An adaptive fusion estimation algorithm for state of charge of lithium-ion batteries considering wide operating temperature and degradation

• Published in: Journal of power sources Vol. 462; p. 228132
• Main Authors: Shu, Xing, Li, Guang, Shen, Jiangwei, Yan, Wensheng, Chen, Zheng, Liu, Yonggang
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 30.06.2020
• Subjects: Adaptive H-infinity filter; Least square support vector machine; Model-based method; State of charge; Least square support vector machine; Adaptive H-infinity filter; Model-based method; State of charge
• ISSN: 0378-7753; 1873-2755
• DOI: 10.1016/j.jpowsour.2020.228132

In this paper, an adaptive fusion algorithm is proposed to robustly estimate the state of charge of lithium-ion batteries. An improved recursive least square algorithm with a forgetting factor is employed to identify parameters of the built equivalent circuit model, and the least square support vector machine algorithm is synchronously leveraged to estimate the battery state of health. On this basis, an adaptive H-infinity filter algorithm is applied to predict the battery state of charge and to cope with uncertainty of model errors and prior noise evaluation. The proposed algorithm is comprehensively validated within a full operational temperature range of battery and with different aging status. Experimental results reveal that the maximum absolute error of the fusion estimation algorithm is less than 1.2%, manifesting its effectiveness and stability when subject to internal capacity degradation of battery and operating temperature variation. •The adaptive H-infinity filter is employed for estimating state of charge.•Battery capacity is updated online to assist improvement of estimation.•The algorithm is qualified for estimation in wide temperature range and degradation.•The superiority of algorithm is justified by comprehensive experimental validation.