State of Charge Estimation of Lithium-Ion Battery for EV Using AI Algorithms

Khayri Abu Sayf; Dr Hammad Nazir

State of Charge Estimation of Lithium-Ion Battery for EV Using AI Algorithms

Faculty of Computing, Engineering and Science

Research output: Contribution to conference › Poster › peer-review

Abstract

The State of Charge (SOC) estimation of Lithium-Ion (Li-ion) batteries is a critical aspect in the efficient operation of Electric Vehicles (EVs). Accurate SOC estimation ensures optimal utilization of battery energy, enhances battery longevity, and improves overall vehicle performance. With the growing demand for EVs and the increasing emphasis on sustainability, there is a pressing need for precise SOC estimation methods. In this research Six different algorithms will be utilised, including ANN, SVM, LR, GPR, Ensemble bagging, and Ensemble boosting algorithms.

Original language	English
Publication status	Published - 1 Jun 2025

UN SDGs

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

Cite this

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title = "State of Charge Estimation of Lithium-Ion Battery for EV Using AI Algorithms",

abstract = "The State of Charge (SOC) estimation of Lithium-Ion (Li-ion) batteries is a critical aspect in the efficient operation of Electric Vehicles (EVs). Accurate SOC estimation ensures optimal utilization of battery energy, enhances battery longevity, and improves overall vehicle performance. With the growing demand for EVs and the increasing emphasis on sustainability, there is a pressing need for precise SOC estimation methods. In this research Six different algorithms will be utilised, including ANN, SVM, LR, GPR, Ensemble bagging, and Ensemble boosting algorithms.",

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AU - Nazir, Dr Hammad

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N2 - The State of Charge (SOC) estimation of Lithium-Ion (Li-ion) batteries is a critical aspect in the efficient operation of Electric Vehicles (EVs). Accurate SOC estimation ensures optimal utilization of battery energy, enhances battery longevity, and improves overall vehicle performance. With the growing demand for EVs and the increasing emphasis on sustainability, there is a pressing need for precise SOC estimation methods. In this research Six different algorithms will be utilised, including ANN, SVM, LR, GPR, Ensemble bagging, and Ensemble boosting algorithms.

AB - The State of Charge (SOC) estimation of Lithium-Ion (Li-ion) batteries is a critical aspect in the efficient operation of Electric Vehicles (EVs). Accurate SOC estimation ensures optimal utilization of battery energy, enhances battery longevity, and improves overall vehicle performance. With the growing demand for EVs and the increasing emphasis on sustainability, there is a pressing need for precise SOC estimation methods. In this research Six different algorithms will be utilised, including ANN, SVM, LR, GPR, Ensemble bagging, and Ensemble boosting algorithms.

M3 - Poster

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