State of Health Estimation of Lithium-ion Battery Based on Ant Lion Optimization and Support Vector Regression

State of Health (SOH) estimation, as one of the key functions of lithium-ion battery management system (BMS), is of great significance to ensure the safe and reliable operation of batteries and reduce the maintenance cost of battery system. To improve the estimation accuracy of lithium battery SOH, a SOH estimation method based on ant lion optimization algorithm and support vector regression (ALO-SVR) was proposed. This method selects the feature parameters highly associated with current, voltage, and temperature as input, using Pearson correlation coefficient analyze the correlation between features and SOH. The key parameters of SVR model are optimized by the Ant Lion Optimization Algorithm, and the final estimation model is established. Compared with the existing GA-SVR and GS-SVR on the public data set of NASA, the results show that ALO-SVR method has higher estimation accuracy and stability, which verifies the feasibility of the estimation method.