Optimal control strategy to charging and discharging techniques for electric vehicle battery pack optimization based on genetic algorithm and machine learning

This paper investigates optimal control strategies for charging and discharging battery packs, aiming to maximize lifespan and performance. The focus is on developing efficient techniques based on Genetic Algorithms (GAs) and machine learning (ML) to optimize battery pack operation. This study uses a proposed GA as a global search engine for optimal control parameters, while integrating Support Vector Machine (SVM) to improve the prediction accuracy of the battery state affected by these parameters. Furthermore, the Deep Reinforcement Learning (DRL) agent is trained in a physics-based simulation environment, such as PyBaMM, directly learning physics-informed, dynamic charging current profiles, unlike traditional DRL studies.The research explores various control parameters, including charging/discharging rates, current profiles, and temperature management, to minimize degradation and maximize energy efficiency. This approach effectively searches the vast solution space to identify optimal control strategies that balance immediate energy demands with long-term battery health. Simulation results demonstrate the effectiveness of the proposed GA-based optimization framework in achieving significant improvements in battery pack lifespan, energy efficiency, and overall performance compared to conventional control methods.