Attention-Enhanced Deep Reinforcement Learning for Fast Charging Optimization of Lithium-Ion Batteries With Incomplete Observations

Optimizing the fast-charging of lithium-ion batteries to minimize charging time while limiting battery degradation is a crucial challenge for the battery community. In this article, we propose an attention-enhanced deep reinforcement learning (AtDRL) approach for fast charging optimization under conditions of partial battery states observable. By incorporating a self-attention mechanism within the actor-critic network architecture, the AtDRL model can dynamically shift its focus across different parts of the input sequence during prediction. This is particularly advantageous for processing input sequences of varying lengths and utilizing incomplete observations to improve sample efficiency, thereby improving the performance of fast-charging optimization. In addition, a crafted reward function is further put forward to meticulously control battery current, temperature, and voltage to mitigate battery degradation. The effectiveness of the proposed AtDRL approach is demonstrated on a porous electrode theory-based battery simulator. The results show that, compared with the conventional DRL-based charging method and Bayesian optimization-based charging method, the proposed AtDRL approach possesses enhanced charging performance and higher sample efficiency under the scenarios of incomplete state observations.