Fine-grained and Multi-stage Fast Charging Optimization of Lithium-ion Batteries Based on TD3 Algorithm

Under the background of dual carbon, lithium batteries are widely used in the energy field. However, range anxiety limits the popularity of electric vehicles. Optimization strategies for fast charging of lithium-ion batteries have been extensively studied to solve this problem. The huge parameter space of charging protocols and the complex aging mechanism of batteries limit the application of fast charging methods. The ability of reinforcement Learning to learn from the environment and adapt to the stochastic nature of battery behavior is a significant advantage. In this paper, we propose an innovative method for fast charging lithium-ion batteries using the Two-Delay Deep Deterministic Policy Gradient (TD3) algorithm and the Single Particle Model with Electrolyte model. The trained agent dynamically adjusts the charging current according to the state every 5 seconds to optimize the trade-off between fast charging and safety limits and can charge the state of charge (SOC) of the battery from 0.2 to 0.8 in 410 seconds, while protecting against overvoltage and overheating. Meanwhile, it still works well for different initial SOC.