Prediction of battery temperature in electric bus charging stage based on CNN-LSTM

Kui Wang; Chengfei Wu; Jiyao Shi

doi:10.1117/12.2660012

3 February 2023 Prediction of battery temperature in electric bus charging stage based on CNN-LSTM

Kui Wang, Chengfei Wu, Jiyao Shi

Proceedings Volume 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022); 125111V (2023) https://doi.org/10.1117/12.2660012
Event: Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 2022, Hulun Buir, China

Abstract

New energy electric vehicles play an important role in reducing carbon emissions, reducing fossil energy consumption, and promoting the development of electrified transportation. As an important energy storage and driving source for pure electric vehicles, the safety of power batteries in the charging process has always attracted much attention. During use, the thermal effect of the battery will affect the temperature and electrochemical properties of the battery, greatly affecting the safety and service life of the battery. This article uses the historical data of the real electric bus in operation and selects the real driving data collected on 2 electric buses. A battery temperature prediction method based on CNN-LSTM hybrid nerve in the charging stage of electric bus is proposed. Finally, compared with other models, the results show that the model can effectively predict the short-term battery temperature change in the future.

Citation Download Citation

Kui Wang, Chengfei Wu, and Jiyao Shi "Prediction of battery temperature in electric bus charging stage based on CNN-LSTM", Proc. SPIE 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 125111V (3 February 2023); https://doi.org/10.1117/12.2660012

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