State of charge estimation for lithium-ion batteries based on improved barnacle mating optimizer and support vector machine. (30th November 2022) - Liu, Boying; Wang, Haiyu; Tseng, Ming-Lang; Li, Zhongtao
Abstract: Precise state of charge (SOC) estimation is essential for battery management systems. The improved barnacle mating optimizer-support vector machine (IBMO-SVM) model is proposed and used for SOC estimation of lithium-ion batteries. (1) The cubic chaotic mapping, hyperbolic sinusoidal conditioning factor, and Gauss-Cosey variation are introduced to improve the barnacle mating optimizer (BMO) to obtain the improved barnacle mating optimizer (IBMO); (2) The convergence performance of IBMO is verified by comparing with other five intelligent optimization algorithms under several test functions; (3) IBMO-SVM is created by using IBMO to optimize the search for support vector machine (SVM) parameters; and (4) IBMO-SVM is used for SOC estimation of lithium-ion batteries and the estimation results are analyzed by multiple evaluation indexes. The proposed model's root mean squared error ( RMSE ) and mean absolute percentage error ( MAPE ) are 0.0042 and 0.61 %, and its R-square ( R 2 ) is 0.9994, outperforming the four comparison models. The SOC estimation methodology proposed in this study is highly accurate and reliable, and it provides advantages for improving the battery management system. Highlights: This study is based on intelligent algorithms and machine learning theory. Multiple algorithm improvement strategies are applied. An improved barnacle mating optimizer algorithm is proposed. A high-precision SOC estimation model is established.