State-of-health estimation and remaining useful life prediction of lithium-ion batteries based on extreme learning machine

Abstract

Lithium-ion batteries have been widely applied in electric vehicles, accurate health state prediction of batteries is one of the key technologies to obtain optimal operation and health management. To achieve the highly accurate state of health (SOH) estimation and remaining useful life (RUL) prediction, a framework based on extreme learning machine (ELM) is proposed. Firstly, the indirect health indicators are extracted from discharge data. Then, the ELM model is proposed to estimate SOH and predict RUL. Finally, the propagation neural network based on particle swarm optimization (BPNN-PSO) is compared with the ELM method. The results show that proposed method hits lower average root mean square error for SOH and RUL.