Estimation of remaining capacity of lithium-ion batteries based on X-ray computed tomography. (1st November 2022)

• Record Type: Journal Article
• Authors: Hou, Junwei; Wu, Weichuang; Li, Lifu; Tong, Xin; Hu, Renjun; Wu, Weibin; Cai, Weizhi; Wang, Hailin
• Is Part Of: Journal of energy storage. Volume 55:Part A(2022)
• Publication Date: 2022-11-01
• Subjects: Lithium-ion battery -- Remaining capacity -- X-ray computed tomography -- Material parameters -- Peukert equation; Energy storage -- Periodicals; Energy storage -- Research -- Periodicals; 621.3126
• DOI: 10.1016/j.est.2022.105369
• Languages: English
• ISSNs: 2352-152X

Abstract

Abstract: Accurate estimation of remaining capacity of Lithium-ion batteries (LIBs) for electric vehicles (EVs) is critical for battery management and second-use. The traditional methods ignore the degradation of the internal architecture of the battery caused by electrochemical reactions and generally fail to be applied to the dynamic operating condition. In this study, a novel estimation method based on X-ray industrial computed tomography (ICT) is developed. First, as the basis of estimation model, the mathematical relationship between the state of charge (SoC) and the content of lithium is obtained through the principles of electrochemistry. Second, according to Faraday's law and Peukert equation, the function of the remaining capacity and the material parameters (i.e. density, thickness, area of active material) and operating conditions is derived. Meanwhile, a new method for detecting the remaining capacity is proposed based on ICT. Third, experiments are conducted on three lithium iron phosphate (LFP) batteries to establish an estimation model. Comparing with the traditional method, the maximum prediction error of this model is 5.2 %, which is lower than that of the traditional method (24.1 %). Therefore, the novel method has great potential in reducing the cost of secondary use and improving the development efficiency. Highlights: A method is established to estimate remaining capacity based on computed tomography. An estimation model is established based on material parameters and working condition. Correlation analysis of remaining capacity with material parameters and working condition The mathematical relationship between the state of charge (SoC) and the content of lithium