Proceedings of the 4th Borobudur International Symposium on Science and Technology 2022 (BIS-STE 2022)

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A Review on Real Driving Cycle-Based State of Charge Prediction for EV Batteries

Authors
Ikhsan Romli1, Bermawi Priyatna Iskandar2, *
1Department of Industrial and Management Engineering, Faculty of Industrial Technology, Institut Teknologi Bandung, Bandung, 40132, Indonesia
2Department of Industrial Engineering, Faculty of Engineering, Pelita Bangsa University, Bekasi, 17530, Indonesia
*Corresponding author. Email: bermawi@mail.ti.itb.ac.id
Corresponding Author
Bermawi Priyatna Iskandar
Available Online 9 November 2023.
DOI
10.2991/978-94-6463-284-2_51How to use a DOI?
Keywords
Real Driving; Charge Prediction; EV Batteries
Abstract

Research on performance of Electric Vehicle is very important, especially in driving range of a Battery Electric Vehicle (BEV) that requires precise State of Charge (SoC) predictions. The battery SoC is an important parameter that reflects the performance of the battery. Meanwhile, the battery has varying time properties depending on real conditions when driving. It has a logical relationship in a strong non-linear form that makes it very complex. Therefore, SoC prediction based on the Real Driving Cycle (RDC) can accurately protect the battery, save energy, increase battery life, prevent overcharging or discharging, and also enable applications to make rational control strategies to achieve goals with a certain range. This paper provides a literature review of various papers that are relevant and related to SoC prediction method for BEVs based on RDC. This paper summarizes the approaches used in Li-ion battery SoC prediction. Three approaches are classified accordingly, i.e. simulation approach, data-driven approach and model-based approach. The results achieved imply that data-driven models, especially machine learning methods have the best accuracy. Based on the assessment of the various SOC prediction methods reviewed, the key issues and direction of developing SOC prediction in the future trend are also discussed.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 4th Borobudur International Symposium on Science and Technology 2022 (BIS-STE 2022)
Series
Advances in Engineering Research
Publication Date
9 November 2023
ISBN
10.2991/978-94-6463-284-2_51
ISSN
2352-5401
DOI
10.2991/978-94-6463-284-2_51How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Ikhsan Romli
AU  - Bermawi Priyatna Iskandar
PY  - 2023
DA  - 2023/11/09
TI  - A Review on Real Driving Cycle-Based State of Charge Prediction for EV Batteries
BT  - Proceedings of the 4th Borobudur International Symposium on Science and Technology 2022 (BIS-STE 2022)
PB  - Atlantis Press
SP  - 453
EP  - 459
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-284-2_51
DO  - 10.2991/978-94-6463-284-2_51
ID  - Romli2023
ER  -