vehicles to leave the highway. Charging stations are located along the highway – A, B, C (Figure_B-13). The vehicles can stop on the charging stations and charge fully or partially their batteries. The following simulation is applied.

The inputs in the described model are:

* the battery capacity of the electric vehicles: $Q_{bat}$ in kWh

* the maximal power of the charging: $P_{charging}$ in kW

* the number of the In-/ Outcomes from the highway: $N_{in}$, $N_{out}$ in [-]

* the SoC of every electric vehicle on the Income: $SoC_{in}$, in [-]

* the SoC of every electric vehicle on the Outcome: $SoC_{out}$, in [-]

* the departure time of every vehicle, $t_{departure}$, in [h]
<figure><img src="image_102.png" /><figcaption>Figure_B-13: Charging on the highway: use case for long way drives.</figcaption></figure>

The variable is the quantity of the charged energy for every vehicle and for every station. The charging energy is different for every couple electric vehicle- charging station, and depends on the quantity of the compensation of the SoC difference between the initial and final state of the vehicle.

The outputs are:

* the total time of charging of every vehicle to run the distance, $Time_{charging\_i}$ [h]

* the total time of charging for the fleet, $Time_{charging\_fleet}$ [h]

* the waiting time for every vehicle, $Time_{waiting\_i}$ [h]

* the needed power for every station and for the highway $P_{charging\_i}$ in [kW]

The objective is to evaluate the power that is needed to be delivered by the charging stations, the waiting time and the total time of charging as a function of the vehicles numbers in the fleet and the distance to run. The optimization is performed according to the following criteria:

* minimize the energy quantity of the charge, $E_{charging}$ in kWh

* minimize the number of stops, $N_{stops}$ in [-]

* minimize the waiting time, $Time_{waiting\_i}$ [h]

* minimize the investment cost of the infrastructure, $Cost_{investment}$ in [€]

The flow is considered optimal if the vehicles charge:

* the minimal quantity of charged energy, $E_{charging}$ in kWh

* with the minimal number of stops, $N_{stops}$ in [-]

* minimizing the waiting lines, $Time_{waiting\_i}$ [h]

* Thus, the optimization problem is defined in (B-16) as:

$$ \min(\text{Time}_{\text{waiting}}(x)), x \in X_{\text{decision\_variables}} \qquad (B-16) $$
<figure><img src="image_103.png" /></figure>
# Appendix C Energy efficiency, storage and conversion

<table><caption>Table_C-1: Fuel cells types and their advantages and disadvantages to power electric vehicles</caption><thead><tr><th></th><th></th></tr></thead><tbody><tr><td><strong>Advantages of using AFC</strong><br>Low operating temperature<br>High efficiency<br>Quick start-up</td><td><strong>Advantages of using PEMFC</strong><br>Low operating temperature<br>Practical efficiency 50-60%</td></tr><tr><td><strong>Disadvantages of using AFC</strong><br>carbonate formation during operation of the fuel cell which block the electrodes material<br>the need to use pure oxygen and hydrogen corrosive solution of e.g. KOH<br>Application of high purity reagents<br>generates additional technological steps<br>in order to produce electricity to power Fuel cells (high operating costs)</td><td><strong>Disadvantages of using PEMFC</strong><br>Material of the cathodes<br>High purity hydrogen and oxygen (less than 10 ppm of carbon monoxide used in the feed gases)</td></tr></tbody></table>

<figure><img src="image_104.png" /></figure>

1. Hydrogen fuel is channeled through field flow plates to the anode on one side of the fuel cell, while oxygen from the air is channeled to the cathode on the other side of the cell.

2. At the anode, a platinum catalyst causes the hydrogen to split into positive hydrogen ions (protons) and negatively charged electrons.

3. The Polymer Electrolyte Membrane (PEM) allows only the positively charged ions to pass through it to the cathode. The negatively charged electrons must travel along an external circuit to the cathode, creating an electrical current.

4. At the cathode, the electrons and positively charged hydrogen ions combine with oxygen to form water, which flows out of the cell.
# C.1. Optimal designs for efficient mobility service for hybrid electric vehicles

The improvement of the efficiency of vehicle energy systems promotes an active search to find innovative solutions during the design process. Engineers can use computer-aided processes to find automatically the best design solutions. This kind of approach named "multi-objective optimization" is based on genetic algorithms. The idea is to obtain simultaneously a population of possible design solutions corresponding to the most efficient energy system definition for a vehicle. These solutions will be optimal from technical, economic and environmental point of view. The "genetic intelligence" is tested for the holistic design of the environomic vehicle powertrain solutions. The environomic methodology for design is applied on D-class hybrid electric vehicles, in order to explore the techno-economic and environmental trade-off for different hybridization level of the vehicles powertrains. For powertrain efficiencies between 0.25 and 0.35 the electrification of the powertrain reduces the global CO₂ emissions. Hybrid electric and plug-in hybrid electric vehicles are reaching these levels. The break point of the electrification effect on the GWP occurs on 0.35 % of powertrain efficiency. For battery capacity value higher than 13 kWh the global reduction of the CO₂ emissions is not obvious. The method gives also an overview of the evolution of environmental categories indicators as a function of the cost of the vehicles. A direct relation links the economic and the environmental performances of the solutions.

Environmental model and economic model are described in Appendix A.

Commercial vehicles are characterized on the normalized driving cycle – New European Driving Cycle (NEDC). Table C-2 summarizes the characteristics of the NEDC, which is well known and well referenced.

<table><caption>Table C-2: Drive cycles characteristics.</caption><thead><tr><td>Cycle</td><td>Distance (km)</td><td>Duration (s)</td><td>Average speed (km/h)</td></tr></thead><tbody><tr><td>NEDC</td><td>11.023</td><td>1180</td><td>32.26</td></tr></tbody></table>

## C.1.1. Results - multi-objective environomic optimization

### Definition of the optimization problem:

A hybrid vehicle characterized with multiple propulsion systems can operate them independently or together. The model contents are the electric machine, battery, supercapacitors, thermal engine and fuel tank, with diesel fuel. The thermal electric hybrid powertrain model characteristics are given in Table B-2. The model represents a commercial D-class vehicle with a parallel thermal (diesel) and electric powertrain (Figure C-1). The target is to size the components of the hybrid powertrain: the converters and the storage tanks and to evaluate on a simultaneous way, the environmental and the economic impacts of the solutions.

A multi objective optimization with three objectives is considered to define design solutions optimal from efficiency, economic and environmental point of view.

For every iteration of the model, the mean powertrain efficiency in traction mode is calculated according equation (C-1):

$$ \eta_{powertrain} = mean\left(\frac{P_{wheel}}{P_{fuel} + P_{BT} + P_{SC}}\right) \qquad (C-1) $$

Where PBT and PSC are respectively the battery and the super capacitors powers in kW and P<sub>wheel</sub> is the power on the wheels in kW.
The vehicle cost is computed for each set of the decision variables, according equation (A-1). The GWP is the category considered as environmental objective. The GWP has to be minimized. The GWP objective function for the environomic optimization considers the equivalent CO2 emissions during the vehicle life cycle (production, use phase). It is defined over the life cycle functional unit of 150000 km. The end of life is neglected, because of the high recycling ratio in the automotive industry and the consideration that the high voltage battery has a second life as storage device in the electricity distribution grid.

The equation (C-2) defines the GWP objective function:

$$GWP_{total} = GWP_{production} + GWP_{use\_phase} \quad \text{in kg. CO}_2 \text{ eq.} \qquad (C-2)$$

In the case of hybrid electric vehicles, the use phase includes the GWP due of the CO2 tank-to-wheels emissions emitted by the ICE during the vehicle operation over 150000 km.

<figure><img src="image_105.png" /><figcaption>Figure C-1: Parallel hybrid electric architecture: FT – fuel tank, ICE – internal combustion engine, BT – high voltage battery, SC – super capacitor, PE – power electronics, M- electric motor, PSD – power split device, G – electric generator, C1- clutch 1, C2- clutch 2, T- Transmission, D- Differential.</figcaption></figure>

The use phase contains also the GWP impact of the production of the energy vectors for charging the vehicles storage tanks – the diesel for the fuel tank and the electricity for the charging of the high voltage battery, over 150000 km. This is adding the well-to-wheels aspect of the study. The impact of electricity is considered only for the plug-in hybrid electric vehicles and the range extender vehicles. This means for vehicles equipped with high voltage battery capacity superior to 3 kWh. On that way, equation (C-2) is detailed in equation (C-3).

$$GWP_{wheel-to-wheel} = GWP_{total} = GWP_{vehicle\_production} + GWP_{tank-to-wheel\_CO2} + \text{kg.CO}_2 \text{ eq.} \qquad (C-3) \\ GWP_{diesel\_production} + GWP_{electricity\_production}$$

The environomic optimization is defined in equation (C-4):

$$\min(-\eta_{powertrain}(x)) \text{ Investment}_\cos t(x), GWP_{total}(x)), \quad x \in X_{\text{decision variables}} \qquad (C-4)$$

The other three categories of the impact are introduced as well, as environmental objectives to be minimized. Equations (C-2) to (C-4) are valid also for the other categories.

The decision variables for the powertrain design are defined in Table C-3:

<table><caption>Table C-3: Decision variables for powertrain design</caption><thead><tr><td>Decision variables for design</td><td>Range</td></tr></thead><tbody><tr><td>ICE displacement volume [l]</td><td>[0.8-1.4-1.6-2.2]</td></tr><tr><td>Electric motor rated power [kW]</td><td>[1-150]</td></tr><tr><td>Battery energy [kWh]</td><td>[5-50]</td></tr><tr><td>Number of super capacitors [-]</td><td>[1-10]</td></tr></tbody></table>
Multi objective environomic optimization

The solutions of the three objective environomic optimization converged on a Pareto Frontier optimal curve. They are projected in the 2D total GWP –powertrain efficiency vision (Figure_ C-2). This represents the trade-off between the energy consumption and the total GWP impact of the vehicles. From this representation, it is visible that the GWP decreases with the powertrain efficiency. This is due to the reduction of the CO2 emissions during the driving.

<figure><img src="image_106.png" /><figcaption>Figure_ C-2: Pareto curve – total GWP to powertrain efficiency, investment cost in color bar, NEDC.</figcaption></figure>

For powertrain efficiencies between 0.25 and 0.35 the electrification of the powertrain reduces the global CO2 emissions. This corresponds on the families of hybrid electric and plug-in hybrid electric vehicles. The break point of the electrification effect on the GWP occurs on 0.35 % of powertrain efficiency. This corresponds on a battery capacity higher than 13 kWh. From this battery, capacity value the global reduction of the CO2 emissions is not obvious.

Figure_ C-3 illustrates the correspondence between the high voltage battery capacity and the hybridization ratio of the vehicle. The hybridization ratio is defined as the as the ratio between the electric power and the total power and represents the power contribution of the electric side of the powertrain.

<figure><img src="image_107.png" /><figcaption>Figure_ C-3: Correspondence between the high voltage battery capacity and the hybridization ratio.</figcaption></figure>

The linear fit between the GWP and the powertrain efficiency is illustrated in Figure_ C-4.
<figure><img src="image_108.png" /><figcaption>Figure_C-4 is defined according to the linear equation (C-5). This equation is valid for the domain 25% -50% of powertrain efficiency. A quadratic utility function with balanced weight of the coefficients a and b between the cost and the powertrain efficiency is applied on the Pareto solution from Figure_C-2. The maximum of the utility function is obtained for points concentrated around values of powertrain efficiency of 35% and investment cost of 45000 € (Figure_C-4 a and Figure_C-4 b).</figcaption></figure>

The positive quadratic utility function with balanced techno-economic coefficients shows that utility maximums are in the PHEV zone, between 30% and 35% of powertrain efficiency (Figure_C-4).

$$GWP = 48749 - 59592*\eta_{\text{powertrain}} \quad \text{in [kg CO}_2 \text{ eq.]} \qquad (C-5)$$

The total GWP is also related to the investment cost.

<figure><img src="image_109.png" /><figcaption>Figure_C-4Figure_C-4 proposes a macroscopic linear fit of the relation between the total GWP and the vehicle investment cost. The relation is given in equation (C-6). The relation is valid in the domain of 25%-50% of powertrain efficiency.</figcaption></figure>

$$GWP = 38428 - 0.18267* \text{Investment\_cost}_{\text{vehicle}} \quad \text{in [kg CO}_2 \text{ eq.]} \qquad (C-6)$$
<figure><img src="image_110.png" /><figcaption>Figure C-4: Evolution of a) the total GWP as a function of the vehicle investment cost b) the investment cost as a function of the powertrain efficiency.</figcaption></figure>

The total GWP decreases with the increasing of the total investment cost. Vehicles with higher powertrain efficiency require higher investment cost. Thus they are less fuel consuming in the operation phase and emit less CO₂ emissions. One can consider that if one maximizes the powertrain efficiency one minimizes the total GWP. The GWP can be considered as an indicator related to the other 2 objectives. This allows simplifying the optimization problem from 3 dimensional to 2 dimensional. The techno-economic optimization brings also optimal environmental solutions in the defined range of decisions variables for hybrid electric vehicles and so defines environomic solutions. The main interest of this conclusion is to simplify the optimization from 3D to 2D techno-economic with activated environmental model, which allows evaluating the environmental impacts of each solution of the techno-economic Pareto curve. The vehicle use phase (including the operation CO₂ emissions and the emissions due to the energy vectors production) is clearly the major contributor to the total equivalent CO₂ emissions, in comparison of the equivalent CO₂ emissions for the vehicle production phase, for powertrain efficiencies between 25% and 35%. The design choices are visible on the impacts of the production phase. With the increasing of the powertrain efficiency over 35% and respectively the hybridization ratio (heavy plug-in hybrid electric vehicles and range extenders) and the size of the electric part of the powertrain, the impact of the vehicles production phase increases. This is due to the increasing of the mass of the materials needed for production of the high voltage battery and the electric machine. Orders of magnitude for the total GWP evolution and the repartition of the impact for the different subsystems and for the production phase are given in Figure C-5 for different sizes of high voltage battery –this means for different hybridization ratio. The major impact is due to the body production. The second contributor to the GWP is the production of the high voltage battery and its part increases with the increasing of the on board battery capacity. With the increasing of the electrification of the powertrain, the vehicle mass increases and so the power range of the machine and the associated power electronics also increases. Thus the part production impact of the electric machine and the power electronics increases. As the thermal engine is downsized, its impact decreases with the increasing of the hybridization ratio. The environmental model uses the CML short impact as explained Appendix A.
<figure><img src="image_111.png" /><figcaption>Figure C-5: GWP contribution for the production phase of the vehicles sub-systems a) Full ICE, b) 3 kWh of HVB, c) 7 kWh of HVB, d) 13 kWh of HEV</figcaption></figure>

Orders of magnitude for the total GWP evolution and the separation of the impact of the different life cycles phases are given in Figure C-6 for different sizes of high voltage battery –this means for different hybridization ratio. The vehicles are considered to be operated in France with European diesel and French electricity mix production. This means that the emissions due to the energy vectors are thus estimated for an optimistic scenario.
<figure><img src="image_112.png" /><figcaption>Figure_C-6: Evolution of the total GWP and repartition of the contribution of reach phase as a function of the hybridization ratio, D -Class vehicles</figcaption></figure>

The operation of the Plug -In vehicles in countries with high carbon percentage use in the electricity generation (Germany, Poland, and China) will increase the contribution of the equivalent CO2 emissions, coming from the electricity generation (Figure _C-7). The functional unit is 150000 km.

<figure><img src="image_113.png" /><figcaption>Figure_C-7: Use phase: GWP evolution as a function of the hybridization ratio and contribution of the energy vectors (diesel and electricity production)</figcaption></figure>