Novel State of Health Estimation for Lithium-Ion Battery Based on Differential Evolution Algorithm - Extreme Learning Machine

To ensure a long-term safety and reliability of electric vehicle and energy storage system, an accurate estimation of the state of health (SOH) for lithium-ion battery is important. In this study, a method for estimating the lithium-ion battery SOH was proposed based on an improved extreme learning machine (ELM). Input weights and hidden layer biases were generated randomly in traditional ELM. To improve the estimation accuracy of ELM, the differential evolution algorithm was used to optimize these parameters in feasible solution spaces. First, incremental capacity curves were obtained by incremental capacity analysis and smoothed by Gaussian filter to extract health interests. Then, the ELM based on differential evolution algorithm (DE-ELM model) was used for a lithium-ion battery SOH estimation. At last, four battery historical aging data sets and one random walk data set were employed to validate the prediction performance of DE-ELM model. Results show that the DE-ELM has a better performance than other studied algorithms in terms of generalization ability.