Battery failure warning based on machine learning and intelligent algorithm

Wenyuan Zheng; Miaohua Huang; Ruifeng Wang; Tianyou Jiang; Guohang Li

doi:10.1117/12.2661160

2 February 2023 Battery failure warning based on machine learning and intelligent algorithm

Wenyuan Zheng, Miaohua Huang, Ruifeng Wang, Tianyou Jiang, Guohang Li

Proceedings Volume 12462, Third International Symposium on Computer Engineering and Intelligent Communications (ISCEIC 2022); 1246233 (2023) https://doi.org/10.1117/12.2661160
Event: International Symposium on Computer Engineering and Intelligent Communications (ISCEIC 2022), 2022, Xi'an, China

Abstract

In recent years, with the increase of the number of pure electric vehicles, the phenomenon of battery spontaneous combustion during charging is also emerging in an endless stream. For early fault early warning, we use a machine learning-based model to predict the probability of battery failure. We also use intelligent algorithm to optimize the hyperparameters of the model so that it can accurately predict the probability of battery failure after different time periods. Through our model, the driver or vehicle safety system can perceive the danger in advance and solve or avoid it in time.

Citation Download Citation

Wenyuan Zheng, Miaohua Huang, Ruifeng Wang, Tianyou Jiang, and Guohang Li "Battery failure warning based on machine learning and intelligent algorithm", Proc. SPIE 12462, Third International Symposium on Computer Engineering and Intelligent Communications (ISCEIC 2022), 1246233 (2 February 2023); https://doi.org/10.1117/12.2661160

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