A novel lumped thermal characteristic modeling strategy for the online adaptive temperature and parameter co-estimation of vehicle lithium-ion batteries. (June 2022)

• Record Type: Journal Article
• Authors: Shi, Haotian; Wang, Liping; Wang, Shunli; Fernandez, Carlos; Xiong, Xin; Dablu, Bobobee Etse; Xu, Wenhua
• Is Part Of: Journal of energy storage. Volume 50(2022)
• Publication Date: 2022-06
• Subjects: Lumped thermal characteristic model -- System online identification -- Adaptive thermal temperature estimation -- Joint Kalman filter algorithm -- Robustness verification analysis; Energy storage -- Periodicals; Energy storage -- Research -- Periodicals; 621.3126
• DOI: 10.1016/j.est.2022.104309
• Languages: English
• ISSNs: 2352-152X

Abstract

Abstract: Accurate modeling of thermal characteristics is critical to the safe use and reliable management of lithium-ion batteries. However, limitations in sensors and testing methods make online real-time acquisition of internal temperatures extremely difficult. This paper uses the similarity of dynamic system modeling to construct a lumped thermal characteristic model of the battery. By analyzing the heat conduction mechanism inside the battery, the optimized heat path model is combined with the classical Bernardi equation to realize the state description of the battery thermal characteristic system. In addition, the forgetting factor recursive least squares algorithm is used to realize the online identification of the parameters of the lumped thermal characteristics model. Meanwhile, the identification of the external thermal resistance is coupled with the estimation of the internal temperature, and a novel online adaptive co-estimation strategy based on the forgetting factor recursive least squares — joint Kalman filter is proposed, which solves the problem that the external thermal resistance cannot be accurately identified adaptively in a complex environment. The experimental results show that the maximum root-mean-square error of the model under different experiments is 0.53 °C, which verifies the high-accuracy of the lumped thermal characteristics modeling strategy. Highlights: The model framework of lumped thermal characteristics is constructed. An adaptive temperature and parameter co-estimation strategy is proposed. Simplified Bernardi equation is applied to the modeling of battery heat source. The FFRLS and JKF methods are combined to achieve temperature state estimation.