State of health estimation for lithium-ion battery based on improved support vector regression

Abstract

The lithium-ion battery has grown to be one of the most popular types of energy storage because of its many great qualities. However, the state of health (SOH) of the lithium-ion battery decreases as the number of cycles increases, resulting in reduced performance or even failure of the device, so an accurate SOH is essential for the safe operation of the device. To solve the issue of existing estimate methods' low estimation accuracy, a SOH estimation approach based on improved support vector regression is proposed. Firstly, the changes in battery characteristics during charging and discharging are analyzed and the health factors characterizing the SOH degradation are extracted. Pearson and Spearman correlation coefficients are used to quantitatively analyze the correlation between SOH and health factors. In addition, by optimizing the kernel function parameters of the SVR model by IALO, the improved ant lion optimization and support vector regression (IALO-SVR) based SOH estimation technique was developed. The IALO-SVR method was validated with the NASA battery dataset, and the experimental results showed that the method can estimate SOH more accurately compared with Back Propagation Neural Network (BP) and ALO-SVR methods, with an estimation error of no more than 1.8% at most.