Machine learning-based fast charging of lithium-ion battery by perceiving and regulating internal microscopic states

Abstract

Fast charging of the lithium-ion battery (LIB) is an enabling technology for the popularity of electric vehicles. However, high-rate charging regardless of the physical limits can induce irreversible degradation or even hazardous safety issues to the LIB system. Motivated by this, this paper proposes a machine learning-based fast charging strategy with multi-physical awareness within a battery-to-cloud framework. In particular, a reduced-order electrochemical-thermal model is built in the cloud to perceive the microscopic states of LIB, leveraging which the soft actor-critic (SAC) deep reinforcement learning (DRL) algorithm is exploited for the first time to train a fast charging strategy. Hardware-in-Loop tests and experiments with practical LIBs are carried out for validation. Results suggest that the battery-to-cloud architecture can mitigate the risk of a heavy computing burden in the real-time controller. The proposed strategy can effectively mitigate the unfavorable over-temperature and lithium deposition, which benefits the safety and longevity during fast charging. Given a similar charging speed, the proposed machine learning approach extends the LIB cycle life by about 75% compared to the commonly-used empirical protocol. Meanwhile, the proposed strategy is proven superior to the state-of-the-art rule-based and the model-based strategies in terms of charging rapidity, charging safety and computational complexity. Moreover, the trained low-complexity strategy is highly adaptive to the ambient temperature and initial charging state, which promises robust performance in practical applications.