State of Health Estimation of Li-ion Batteries Based on GWO-LSSVM

Abstract

The algorithms currently applied to state of health(SOH) estimation require numerous data samples for training and the estimation effect is not good. To address this issue, this study proposed a least-squares support vector machine(LSSVM) algorithm based on the grey wolf optimization(GWO) algorithm to estimate the SOH using the grey relational analysis method to choose constant current charging time as the input characteristic. Considering the 18650 lithium cobalt oxide battery charge/discharge cycle test as an example, the established algorithm model was used to estimate the SOH of batteries with different capacity specifications under different proportions of training set samples. The estimated results were compared with those obtained by the LSSVM algorithm based on the grid search method and the LSSVM algorithm based on the particle swarm optimization algorithm. The experimental results showed that the LSSVM algorithm model based on the GWO algorithm is suitable for small-sample data and is characterized by small estimation errors; therefore, it is more effective for battery SOH.

Keywords

• li-ion battery
• state of health estimation
• grey relational analysis
• grey wolf optimization algorithm
• least square support vector machine