Characterization of external short circuit faults in electric vehicle Li-ion battery packs and prediction using artificial neural networks. (15th February 2020)

• Record Type: Journal Article
• Authors: Yang, Ruixin; Xiong, Rui; Ma, Suxiao; Lin, Xinfan
• Is Part Of: Applied energy. Volume 260(2020)
• Publication Date: 2020-02-15
• Subjects: Lithium-ion battery -- External short circuit -- Current prediction -- Temperature prediction -- Artificial neural networks; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2019.114253
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: ESC experiments are performed when the whole pack is being charged or discharged. Experiment results are analyzed to study the mutual influence between battery cells. An ANN-based method is proposed to estimate the current through the ESC cell. A 3D electro-thermal model is used to estimate the temperature rise of the ESC cell. Validation tests are performed to validate the accuracy of the proposed method. Abstract: To investigate the characteristics of lithium-ion battery packs under the condition that one cell is short-circuited when the whole battery pack is being discharged or charged, systematic battery external short circuit (ESC) experiments are conducted. Since not all battery cells are equipped with current sensors because of the space limitation and manufacturing cost, an artificial neural network (ANN)-based method is proposed to estimate the current of the short-circuited cell using only the voltage information, which is the feasible practice in electric vehicle application. Furthermore, the estimated current is used to predict maximum temperature increase as well as internal and surface temperature distribution of the ESC cell based on a 3D electro-thermal coupling model. Two experimental groups under constant current charging condition and constant power discharging condition are employed to validate the stability and accuracy of the proposed method. The results indicate that the root-mean-square-error between the estimated and measured current are 3.72 A and 6.61 A under the two validation experiments respectively, and the maximum estimation errors of temperature increase are 4.9 °C and 7.3 °C respectively.