State of health estimation for lithium batteries using improved weighted grey relational method

The State of Health (SOH) for lithium batteries is a crucial safety index, which is usually subjected to the many influencing factors including current, voltage, temperature, and so on. The existing direct indices to predict SOH have limitations with agreeable reliability. A new approach using multi-sensor fusion technology is proposed to construct a comprehensive health indicator for predicting SOH. Firstly, we calculate the probability density of each sensor for the charge/discharge cycle using the kernel probability density method. Secondly, we use the peak value of each probability density function as a new health index. Thirdly, we fuse the strongly correlated health indices using an improved grey correlation analysis method and use the comprehensive health indicator to estimate SOH. Thereafter, we conduct an experimental comparison study using publicly available degradation data of lithium batteries, and the results demonstrate that our method can effectively address the issues of incomplete consideration for influencing factors and low reliability of the SOH characterization indicators.