State of Health Estimation for Lithium-Ion Batteries Using IAO–SVR

Abstract

The state of health (SOH) of lithium-ion batteries (LIBs) needs to be accurately estimated to ensure the safety and stability of electric vehicles (EVs) while in operation. In this paper, we proposed a SOH estimation method based on Improved Aquila Optimizer (IAO) and Support Vector Regression (SVR) to achieve an accurate estimation of SOH. During the charging and discharging phases of the battery, we analyzed the trends in current, voltage, and energy, then extracted four features. We used the Kendall coefficient and gray relational grade to prove that features and SOH were highly correlated. On the other hand, IAO was used to optimize the penalty factor and kernel function parameters of the SVR to further improve the generalization and mapping ability. The proposed method was verified under different operating conditions using the CACLE battery data set; the results show that high accuracy can be achieved in SOH estimation via IAO–SVR, and the estimation error of mean MAE is remaining within 2%.

State of Health Estimation of Lithium-Ion Battery for Electric Vehicle Based on VMD-DBO-SVR Model

“…By screening VC models, extracting health features, and building SVR models, the method showed high prediction accuracy on four aging datasets. Xing et al 30 proposed a SOH estimation method based on improved Eagle optimizer (IAO) and support vector regression (SVR). The penalty factor and kernel function parameters of SVR were optimized using IAO, and the high prediction accuracy of the proposed algorithm was verified using the CACLE battery dataset.…”