Improved Particle Swarm Optimization-Extreme Learning Machine Modeling Strategies for the Accurate Lithium-ion Battery State of Health Estimation and High-adaptability Remaining Useful Life Prediction

Abstract

To ensure the secure and stable operation of lithium-ion batteries, the state of health (SOH) and the remaining useful life (RUL) are the critical state parameters of lithium-ion batteries, which need to be estimated precisely. A joint SOH and RUL estimation approach based on an improved Particle Swarm Optimization Extreme Learning Machine (PSO-ELM) is proposed in this paper. The approach adopts Pearson coefficients to screen multivariate information of the discharge process as health indicators and uses them as inputs to enable accurate estimation of SOH and RUL prediction of lithium-ion batteries on the basis of the PSO-ELM model. The validity of the model is demonstrated by the NASA lithium-ion battery data set: the maximum root mean square error (RMSE) of the SOH estimation of the tested battery is 0.0033, the maximum RMSE of its RUL prediction is 0.0082, and the maximum absolute error of RUL prediction is one cycle number. In comparison with the prediction results of the traditional extreme learning machine, the optimized model proposed in this paper estimates the SOH of lithium-ion batteries and RUL with relatively high accuracy.