Optimisation d’un système de stockage hybride de l’énergie électrique avec batterie et supercondensateurs pour véhicule électrique

This work contributes to the optimization of a hybrid storage system that combines lithium-ion batteries with supercapacitors used for electric vehicles. This hybridization structure was chosen due to the complementarity between both used storage devices. Our study focuses on the implementation of advanced energy control and management techniques. Using better the storage system represents the goal of this thesis. Our approach is to develop a real time algorithm of energy management taking into account battery electrical and thermal behaviors. A comparative study evaluates the benefits and the drawbacks of each proposed strategy in order to offer various choices between low cost power sharing solutions and control strategy with high performances. An experimental bench was implemented to apply the theoretical conceptCe travail contribue à l’optimisation d’un système de stockage hybride couplant une batterie lithium-ion et des supercondensateurs pour les véhicules électriques. La complémentarité entre ces deux sources d’énergie permet l’amélioration des performances globales du système. Notre étude porte sur la mise en oeuvre de techniques avancées de contrôle et de gestion de l’énergie. Notre objectif est d’avoir une meilleure utilisation du système de stockage. Dans ce cadre, notre démarche est de développer une gestion d’énergie en temps réel qui tient compte des contraintes électriques et thermiques des systèmes de stockage. Une étude comparative sur les avantages et les inconvénients de différentes techniques de gestion d’énergie nous a permis d’effectuer le choix entre un partage de puissance à moindre coût et un partage performant de l’énergie entre les systèmes de stockage. Un banc expérimental a été mis en oeuvre afin de concrétiser la démarche théoriqu

Optimization of a hybrid energy storage system with battery and supercapacitors for electric vehicles

Ce travail contribue à l’optimisation d’un système de stockage hybride couplant une batterie lithium-ion et des supercondensateurs pour les véhicules électriques. La complémentarité entre ces deux sources d’énergie permet l’amélioration des performances globales du système. Notre étude porte sur la mise en oeuvre de techniques avancées de contrôle et de gestion de l’énergie. Notre objectif est d’avoir une meilleure utilisation du système de stockage. Dans ce cadre, notre démarche est de développer une gestion d’énergie en temps réel qui tient compte des contraintes électriques et thermiques des systèmes de stockage. Une étude comparative sur les avantages et les inconvénients de différentes techniques de gestion d’énergie nous a permis d’effectuer le choix entre un partage de puissance à moindre coût et un partage performant de l’énergie entre les systèmes de stockage. Un banc expérimental a été mis en oeuvre afin de concrétiser la démarche théoriqueThis work contributes to the optimization of a hybrid storage system that combines lithium-ion batteries with supercapacitors used for electric vehicles. This hybridization structure was chosen due to the complementarity between both used storage devices. Our study focuses on the implementation of advanced energy control and management techniques. Using better the storage system represents the goal of this thesis. Our approach is to develop a real time algorithm of energy management taking into account battery electrical and thermal behaviors. A comparative study evaluates the benefits and the drawbacks of each proposed strategy in order to offer various choices between low cost power sharing solutions and control strategy with high performances. An experimental bench was implemented to apply the theoretical concep

Optimal power sharing between batteries and supercapacitors in Electric vehicles

International audienceThe success of hybrid electrical storage systems which combines batteries and supercapacitors is related to the manner how these devices are managed. In this paper, optimal use of the complementarity between batteries and supercapacitors in electric vehicles is studied. First, storage devices are sized based on reduced size specifications of the electric vehicle. Then, power waveforms for each storage device obtained by simulation using the frequency sharing strategy are presented. Different values of filter constant are therefore analyzed to show the variation effect of power sharing strategy on battery current waveforms. The optimal filter constant is finally determined. It offers reduced values of RMS battery current and enhancement of battery service life

An improved Frequency Sharing Strategy Between Battery and Supercapacitors in Electric Vehicles

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Reinforcement learning for the control of battery electrothermal behaviours in electric vehicles

International audienceBattery lifetime is related, among other things, to the battery temperature and RMS battery current. This paper presents an improved energy management of battery / supercapacitors (SCs) hybrid energy storage system (HESS) in an electric vehicle (EV) aiming at reducing the RMS battery current and battery temperature. A reinforcement learning (RL) based real-time energy management framework is designed to ensure an optimal power flow distribution between battery and supercapacitors starting from historical observation of the RMS battery current. First, the battery and SCs storage devices are modeled. An electrical model is used for the SC and an electrothermal representation is adopted to follow the evolution of the battery temperature and its electrical parameters (current, voltage). Then the RL energy management problem is formulated satisfying the electrical HESS constraints. The proposed methodology generates in real time an optimal power sharing between battery and SCs without any prior knowledge of the load variations of the EV. In our work, we propose a novel approach combining the rule based controller Frequency sharing with RL learning to achieve the best solution optimality. This approach is effective to adapt the rule-based strategy to work in their efficiency region and introduce additional intelligence. Simulation results have confirmed the convergence of the RMS battery current to the minimum values and appreciable reductions of the battery temperature are obtained

Frequency Power Sharing for Battery/Supercapacitors Hybrid Energy Storage System in Electric Vehicles

International audienceBattery/Supercapacitors combination offers superior performances for a hybrid electric energy storage system in Electric Vehicles. This paper, presents a frequency power sharing strategy to get the best use of complementarity between battery and supercapacitors. Storage devices are sized based on reduced scale specifications for the studied EV. Power and current waveform simulation results are shown for each storage device due to the use of a frequency power sharing policy. Various power split configurations are analyzed to study their effect on battery current waveforms. Optimal frequency sharing policy for the battery/SCs hybrid reducing RMS battery current is obtained. The best use of battery/SCs association provides significant improvements of Electric Vehicle performances in terms of battery lifetime and amortized cost

Reinforcement learning-based power sharing between batteries and supercapacitors in electric vehicles

International audienceEnergy management of Battery/Supercapacitors (SCs) hybrid energy storage system (HESS) aims to reduce RMS battery current values and enhance the battery lifetime. This paper presents a reinforcement learning (RL) based energy management strategy for Electric Vehicles (EV). This approach allows for learning in real time the optimal power flow distribution between battery and supercapacitors starting from historic of the observation of RMS current of battery. The power management problem is presented with RL formulation verifying the electrical HESS constraints. The presented framework uses the RL technique to control the power flow distribution leading to the minimization of the RMS battery current. Particularly, we propose a methodology that generates optimal frequency sharing policy between battery and SCs taking into account the load variations of the EV dynamically in real time. Numerical simulations carried out on Matlab/Simulink confirmed the convergence of the RMS battery current to the optimal value without any prior knowledge of the driving conditions. The proposed framework aims to adapt automatically the power management policy to the optimal solution

Screening for clusters of charge in human virus proteomes

Abstract Background The identification of charge clusters (runs of charged residues) in proteins and their mapping within the protein structure sequence is an important step toward a comprehensive analysis of how these particular motifs mediate, via electrostatic interactions, various molecular processes such as protein sorting, translocation, docking, orientation and binding to DNA and to other proteins. Few algorithms that specifically identify these charge clusters have been designed and described in the literature. In this study, 197 distinctive human viral proteomes were screened for the occurrence of charge clusters (CC) using a new computational approach. Results Three hundred and seventy three CC have been identified within the 2549 viral protein sequences screened. The number of protein sequences that are CC-free is 2176 (85.3 %) while 150 and 180 proteins contained positive charge (PCC) and negative charge clusters (NCC), respectively. The NCCs (211 detected) were more prevalent than PCC (162). PCC-containing proteins are significantly longer than those having NCCs (p = 2.10-16). The most prevalent virus families having PCC and NCC were Herpesviridae followed by Papillomaviridae. However, the single-strand RNA group has in average three times more NCC than PCC. According to the functional domain classification, a significant difference in distribution was observed between PCC and NCC (p = 2. 10−8) with the occurrence of NCCs being more frequent in C-terminal region while PCC more often fall within functional domains. Only 29 proteins sequences contained both NCC and PCC. Moreover, 101 NCC were conserved in 84 proteins while only 62 PCC were conserved in 60 protein sequences. To understand the mechanism by which the membrane translocation functionalities are embedded in viral proteins, we screened our PCC for sequences corresponding to cell-penetrating peptides (CPPs) using two online databases: CellPPd and CPPpred. We found that all our PCCs, having length varying from 7 to 30 amino-acids were predicted as CPPs. Experimental validation is required to improve our understanding of the role of these PCCs in viral infection process. Conclusions Screening distinctive cluster charges in viral proteomes suggested a functional role of these protein regions and might provide potential clues to improve the current understanding of viral diseases in order to tailor better preventive and therapeutic approaches