@inproceedings{0d6c7d6904144f4291b2909c01bc9b64,
title = "Forecasting the Temperature of BEV Battery Pack Based on Field Testing Data",
abstract = "Monitoring electric vehicles{\textquoteright} 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{\textquoteright}s average temperature in real-world driving. Our best Bi-LSTM model achieved the mean absolute error of 2.92 ∘ C on test data and 1.7 ∘ C on cross-validation for prediction of 10 min on the battery pack{\textquoteright}s temperature.",
keywords = "Battery Temperature Forecasts, Driving Behaviour, Electric Vehicle, Electric Vehicle Data, Lithium-ion Battery, Machine Learning",
author = "Chou, {Ka Seng} and Wong, {Kei Long} and Davide Aguiari and Rita Tse and Tang, {Su Kit} and Giovanni Pau",
note = "Publisher Copyright: {\textcopyright} 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.; 3rd EAI International Conference on Edge Computing and IoT, EAI ICECI 2022 ; Conference date: 13-12-2022 Through 14-12-2022",
year = "2023",
doi = "10.1007/978-3-031-28990-3_1",
language = "English",
isbn = "9783031289897",
series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "3--17",
editor = "Zhu Xiao and Xingxia Dai and Jinmei Shu and Ping Zhao",
booktitle = "Edge Computing and IoT",
address = "Germany",
}
