Review of Lithium-Ion Battery State of Charge Estimation Methodologies for Electric Vehicle Application

With the ever-rising concerns over energy conservation, global warming, and climate change issues, extensive research on electric vehicles (EVs) is being actively performed. EVs are considered to be the most promising replacement of gasoline-based vehicles in reducing CO2 emissions in recent decades. The number of EV run by rechargeable batteries has been increased significantly due to enhanced performances and efficiencies. The lithium-ion battery has some advantageous features such as lightweight, long lifespan, low self-discharge, high voltage, high energy density, and low memory effect. However, EVs using lithium-ion batteries have drawbacks with regard to short mileage, slow charging, and high cost. Moreover, load variation, temperature variations, and battery aging can degrade EV performance and efficiency. Therefore, there is an urgent necessity to develop an efficient energy storage management system that can evaluate the charging state and health condition of lithium-ion batteries. The state of charge (SOC) is an essential parameter of EV which determines the remaining charge of lithium-ion batteries. Also, SOC provides information about the charging/discharging approach and thus protects the battery from being overcharged/over discharged. This paper comprehensively reviews the different estimation methodologies to evaluate SOC. The estimation approaches of SOC are discussed in detail on their algorithm, mathematical model, strength, weakness and error rate. Finally, the review delivers some important proposals for the future development of SOC estimation in EV application.

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Publication Details

Journal
International Journal of Engineering & Technology
Published
2026-04-27
DOI
https://doi.org/10.14419/ijet.v7i3.17.21909
Citations
1
Primary Topic
Advanced Battery Technologies Research
Type
article
Field-Weighted Citation Impact
0.00

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article

Review of Lithium-Ion Battery State of Charge Estimation Methodologies for Electric Vehicle Application

M.H.M. Saad, A. Ayob, A. Hussain, M.S. Hossain Lipu et al.
1 citations
International Journal of Engineering & Technology
Advanced Battery Technologies Research
article

Review of Lithium-Ion Battery State of Charge Estimation Methodologies for Electric Vehicle Application

M.H.M. Saad, A. Ayob, A. Hussain, M.S. Hossain Lipu, M.A. Hannan
article en
1 citations

Abstract

With the ever-rising concerns over energy conservation, global warming, and climate change issues, extensive research on electric vehicles (EVs) is being actively performed. EVs are considered to be the most promising replacement of gasoline-based vehicles in reducing CO2 emissions in recent decades. The number of EV run by rechargeable batteries has been increased significantly due to enhanced performances and efficiencies. The lithium-ion battery has some advantageous features such as lightweight, long lifespan, low self-discharge, high voltage, high energy density, and low memory effect. However, EVs using lithium-ion batteries have drawbacks with regard to short mileage, slow charging, and high cost. Moreover, load variation, temperature variations, and battery aging can degrade EV performance and efficiency. Therefore, there is an urgent necessity to develop an efficient energy storage management system that can evaluate the charging state and health condition of lithium-ion batteries. The state of charge (SOC) is an essential parameter of EV which determines the remaining charge of lithium-ion batteries. Also, SOC provides information about the charging/discharging approach and thus protects the battery from being overcharged/over discharged. This paper comprehensively reviews the different estimation methodologies to evaluate SOC. The estimation approaches of SOC are discussed in detail on their algorithm, mathematical model, strength, weakness and error rate. Finally, the review delivers some important proposals for the future development of SOC estimation in EV application.

International Journal of Engineering & TechnologyVol. 7(3.17)
Universiti Kebangsaan Malaysia
Affordable and clean energy
Openalex Percentile: Top 7%
Advanced Battery Technologies Research
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