Lithium-ion battery state of health estimation using the incremental capacity and wavelet neural networks with genetic algorithm

•Health performance indicators are extracted from incremental capacity curves using Pearson correlation coefficient method.•A fusion method of incremental capacity and wavelet neural networks with genetic algorithm (GA-WNN) is proposed to estimate state of health.•Validations with battery data show this method has good performances. Accurate state of health (SOH) is a crucial factor for the regular operation of the electric vehicle. Compared with the equivalent circuit methods, the data-driven methods do not rely on the battery model and do not need to measure the open-circuit voltage. This paper proposes an on-line method based on the fusion of incremental capacity (IC) and wavelet neural networks with genetic algorithm (GA-WNN) to estimate SOH under current discharge. Firstly, IC curves are acquired, and the important health feature variables are extracted from IC curves using Pearson correlation coefficient method. Second, The GA is used to optimize the initial connection weights, translation factor and scaling factor of WNN; then, the GA-WNN model is applied to estimate battery's SOH. Third, the established model is verified by battery data. Finally, the experiment results show that the SOH estimation error of this method is less than 3%.