Smart battery management system and remaining useful life prediction for second life batteries

Given the popularity of EVs, it can be foreseen that many retired EV battery packs will appear. Since the retired EV batteries still have 70%-80% capacity, reusing the retired EV batteries as the stationary energy storage becomes appealing. However, the management of the retired batteries is difficult. For example, the degradation mechanism of retired battery varies, and they are prone to overcharge or over-discharge.In this study, a smart battery management system for retired EV batteries will be developed. Firstly, a high accuracy advanced co-estimation algorithm will be developed to estimate important battery characteristics, such as state of charge, state of health and state of power. Secondly, a dynamic balancing current-based voltage equalization for series-connected batteries will be developed. Thirdly, the remaining useful life of the retired batteries will be predicted. Finally, a prototype will be built for retired battery management and useful life prediction in various applications.

Prashant Shrivastava
Faculty Supervisor: 
Zhanle Wang
Partner University: