# Power management strategies of hybrid storage system supplying electric vehicle

Conference Paper · June 2022

DOI: 10.1109/MED54222.2022.9837189

CITATION

1

READS

83

6 authors, including:

<figure><img src="image_1.png" /></figure>

Laid Degaa

Ecole Supérieure des Techniques Aéronautiques et de Construction Automobile

25 PUBLICATIONS

120 CITATIONS

SEE PROFILE

<figure><img src="image_2.png" /></figure>

Nassim Rizoug

Ecole Supérieure des Techniques Aéronautiques et de Construction Automobile

33 PUBLICATIONS

443 CITATIONS

SEE PROFILE

<figure><img src="image_3.png" /></figure>

Imen Jarraya

Prince Sultan University

11 PUBLICATIONS

71 CITATIONS

SEE PROFILE

<figure><img src="image_4.png" /></figure>

Sara Daas

Badji Mokhtar - Annaba University

11 PUBLICATIONS

44 CITATIONS

SEE PROFILE
Power management strategies of hybrid storage
system supplying electric vehicle

DEGAA Laid

Ecole Supérieure des Techniques
Aéronautiques et de Construction
Automobile, France
France
laid.degaa@estaca.fr

JARRAYA Imen

National Engineering School of Sfax,
ENSIT
Tunisia
imenjarraya85@gmail.com

RIZOUG Nassim

Ecole Supérieure des Techniques
Aéronautiques et de Construction
Automobile, France
France
Nassim.rizoug@estaca.fr

DAAS Sara

Faculty of Engineering
Badji Mokhtar University, Annaba,
Algeria.
sara.daas@yahoo.com

LAROUCI Cherif

Ecole Supérieure des Techniques
Aéronautiques et de Construction
Automobile, France
France
cherif.larouci@estaca.fr

Achraf Jabeur TELMOUDI
University of Tunis
Tunisia
achraf.telmoudi@ieee.org

**Abstract**— Battery electric vehicles (BEV) initially appeared as a promising solution against climatic disasters due to petroleum vehicles and the continuous growth in energy demand for road transport. However, their weak autonomy combined with too high a cost slowed down their development in the world market. However, a second transition to multi-source electric vehicles which consist of a set of Energy Storage Systems (ESSs), may be possible solutions to improve the vehicle's autonomy and the duration of battery life by significantly reducing polluting emissions to the environment. In fact, this paper presents a hybrid SSE which is mainly based on High energy lithium-ion (Li-ion) batteries and High power batteries for powering an all-electric vehicle. This hybridization of a main High energy Li-ion battery source with the secondary High power batteries source adds greater HEV autonomy but it increases the complexity of the Energy Management System (EMS). In this work, the main objective is designed to simulate the strategies based on deterministic rules like the filtering method (MF) and the Limitation method (ML) for running electric vehicle with hybrid source in real time. Strategies ML and MF were chosen in the first place thanks to their simplicity in time integration real but unfortunately these techniques have no control over the behavior of the High power batteries during the rolling cycle. For this an improvement of these three strategies was introduced by integrating a technique of control of the state of charge "Control SOC" of this secondary source in order to follow the behavior of the High power batteries along the conduct of the driver in order to ensure loading the High power batteries at the end of each cycle. These strategies of the proposed energy management system have been successively validated by experimental tests for an urban-type rolling cycle (ARTEMIS) using the Matlab-Simulink.

**Keywords**— Electric vehicle with hybrid source, High energy lithium-ion (Li-ion) batteries, High power batteries, State of charge, Limitation method, Filtering method, Control SOC method

# I. INTRODUCTION

The question of the energy crisis, as well as climate
change, has made a lot of anchors this last decade[1].
According to an inventory made in 2019 by the International
Energy Agency, and with 23% of the contribution, transport
is the second source of greenhouse gases after producing

electricity and heat, followed by industry With 20%. In the
first 23%, three quarters are caused by road transport,
especially with the growing dependence on fossil fuel in this
industry and most others. This issue provides a strong
driving force in developing green and sustainable
transportation systems that have boosted the electrification of
vehicles worldwide. In a non-far future, this new transport
mode will replace fossil fuel vehicles, and one of the major
components of an electric vehicle is its storage system [2].
The management of energy flows in a hybrid storage source
Electric vehicle is always a delicate subject, as there are
countless ways to proceed. The main questions to be asked
are[3]:

* How to manage these flows “optimally”?

* What is the right balance between advantages/disadvantages?

In our case, the addition of an auxiliary source with a
specific high power like high power batteries, allows to
decrease strongly the current stresses on the batteries and
thus increase the overall performance of the storage source
(duration of life, performance, dynamics, autonomy,
etc...)[4]. In addition, the presence of this energy source
additional functionalities, particularly because of its
reversibility. It then becomes possible to recover the entire
braking energy[5]. This distribution must satisfy the power
demand of the electric motor and comply with energy
constraints of the storage system. This hybrid system
requires an appropriate energy management strategy to
maximize the potential of different sources and in order to
efficiently use the on-board energy[6]. It can be based on
deterministic rules or not, but also on objective functions
(lifetime of the battery, driving comfort, fuel consumption of
the vehicle, etc...). Many work was done on the subject[7].

These strategies can be classified into two main
categories:

* Strategies based on predefined rules

* Strategies based on optimization

The Fig.1 summarizes the relationship between design
basis and hybrid source management. The input variable of
this process is the power profile of the electric vehicle and
the characteristics of batteries HE and batteries HP[8].
<figure><img src="image_5.png" /><figcaption>Fig. 1. Energy management for hybrid energy</figcaption></figure>

The output variables are the power instructions distributed on both sources.

In this thinking context, this paper will discuss the specifications needed in our study as a first part; they will detail into the used vehicle dynamic model and the storage system besides their modelling. After that, we'll discuss the results we've had from simulations work, followed by results discussion and a conclusion.

## II. SPECIFICATIONS

### A. Vehicle Model

The power can be calculated based on the type of vehicle used. The amount of mechanical energy consumed by a vehicle when driving a given driving pattern depends on three effects:

* Aerodynamic friction losses;

* Losses by rolling friction;

* The energy dissipated in the brakes.

From Fig.2 the vehicle model can be described using Newton's second law [2].

Where 'Fa' is the drag force, 'Fr' is the rolling friction, 'Fg' is the force caused by gravity when driving on non-horizontal roads, 'Fd' is the disturbing force that sums up all other effects, and 'Ft' is the pulling force which depends on speed and acceleration.

<figure><img src="image_6.png" /><figcaption>Fig. 2. Acting forces on the vehicle.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Fig. 3. Artemis Cycle</figcaption></figure>

### B. Mission

To validate our system, we must test under different circumstances that could be faced in an automotive application as a test mission. We chose the Artemis Driving cycle. This cycle is based on a statistical study carried out in Europe as part of the Artémis project. It is composed of 3 different configurations, plus an additional variant: urban cycle, rural, highway 130 km/h and highway 150 km/h as we can observe in the Fig.3[9].

### C. Li-ion batteries and cells selected for study

The following Table.1 summarize the main characteristics of the two type of battery elements Li-ion selected for our study:

* One High Power Density (HP).

* The other with High Energy Density (HE).

<table><caption>TABLE I. KOKAM CELL SPECIFICATIONS[10]</caption><thead><tr><td>Parameters</td><td>Kokam HE</td><td>Kokam HP</td></tr></thead><tbody><tr><td>Capacity C_elb (Ah)</td><td>40</td><td>13</td></tr><tr><td>Voltage U_elb(V)</td><td>3.7</td><td>3.7</td></tr><tr><td>Discharge current I_elb_Dis (A)</td><td>40</td><td>104</td></tr><tr><td>Charge current I_elb_C (A)</td><td>40</td><td>39</td></tr><tr><td>Weight W_elb (kg)</td><td>0.885</td><td>0.325</td></tr><tr><td>Volume V_elb(L)</td><td>0.441</td><td>0.174</td></tr><tr><td>Cost (Euros)</td><td>20.63</td><td>30.36</td></tr></tbody></table>
<figure><img src="image_8.png" /><figcaption>Fig. 4. Principle of the frequency energy</figcaption></figure>

### III. RESULTS AND DISCUSSION

#### A. Strategies based on frequency energy

Frequency energy management consists of making sources complementary energy and power (battery HE/battery HP) taking into account the characteristics intrinsic to each source to ensure the overall mission of the vehicle. The guiding idea of this strategy consists of distributing the low-frequency components of the traction power to the power source ($P_{HE}$) and high frequencies at the power source ($P_{HP}$).

Fig. 04 illustrates the principle of frequency energy management. The power (PV) required for the propulsion of the electric vehicle is broken down into two parts by the low pass filter[11].

##### ➤ Application

The mission profiles (power and energy) of the batteries HE/HP, given by the filtering, are shown in Fig. 5. It can be seen that the power recovered by the batteries HE is zero and that the batteries HP have, most of the time, the task of recovering this energy. In addition, the high powers returned to the batteries HP are not necessarily high frequency harmonic powers.

<figure><img src="image_9.png" /><figcaption>(A)</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>(B)</figcaption></figure>

<caption>Fig. 5. Power and energy profiles from filtering strategy. (A) batteries HE, (B) Batteries HP strategy</caption>

#### B. Strategies based on power limitation

This strategy uses a classic power saturation approach. It consists of limit the charging and discharging power of the HE battery in advance over the entire route considered. The use of power limits then makes it possible to maintain the state of charge HE battery power within an acceptable range and provides an additional degree of freedom to help to optimize the operation of the hybrid storage system. Indeed, the choice of the limits of battery power is generally related to the average mission power and the internal characteristics of the two components[12]. Fig 6 presents energy management in a hybrid source where the instantaneous power is broken down into two missions. This example of power profiles shows that the energy management algorithm limits the battery power in both phases (charge / discharge). The HP battery then ensures the power difference between the mission of the vehicle (Pv) and that of the battery ($P_{HE}$)[13].

##### ➤ Application

<figure><img src="image_11.png" /><figcaption>(A)</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>(B)</figcaption></figure>

<caption>Fig. 6 Power and energy profiles from limitation strategy. (A) batteries HE, (B) Batteries HP</caption>
### C. Strategy based on power limitation with SOC control

To meet the same objectives imposed for the power limitation method, namely the use of batteries to ensure autonomy and HP batteries for power peaks. These should be recharged to energy balance at the end of the driving cycle[14]. To do this, modifications have been made to the frequency management strategy, by integrating battery power saturation and management of the state of charge of the batteries HP (see Fig. 7)[15].

The HP batteries would have the possibility of discharging on the batteries and vice versa to regain energy balance[16].

<figure><img src="image_13.png" /><figcaption>Fig. 7. Principle of the power limitation with SOC control strategy</figcaption></figure>

<figure><img src="image_14.png" /></figure>

Application

<figure><img src="image_15.png" /><figcaption>(A)</figcaption></figure>

<figure><img src="image_16.png" /><figcaption>Fig. 8. power and energy profiles from limitation strategy. (A) batteries HE, (B) Batteries HP</figcaption></figure>

<figure><img src="image_17.png" /><figcaption>(A)</figcaption></figure>

<figure><img src="image_18.png" /><figcaption>(B)</figcaption></figure>

<figure><img src="image_19.png" /><figcaption>(C)</figcaption></figure>

<caption>Fig. 9. State of charge. (A) frequency, (B) limitation, (C) SOC control</caption>
showing us the evolution of the powers obtained with the different management methods for the two HE and HP battery technologies. In the case of power limitation, the constraints on the power battery are very low, however the energy state of this battery decreases with use and it does not return to the initial state (Fig. 9). With the filtering method, we notice an increase in the constraints on the power battery with an average smoothing of the power of the HE battery. With the method developed, we have a maximum smoothing of the power of the HE battery which will reduce the constraints of this component and we also notice that the energy state of the HP battery always returns to the initial state.

## IV. CONCLUSION :

This article presents three power management strategies for a hybrid energy storage system powering an electric vehicle that mainly includes a primary high-energy type Li-ion battery and another high-power battery as a secondary source. In this context, we applied ML and MF strategies based on deterministic rules currently used to ensure constraint sharing between the two sources. On the basis of these two strategy variants, a “Control SOC” method has been developed based on an adaptation of the battery limit powers according to the state of charge of the high-power battery. The simulation results using MATLAB-Simulink, demonstrate the interest of the strategy developed, with a gain of 21.4% on the effective power and a reduction of 25% on the ampere-hours exchanged by the primary source. This stress reduction can significantly improve the life of the storage system.

## REFERENCES

[1] L. Degaa, N. Rizoug, B. Bendjedia, A. Saidane, C. Larouci, et A. Belaidi, « Using of multi-physical models to evaluate the Influence of power management strategies on the ageing of hybrid energy storage system », in *2019 6th International Conference on Control, Decision and Information Technologies (CoDIT)*, avr. 2019, p. 2052-2057. [En ligne]. Disponible sur: https://ieeexplore.ieee.org/abstract/document/8820567/

[2] L. Degaa, B. Bendjedia, N. Rizoug, et A. Saidane, « Energy Secondary Source Technology Effect on Hybrid Energy Source Sizing for Automotive Applications », in *2018 5th International Conference on Control, Decision and Information Technologies (CoDIT)*, avr. 2018, p. 1040-1044. doi: 10.1109/CoDIT.2018.8394933.

[3] S. Adhikari, Zhang Lei, Wang Peng, et Yi Tang, « A battery/supercapacitor hybrid energy storage system for DC microgrids », in *2016 IEEE 8th International Power Electronics and Motion Control Conference (IPEMC-ECCE Asia)*, Hefei, China, mai 2016, p. 1747-1753. doi: 10.1109/IPEMC.2016.7512558.

[4] I. Shchur et Y. Biletskyi, « Battery Currents Limitation in Passivity Based Controlled Battery/Supercapacitor Hybrid Energy Storage System », in *2018 IEEE 38th International Conference on Electronics and Nanotechnology (ELNANO)*, Kiev, avr. 2018, p. 504-510. doi: 10.1109/ELNANO.2018.8477477.

[5] L. Degaa, B. Bendjedia, N. Rizoug, et A. Saidane, « Energy secondary source technology effect on hybrid energy source sizing for automotive applications », in *2018 5th International Conference on Control, Decision and Information Technologies (CoDIT)*, avr. 2018, p. 1040-1044. [En ligne]. Disponible sur: https://ieeexplore.ieee.org/abstract/document/8394933/

[6] M. Robayo, M. Abusara, M. Mueller, et S. Sharkh, « A Smart Energy Management System for Battery-Supercapacitor in Electric Vehicles based on the Discrete Wavelet Transform and Deep Learning », in *2020 IEEE 29th International Symposium on Industrial Electronics (ISIE)*, Delft, Netherlands, juin 2020, p. 9-14. doi: 10.1109/ISIE45063.2020.9152559.

[7] T. Mesbahi, N. Rizoug, P. Bartholomeus, R. Sadoun, F. Khenfri, et P. L. Moigne, « Optimal energy management for a li-ion battery/supercapacitor hybrid energy storage system based on a particle swarm optimization incorporating Nelder Mead simplex approach », *IEEE Trans. Intell. Veh.*, vol. 2, n° 2, p. 99-110, juin 2017.

[8] V. I. Herrera, H. Gaztanaga, A. Milo, A. Saez-de-Ibarra, I. Etxeberria-Otadui, et T. Nieva, « Optimal energy management of a battery-supercapacitor based light rail vehicle using genetic algorithms », in *2015 IEEE Energy Conversion Congress and Exposition (ECCE)*, Montreal, QC, Canada, sept. 2015, p. 1359-1366. doi: 10.1109/ECCE.2015.7309851.

[9] M. Andre, « Real-world driving cycles for measuring cars pollutant emissions - Part A: The ARTEMIS European driving cycles », IFSTTAR - Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux, Research Report, janv. 2009. Consulté le: 9 septembre 2021. [En ligne]. Disponible sur: https://hal.archives-ouvertes.fr/hal-01431829

[10] L. Degaa, B. Bendjedia, N. Rizoug, A. Saidane, et A. Belaidi, « Comparative study between different energy storage technologies to design hybrid source supplying urban electric vehicles », in *2020 3rd International Conference on Computer Applications and Information Security (ICCAIS)*, mars 2020, p. 1-6. [En ligne]. Disponible sur: https://ieeexplore.ieee.org/abstract/document/9096828/

[11] H. Alloui, F. Khoucha, N. Rizoug, M. Benbouzid, et A. Kheloui, « Comparative study between rule-based and frequency separation energy management strategies within fuel-cell/battery electric vehicle », in *2017 IEEE International Conference on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe (EEEIC/landCPS Europe)*, juin 2017, p. 1-5. [En ligne]. Disponible sur: https://ieeexplore.ieee.org/abstract/document/7977524/

[12] P. Yu et al., « Battery-supercapacitor hybrid energy storage system for wind power suppression based on the turbulence model of wind speed », *J. Eng.*, vol. 2018, n° 17, p. 1922-1929, nov. 2018, doi: 10.1049/joe.2018.8334.

[13] S. R. A. Bolonne et D. P. Chandima, « Sizing an Energy System for Hybrid Li-Ion Battery-Supercapacitor RTG Cranes Based on State Machine Energy Controller », *IEEE Access*, vol. 7, p. 71209-71220, 2019, doi: 10.1109/ACCESS.2019.2919345.
[14] B.-H. Nguyen, J. P. Trovao, R. German, et A. Bouscayrol, « An Optimal Control-Based Strategy for Energy Management of Electric Vehicles Using Battery/Supercapacitor », in *2017 IEEE Vehicle Power and Propulsion Conference (VPPC)*, Belfort, France, déc. 2017, p. 1-6. doi: 10.1109/VPPC.2017.8330985.

[15] A. A. Abdelgadir et J. Y. Alsawalhi, « Energy management optimization for an extended range electric vehicle », in *2017 7th International Conference on Modeling, Simulation, and Applied Optimization*

(ICMSAO), Sharjah, United Arab Emirates, avr. 2017, p. 1-5. doi: 10.1109/ICMSAO.2017.7934911.

[16] L. Degaa, B. Bendjedia, N. Rizoug, et S. Abdelkader, « Sizing of hybrid source battery/supercapacitor for automotive applications », in *2017 Conference Internationale des Energies Renouvables, Proceeding of Engineering and Tecnology-PET*, 2017, vol. 28, p. 17-22.