Abstract:

The joint evaluation of state of charge (SOC) and state of health (SOH) of lithium batteries is a prerequisite for ensuring stable and efficient operation of batteries. This study provides a review of current related joint evaluation methods. Firstly, the difficulties faced in evaluating the SOC and SOH of lithium batteries were analyzed in sequence, including initial value dependence, linear characteristics, and physical field coupling issues; Then, a detailed analysis was conducted on the emerging joint evaluation methods for SOC and SOH of lithium batteries, which are supported by big data and deep learning technologies. The focus was on exploring the evaluation emphasis and operational logic under different data-driven models. Finally, the development process of related technologies in recent years was summarized, hoping to provide some reference for the development of energy storage industry and research on lithium battery evaluation technology.

Key words: big data, deep learning, lithium battery, assessment