Proceedings of the International Conference on Recent Trends in Intelligent Computing, Manufacturing, and Electronics (rTIME 2025)

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A Systematic Review & Experimental Benchmark of SOC Estimation Methods for EV Applications

Authors
Mukesh Kumar1, *, Prem Nath Suman2, Aanyaa Sinha3, Madhu Kumari4, Ashwini Kumar5
1Dept. of ECE, BIT, Mesra, Ranchi, Jharkhand, India
2Dept. of EEE, SoE&IT, AJU, Jamshedpur, Jharkhand, India
3Dept. of EEE, BIT, Mesra, Ranchi, Jharkhand, India
4Dept. of ECE, NIAMT, Hatia, Ranchi, Jharkhand, India
5Dept. of ME, SoE&IT, AJU, Jamshedpur, Jharkhand, India
*Corresponding author. Email: mukeshnitp33@gmail.com
Corresponding Author
Mukesh Kumar
Available Online 31 March 2026.
DOI
10.2991/978-94-6239-628-9_25How to use a DOI?
Keywords
Electric Vehicles (EV); State of Charge (SOC); Battery Management Systems (BMS)
Abstract

The need to have good batteries keeps increasing as the use of electric vehicles and smart grids continues to increase. A Battery Management System (BMS) lies in the heart of any battery-based energy storage. Designers of electric vehicles still face a difficult task in building a successful BMS. It is one of the most crucial things it carries out to monitor the State of Charge (SOC) of the battery. SOC is important as it directly influences safety, performance and battery life. When the SOC is estimated properly, the system will be able to avoid overcharging and deep dis-charging, safeguard the health of the battery, and assist in smarter control measures that conserve energy. The subject of this paper is SOC estimation under various discharge conditions. It will look at two large categories of estimation techniques. All these groups possess their strengths: some of them are best applied when the discharge current remains constant, whereas other groups manage the discharge current that is not constant and has different and unpredictable values.

Copyright
© 2026 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 International Conference on Recent Trends in Intelligent Computing, Manufacturing, and Electronics (rTIME 2025)
Series
Advances in Engineering Research
Publication Date
31 March 2026
ISBN
978-94-6239-628-9
ISSN
2352-5401
DOI
10.2991/978-94-6239-628-9_25How to use a DOI?
Copyright
© 2026 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  - Mukesh Kumar
AU  - Prem Nath Suman
AU  - Aanyaa Sinha
AU  - Madhu Kumari
AU  - Ashwini Kumar
PY  - 2026
DA  - 2026/03/31
TI  - A Systematic Review & Experimental Benchmark of SOC Estimation Methods for EV Applications
BT  - Proceedings of the International Conference on Recent Trends in Intelligent Computing, Manufacturing, and Electronics (rTIME 2025)
PB  - Atlantis Press
SP  - 274
EP  - 286
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-628-9_25
DO  - 10.2991/978-94-6239-628-9_25
ID  - Kumar2026
ER  -