Optimizing fast charging protocols for lithium-ion batteries using reinforcement learning: Balancing speed, efficiency, and longevity

Abstract

Although lithium-ion batteries are essential for contemporary energy storage applications, maintaining battery longevity, safety, and health frequently clashes with the requirement for quick charging. The problem of developing rapid charging protocols to strike a balance between battery protection and charging speed is addressed in this work. We create an adaptive charging strategy that dynamically modifies charging rates in response to battery conditions while respecting safety limitations including voltage and temperature limits using Reinforcement Learning (RL). In order to maximize performance metrics and avoid degradation, the RL agent is trained in a simulated environment.To examine their effects on charging time, capacity, temperature, deterioration, energy efficiency, and State of Health (SoH), five charging profiles—constant, decreasing, and alternating current techniques—are assessed. The findings show that quicker charging profiles speed up deterioration, raise temperature, and hasten the drop of SoH even though they shorten charging times. Slower profiles, on the other hand, improve long-term battery health and efficiency by controlling temperature and minimizing deterioration, even though they require longer charging times.The RL-based approach balances quick charging with battery preservation by implementing a reward system that penalizes dangerous conditions like high voltage or temperature in order to lessen these trade-offs. These results highlight the necessity of sophisticated charging processes to maximize efficiency in battery-dependent systems, such as electric cars and portable devices.

Keywords

• Reinforcement learning
• Lithium-ion batteries
• State of health
• Reward function
• Charging speed and battery protection