A combination Kalman filter approach for State of Charge estimation of lithium-ion battery considering model uncertainty. (15th August 2016)

• Record Type: Journal Article
• Authors: Li, Yanwen; Wang, Chao; Gong, Jinfeng
• Is Part Of: Energy. Volume 109(2016)
• Page Start: 933
• Page End: 946
• Publication Date: 2016-08-15
• Subjects: Lithium-ion battery -- State of Charge -- Chi-square distribution -- Strong tracking factor -- Adaptive unscented Kalman filter; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2016.05.047
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: An accurate battery State of Charge estimation plays an important role in battery electric vehicles. This paper makes two contributions to the existing literature. (1) A recursive least squares method with fuzzy adaptive forgetting factor has been presented to update the model parameters close to the real value more quickly. (2) The statistical information of the innovation sequence obeying chi-square distribution has been introduced to identify model uncertainty, and a novel combination algorithm of strong tracking unscented Kalman filter and adaptive unscented Kalman filter has been developed to estimate SOC (State of Charge). Experimental results indicate that the novel algorithm has a good performance in estimating the battery SOC against initial SOC errors and voltage sensor drift. A comparison with the unscented Kalman filter-based algorithms and adaptive unscented Kalman filter-based algorithms shows that the proposed SOC estimation method has better accuracy, robustness and convergence behavior. Highlights: Recursive least squares method with fuzzy adaptive forgetting factor is presented. The innovation obeying chi-square distribution is used to identify uncertainty. A combination Karman filter approach for State of Charge estimation is presented. The performance of the proposed method is verified by comparison results.