Thermal runaway modeling of LiNi0.6Mn0.2Co0.2O2/graphite batteries under different states of charge. (May 2022)

• Record Type: Journal Article
• Authors: Chen, Jie; Rui, Xinyu; Hsu, Hungjen; Lu, Languang; Zhang, Caiping; Ren, Dongsheng; Wang, Li; He, Xiangming; Feng, Xuning; Ouyang, Minggao
• Is Part Of: Journal of energy storage. Volume 49(2022)
• Publication Date: 2022-05
• Subjects: Lithium-ion battery -- Battery safety -- Thermal runaway -- Modeling -- State of charge; Energy storage -- Periodicals; Energy storage -- Research -- Periodicals; 621.3126
• DOI: 10.1016/j.est.2022.104090
• Languages: English
• ISSNs: 2352-152X

Abstract

Highlights: TR behaviors of nickel-rich Li-ion batteries under different SoCs are studied. A novel method is proposed to identify the kinetic parameters of exothermic reactions. A TR model is established for Li-ion batteries by coupling four exothermic reactions. The model's simulation and prediction can fit well with the experiment results. Abstract: Thermal runaway (TR) is crucial for the safe application of lithium-ion batteries, especially for high-energy-density batteries, and thus should be investigated in detail. In this paper, the TR behaviors of the LiNi0.6 Mn0.2 Co0.2 O2 /graphite lithium-ion batteries under different states of charge (SoC) are studied, and the relationship between SoC and characteristic temperatures of TR is revealed. Based on TR test results, a novel method is proposed to identify the kinetics parameters of the exothermic reactions during the TR process. In the proposed method, the battery TR process is divided into four stages according to the changes in the temperature rate profiles. The reaction kinetic models for the exothermic reactions in each stage are constructed, and the kinetics parameters are identified through kinetics analysis on the battery temperature rate profiles. A lumped TR model is then established by superimposing the four stages of heat generation. The model fits well with the experimental results and can be further utilized to predict the battery TR behaviors under unknown SoC and different test conditions, with the errors less than 7%, demonstrating its capability to enhance the efficiency in battery safety evaluation and saving test cost.