1. Reinforcement learning-based power sharing between batteries and supercapacitors in electric vehicles
- Author
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Pascal Venet, Ali Sari, Amine Lahyani, Ahmed Chiheb Ammari, Riadh Abdelhedi, INSAT Tunis (MMA), Institut National des Sciences Appliquées et de Technologie [Tunis] (INSAT), Ampère, Département Méthodes pour l'Ingénierie des Systèmes (MIS), Ampère (AMPERE), École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-École Centrale de Lyon (ECL), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)
- Subjects
Battery (electricity) ,Power management ,Energy management ,Computer science ,020209 energy ,Battery ,02 engineering and technology ,7. Clean energy ,Automotive engineering ,Hardware_GENERAL ,Reinforcement learning ,0202 electrical engineering, electronic engineering, information engineering ,Supercapacitors ,MATLAB ,computer.programming_language ,Supercapacitor ,Electric Vehicle ,business.industry ,020208 electrical & electronic engineering ,[SPI.NRJ]Engineering Sciences [physics]/Electric power ,Frequency sharing ,Computer data storage ,Power sharing ,business ,computer - Abstract
International audience; Energy 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.
- Published
- 2018
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