An improved resistance-based thermal model for prismatic lithium-ion battery charging. (5th November 2020)

• Record Type: Journal Article
• Authors: Xie, Yi; Zheng, Jintao; Hu, Xiaosong; Lin, Xianke; Liu, Kailong; Sun, Jinlei; Zhang, Yangjun; Dan, Dan; Xi, Dong; Feng, Fei
• Is Part Of: Applied thermal engineering. Volume 180(2020)
• Publication Date: 2020-11-05
• Subjects: Dynamic thermal model for charging -- Prismatic lithium-ion battery -- Charging resistance model -- Dynamic heat generation -- Regular and fast charging strategies; Heat engineering -- Periodicals; Heating -- Equipment and supplies -- Periodicals; Periodicals; 621.40205
• DOI: 10.1016/j.applthermaleng.2020.115794
• Languages: English
• ISSNs: 1359-4311

Abstract

Highlights: A dynamic resistance-based thermal model for charging is established. A dynamic resistance model including effects of multi-variables is proposed. The heat generation in the electrode zones is modelled. Prediction accuracy of the charging thermal model is validated. Prediction precision of discharging/charging resistance is compared. Abstract: This study proposed a dynamic resistance-based thermal model to predict the temperature evolution of a prismatic lithium-ion battery in fast and regular charging strategies. The effects of battery temperature, state of charge (SOC), and charging current on resistance were investigated to form a dynamic heat generation model. This model included both the heat generation of battery body and the heat generation of electrodes. Then the charging resistance-based thermal model was used to predict the temperature distribution and temperature evolution of a 50 Ah prismatic battery under different charging strategies and ambient temperatures. The charging strategies included a multi-stage constant current-constant voltage (MSCC-CV) for fast charging and a constant current-constant voltage (CC-CV) for regular charging. Experiments verified the prediction accuracy of the proposed thermal model. The results showed that the dynamic thermal model accurately predicted the temperature distribution and its evolution under different charging strategies and ambient temperatures. With regard to temperature distribution prediction, the average root mean square errors (RMSEs) of temperature among the five test points were 0.43 °C for the MSCC-CV charging strategy and 0.3 °C for the CC-CV charging strategy. For the temperature prediction under various ambient temperatures, the average RMSEs at different ambient temperatures were 0.34 °C for the MSCC-CV strategy and 0.25 °C for the CC-CV strategy. Finally, the effect of the charging and discharging resistance on the temperature prediction under the MSCC-CV charging condition was studied. Although the discharging resistance-based thermal model could describe the temperature change with charging time, it showed higher predicted temperatures than the tested values and a larger estimation error than the charging resistance-based thermal model.