Least squares support vector machine for state of charge estimation of lithium-ion battery using gray wolf optimizer. Issue 1 (July 2021)

• Record Type: Journal Article
• Authors: Wang, Qiao; Ye, Min; Wei, Meng; Wu, Chenguang; Ma, Yuchuan
• Is Part Of: Journal of physics. Volume 1983:Issue 1(2021)
• Publication Date: 2021-07
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1983/1/012077
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: An accurate SOC estimation can ensure the effective and normal operation of the battery. To this end, a method for estimating the SOC of lithium-ion battery based on least squares support vector machine (LSSVM) is proposed, and the gray wolf optimizer (GWO) algorithm is used to further optimize the model parameters to overcome the non-linearity and unevenness of the actual working condition. Influence of interference factors, and then obtain accurate SOC estimation results. The optimized LSSVM is verified by collected data at different temperatures, the results shows the proposed optimization model is greatly improved compared to before optimization, the model accuracy is improved by about 35%.