A team from the University of Science and Technology of China in Hefei is proposing a new approach to estimate the state of health of EV battery packs using an artificial intelligence (AI) optimization algorithm. A paper on their work is published in the Journal of Power Sources.
Accurately estimating battery pack state of health (SOH) estimation is important for a number of reasons in EV applications, including dynamic response of the pack, safety reliability. However, the different performances in battery discharge/charge characteristics and working conditions in battery pack make estimating the battery pack SOH difficult.
Thew researchers define the battery pack SOH as the change of battery pack maximum energy storage. It contains all the cells’ information including battery capacity, the relationship between state of charge (SOC) and open circuit voltage (OCV), and battery inconsistency.
To predict the battery pack SOH, the team applied a particle swarm optimization-genetic algorithm. Based on the results, they employed a particle filter in battery SOC and OCV estimation to avoid the noise influence occurring in battery terminal voltage measurement and current drift. A recursive least square method updated cells’ capacity
The verified the proposed method using the New European Driving Cycle and dynamic test profiles. The experimental results indicate that the proposed method can estimate the battery states with high accuracy for actual operation.
Xu Zhang, Yujie Wang, Chang Liu, Zonghai Chen (2017) “A novel approach of battery pack state of health estimation using artificial intelligence optimization algorithm,” Journal of Power Sources, Volume 376, Pages 191-199 doi: 10.1016/j.jpowsour.2017.11.068