Research Article
Forecasting the Temperature of BEV Battery Pack Based on Field Testing Data
@INPROCEEDINGS{10.1007/978-3-031-28990-3_1, author={Ka Seng Chou and Kei Long Wong and Davide Aguiari and Rita Tse and Su-Kit Tang and Giovanni Pau}, title={Forecasting the Temperature of BEV Battery Pack Based on Field Testing Data}, proceedings={Edge Computing and IoT: Systems, Management and Security. Third EAI International Conference, ICECI 2022, Virtual Event, December 13-14, 2022, Proceedings}, proceedings_a={ICECI}, year={2023}, month={3}, keywords={Electric Vehicle Battery Temperature Forecasts Electric Vehicle Data Lithium-ion Battery Driving Behaviour Machine Learning}, doi={10.1007/978-3-031-28990-3_1} }- Ka Seng Chou
Kei Long Wong
Davide Aguiari
Rita Tse
Su-Kit Tang
Giovanni Pau
Year: 2023
Forecasting the Temperature of BEV Battery Pack Based on Field Testing Data
ICECI
Springer
DOI: 10.1007/978-3-031-28990-3_1
Abstract
Monitoring electric vehicles’ battery situation and indicating the state of health is still challenging. Temperature is one of the critical factors determining battery degradation over time. We have collected more than 2.3 million discharging samples via a custom Internet of Thing device for more than one year to build a machine-learning model that can forecast the battery pack’s average temperature in real-world driving. Our best Bi-LSTM model achieved the mean absolute error of 2.92(^\circ )C on test data and 1.7(^\circ )C on cross-validation for prediction of 10 min on the battery pack’s temperature.
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