State of Health estimation and Remaining Useful Life prediction for lithium-ion batteries by Improved Particle Swarm Optimization-Back Propagation Neural Network

Abstract

Accurate State of Health (SOH) estimation and Remaining Useful Life (RUL) prediction play important roles in ensuring the safe operation of the batteries and minimizing maintenance costs. It is difficult to directly measure the SOH and RUL of batteries in actual application. This paper estimates SOH and predicts RUL based on Improved Particle Swarm Optimization-Back Propagation Neural Network (IPSO-BPNN) with Health Indicators (HIs) as input. The HIs are extracted from the lithium-ion batteries charging process because the charging process is stable and easy to measure. Aiming at the nonlinear problem of batteries, Back Propagation Neural Network (BPNN) with strong generalization ability is used to estimate SOH and predict RUL. In order to solve the problem of BPNN parameter initialization, Particle Swarm Optimization (PSO) combined with variation factor is adopted in this paper to optimize the initial weights and thresholds of the neural network. In addition, the exponential decaying learning rate is adopted to improve the stability and learning efficiency of the network. Two datasets of batteries are used to verify the proposed IPSO-BPNN method. The results show that compared with the standard BPNN method, the maximum root mean square error and mean absolute error of SOH estimation results by the proposed IPSO-BPNN method are reduced to 0.78% and 1.01% respectively, which proves IPSO-BPNN method has higher accuracy and validity than standard BPNN method.