Forecasting the Temperature of BEV Battery Pack Based on Field Testing Data

Ka Seng Chou; Kei Long Wong; Davide Aguiari; Rita Tse; Su Kit Tang; Giovanni Pau

doi:10.1007/978-3-031-28990-3_1

Forecasting the Temperature of BEV Battery Pack Based on Field Testing Data

Ka Seng Chou, Kei Long Wong, Davide Aguiari, Rita Tse, Su Kit Tang, Giovanni Pau

Faculty of Applied Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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 ^∘ C on test data and 1.7 ^∘ C on cross-validation for prediction of 10 min on the battery pack’s temperature.

Original language	English
Title of host publication	Edge Computing and IoT
Subtitle of host publication	Systems, Management and Security - 3rd EAI International Conference, ICECI 2022, Proceedings
Editors	Zhu Xiao, Xingxia Dai, Jinmei Shu, Ping Zhao
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	3-17
Number of pages	15
ISBN (Print)	9783031289897
DOIs	https://doi.org/10.1007/978-3-031-28990-3_1
Publication status	Published - 2023
Event	3rd EAI International Conference on Edge Computing and IoT, EAI ICECI 2022 - Virtual, Online Duration: 13 Dec 2022 → 14 Dec 2022

Publication series

Name	Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume	478 LNICST
ISSN (Print)	1867-8211
ISSN (Electronic)	1867-822X

Conference

Conference	3rd EAI International Conference on Edge Computing and IoT, EAI ICECI 2022
City	Virtual, Online
Period	13/12/22 → 14/12/22

Keywords

Battery Temperature Forecasts
Driving Behaviour
Electric Vehicle
Electric Vehicle Data
Lithium-ion Battery
Machine Learning

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-031-28990-3_1

Cite this

Chou, K. S., Wong, K. L., Aguiari, D., Tse, R., Tang, S. K., & Pau, G. (2023). Forecasting the Temperature of BEV Battery Pack Based on Field Testing Data. In Z. Xiao, X. Dai, J. Shu, & P. Zhao (Eds.), Edge Computing and IoT: Systems, Management and Security - 3rd EAI International Conference, ICECI 2022, Proceedings (pp. 3-17). (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 478 LNICST). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-28990-3_1

Chou, Ka Seng ; Wong, Kei Long ; Aguiari, Davide et al. / Forecasting the Temperature of BEV Battery Pack Based on Field Testing Data. Edge Computing and IoT: Systems, Management and Security - 3rd EAI International Conference, ICECI 2022, Proceedings. editor / Zhu Xiao ; Xingxia Dai ; Jinmei Shu ; Ping Zhao. Springer Science and Business Media Deutschland GmbH, 2023. pp. 3-17 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST).

@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",

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series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",

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booktitle = "Edge Computing and IoT",

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}

Chou, KS, Wong, KL, Aguiari, D, Tse, R , Tang, SK & Pau, G 2023, Forecasting the Temperature of BEV Battery Pack Based on Field Testing Data. in Z Xiao, X Dai, J Shu & P Zhao (eds), Edge Computing and IoT: Systems, Management and Security - 3rd EAI International Conference, ICECI 2022, Proceedings. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, vol. 478 LNICST, Springer Science and Business Media Deutschland GmbH, pp. 3-17, 3rd EAI International Conference on Edge Computing and IoT, EAI ICECI 2022, Virtual, Online, 13/12/22. https://doi.org/10.1007/978-3-031-28990-3_1

Forecasting the Temperature of BEV Battery Pack Based on Field Testing Data. / Chou, Ka Seng; Wong, Kei Long; Aguiari, Davide et al.
Edge Computing and IoT: Systems, Management and Security - 3rd EAI International Conference, ICECI 2022, Proceedings. ed. / Zhu Xiao; Xingxia Dai; Jinmei Shu; Ping Zhao. Springer Science and Business Media Deutschland GmbH, 2023. p. 3-17 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 478 LNICST).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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 ∘ C on test data and 1.7 ∘ C on cross-validation for prediction of 10 min on the battery pack’s temperature.

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Chou KS, Wong KL, Aguiari D, Tse R , Tang SK, Pau G. Forecasting the Temperature of BEV Battery Pack Based on Field Testing Data. In Xiao Z, Dai X, Shu J, Zhao P, editors, Edge Computing and IoT: Systems, Management and Security - 3rd EAI International Conference, ICECI 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 3-17. (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST). doi: 10.1007/978-3-031-28990-3_1