Short-term time series state of charge prediction in electric vehicle LGM50 batteries: a rolling forecasting approach

Zeyang Zhou; Angelo Greco; Fenix Huang; Karthick Thiyagarajan; Jun Li; Mukesh Prasad

doi:10.1109/ICST62759.2024.10992073

Short-term time series state of charge prediction in electric vehicle LGM50 batteries: a rolling forecasting approach

Zeyang Zhou, Angelo Greco, Fenix Huang, Karthick Thiyagarajan, Jun Li, Mukesh Prasad

School of Engineering, Design & Built Environment

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

Abstract

The prediction of the state of charge (SOC) for battery management systems has become increasingly important with the rapid growth of the electric vehicle industry. Traditional methods for SOC prediction often depend on extensive historical data from vehicles, which can be unreliable due to various uncontrollable factors such as driving behavior, weather, and road conditions. To overcome these limitations, we propose a rolling forecasting model that leverages month-by-month data from electric vehicles to predict the SOC for the subsequent month. This approach to short-term SOC prediction aims to enhance intelligent mechatronic monitoring systems, enabling the delivery of personalized driving and charging strategies to customers.

Original language	English
Title of host publication	Proceedings of the 17th International Conference on Sensing Technology (ICST), 9-11 December 2024, Sydney, New South Wales, Australia
Place of Publication	U.S.
Publisher	IEEE
Number of pages	6
ISBN (Electronic)	9798350374827
DOIs	https://doi.org/10.1109/ICST62759.2024.10992073
Publication status	Published - 2024
Event	International Conference on Sensing Technology - Sydney, Australia Duration: 9 Dec 2024 → 11 Dec 2024 Conference number: 17th

Conference

Conference	International Conference on Sensing Technology
Abbreviated title	ICST
Country/Territory	Australia
City	Sydney
Period	9/12/24 → 11/12/24

Keywords

Advanced Manufacturing
Battery Management Systems
Energy Efficiency Management
Forecasting
Intelligent Mechatronics
Smart Sensors

UN SDGs

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

Access to Document

10.1109/ICST62759.2024.10992073

Cite this

Zhou, Z., Greco, A., Huang, F., Thiyagarajan, K., Li, J., & Prasad, M. (2024). Short-term time series state of charge prediction in electric vehicle LGM50 batteries: a rolling forecasting approach. In Proceedings of the 17th International Conference on Sensing Technology (ICST), 9-11 December 2024, Sydney, New South Wales, Australia IEEE. https://doi.org/10.1109/ICST62759.2024.10992073

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title = "Short-term time series state of charge prediction in electric vehicle LGM50 batteries: a rolling forecasting approach",

abstract = "The prediction of the state of charge (SOC) for battery management systems has become increasingly important with the rapid growth of the electric vehicle industry. Traditional methods for SOC prediction often depend on extensive historical data from vehicles, which can be unreliable due to various uncontrollable factors such as driving behavior, weather, and road conditions. To overcome these limitations, we propose a rolling forecasting model that leverages month-by-month data from electric vehicles to predict the SOC for the subsequent month. This approach to short-term SOC prediction aims to enhance intelligent mechatronic monitoring systems, enabling the delivery of personalized driving and charging strategies to customers.",

keywords = "Advanced Manufacturing, Battery Management Systems, Energy Efficiency Management, Forecasting, Intelligent Mechatronics, Smart Sensors",

author = "Zeyang Zhou and Angelo Greco and Fenix Huang and Karthick Thiyagarajan and Jun Li and Mukesh Prasad",

year = "2024",

doi = "10.1109/ICST62759.2024.10992073",

language = "English",

booktitle = "Proceedings of the 17th International Conference on Sensing Technology (ICST), 9-11 December 2024, Sydney, New South Wales, Australia",

publisher = "IEEE",

note = "International Conference on Sensing Technology, ICST ; Conference date: 09-12-2024 Through 11-12-2024",

}

Zhou, Z, Greco, A, Huang, F, Thiyagarajan, K, Li, J & Prasad, M 2024, Short-term time series state of charge prediction in electric vehicle LGM50 batteries: a rolling forecasting approach. in Proceedings of the 17th International Conference on Sensing Technology (ICST), 9-11 December 2024, Sydney, New South Wales, Australia. IEEE, U.S., International Conference on Sensing Technology, Sydney, New South Wales, Australia, 9/12/24. https://doi.org/10.1109/ICST62759.2024.10992073

Short-term time series state of charge prediction in electric vehicle LGM50 batteries: a rolling forecasting approach. / Zhou, Zeyang; Greco, Angelo; Huang, Fenix et al.
Proceedings of the 17th International Conference on Sensing Technology (ICST), 9-11 December 2024, Sydney, New South Wales, Australia. U.S.: IEEE, 2024.

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

TY - GEN

T1 - Short-term time series state of charge prediction in electric vehicle LGM50 batteries

T2 - International Conference on Sensing Technology

AU - Zhou, Zeyang

AU - Greco, Angelo

AU - Huang, Fenix

AU - Thiyagarajan, Karthick

AU - Li, Jun

AU - Prasad, Mukesh

N1 - Conference code: 17th

PY - 2024

Y1 - 2024

N2 - The prediction of the state of charge (SOC) for battery management systems has become increasingly important with the rapid growth of the electric vehicle industry. Traditional methods for SOC prediction often depend on extensive historical data from vehicles, which can be unreliable due to various uncontrollable factors such as driving behavior, weather, and road conditions. To overcome these limitations, we propose a rolling forecasting model that leverages month-by-month data from electric vehicles to predict the SOC for the subsequent month. This approach to short-term SOC prediction aims to enhance intelligent mechatronic monitoring systems, enabling the delivery of personalized driving and charging strategies to customers.

AB - The prediction of the state of charge (SOC) for battery management systems has become increasingly important with the rapid growth of the electric vehicle industry. Traditional methods for SOC prediction often depend on extensive historical data from vehicles, which can be unreliable due to various uncontrollable factors such as driving behavior, weather, and road conditions. To overcome these limitations, we propose a rolling forecasting model that leverages month-by-month data from electric vehicles to predict the SOC for the subsequent month. This approach to short-term SOC prediction aims to enhance intelligent mechatronic monitoring systems, enabling the delivery of personalized driving and charging strategies to customers.

KW - Advanced Manufacturing

KW - Battery Management Systems

KW - Energy Efficiency Management

KW - Forecasting

KW - Intelligent Mechatronics

KW - Smart Sensors

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DO - 10.1109/ICST62759.2024.10992073

M3 - Conference Paper

AN - SCOPUS:105006430777

BT - Proceedings of the 17th International Conference on Sensing Technology (ICST), 9-11 December 2024, Sydney, New South Wales, Australia

PB - IEEE

CY - U.S.

Y2 - 9 December 2024 through 11 December 2024

ER -

Short-term time series state of charge prediction in electric vehicle LGM50 batteries: a rolling forecasting approach

Abstract

Conference

Keywords

UN SDGs

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