Optimizing EV Battery Management: Advanced Hybrid Reinforcement Learning Models for Efficient Charging and Discharging
Open Access
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
This publication has 53 references indexed in Scilit:
- Electric vehicles charging management using deep reinforcement learning considering vehicle-to-grid operation and battery degradationEnergy Reports, 2023
- State of Charge Estimation of Lithium-ion Batteries Using Artificial Intelligence Based on Entropy and Enthalpy Variation2023
- Bi-level optimization of charging scheduling of a battery swap station based on deep reinforcement learningEngineering Applications of Artificial Intelligence, 2022
- Deep learning enabled state of charge, state of health and remaining useful life estimation for smart battery management system: Methods, implementations, issues and prospectsJournal of Energy Storage, 2022
- A novel hybrid data-driven method based on uncertainty quantification to predict the remaining useful life of lithium batteryJournal of Energy Storage, 2022
- Economic Operation Scheduling of Microgrid Integrated with Battery Swapping StationArabian Journal for Science and Engineering, 2022
- Soft actor-critic –based multi-objective optimized energy conversion and management strategy for integrated energy systems with renewable energyEnergy Conversion and Management, 2021
- Real-Time metadata-driven routing optimization for electric vehicle energy consumption minimization using deep reinforcement learning and Markov chain modelElectric Power Systems Research, 2021
- Parametric study on reinforcement learning optimized energy management strategy for a hybrid electric vehicleApplied Energy, 2020
- Intelligent energy management for hybrid electric tracked vehicles using online reinforcement learningApplied Energy, 2019