Optimizing EV Battery Management: Advanced Hybrid Reinforcement Learning Models for Efficient Charging and Discharging

Sercan Yalçın and Münür Sacit Herdem ()
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Sercan Yalçın: Department of Computer Engineering, Adiyaman University, Adiyaman 02040, Turkey
Münür Sacit Herdem: Department of Mechanical Engineering, Adiyaman University, Adiyaman 02040, Turkey

Energies, 2024, vol. 17, issue 12, 1-21

Abstract: This paper investigates the application of hybrid reinforcement learning (RL) models to optimize lithium-ion batteries’ charging and discharging processes in electric vehicles (EVs). By integrating two advanced RL algorithms—deep Q-learning (DQL) and active-critic learning—within the framework of battery management systems (BMSs), this study aims to harness the combined strengths of these techniques to improve battery efficiency, performance, and lifespan. The hybrid models are put through their paces via simulation and experimental validation, demonstrating their capability to devise optimal battery management strategies. These strategies effectively adapt to variations in battery state of health (SOH) and state of charge (SOC) relative error, combat battery voltage aging, and adhere to complex operational constraints, including charging/discharging schedules. The results underscore the potential of RL-based hybrid models to enhance BMSs in EVs, offering tangible contributions towards more sustainable and reliable electric transportation systems.

Keywords: artificial intelligence; battery management system; reinforcement learning; energy storage; lithium-ion batteries; charging and discharging (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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