Bayesian optimized support vector regression with a Gaussian kernel for accurate prediction of the state of health of lithium-ion batteries used for electric vehicle applications

Selvar aj Vedhanayaki; Vairavasundara m Indragandhi

doi:10.1016/j.gloei.2025.02.003

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Bayesian optimized support vector regression with a Gaussian kernel for accurate prediction of the state of health of lithium-ion batteries used for electric vehicle applications

Integrated Energy System | 更新时间：2025-12-10

- Bayesian optimized support vector regression with a Gaussian kernel for accurate prediction of the state of health of lithium-ion batteries used for electric vehicle applications
- Bayesian optimized support vector regression with a Gaussian kernel for accurate prediction of the state of health of lithium-ion batteries used for electric vehicle applications
- 全球能源互联网(英文) 2025年第5期
- 作者机构：
  
  Department of Electrical Engineering, U.V.Patel College of Engineering, Ganpat University, Kherva,India,384012
- 作者简介：
- 基金信息：
- DOI：10.1016/j.gloei.2025.02.003
  中图分类号：
- 纸质出版：2025
- 稿件说明：
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Selvar aj Vedhanayaki, Vairavasundara m Indragandhi. Bayesian optimized support vector regression with a Gaussian kernel for accurate prediction of the state of health of lithium-ion batteries used for electric vehicle applications[J]. 全球能源互联网(英文), 2025,(5).

Selvar aj Vedhanayaki, Vairavasundara m Indragandhi. Bayesian optimized support vector regression with a Gaussian kernel for accurate prediction of the state of health of lithium-ion batteries used for electric vehicle applications[J]. Global Energy Interconnection, 2025, (5).
Selvar aj Vedhanayaki, Vairavasundara m Indragandhi. Bayesian optimized support vector regression with a Gaussian kernel for accurate prediction of the state of health of lithium-ion batteries used for electric vehicle applications[J]. 全球能源互联网(英文), 2025,(5). DOI： 10.1016/j.gloei.2025.02.003.

Selvar aj Vedhanayaki, Vairavasundara m Indragandhi. Bayesian optimized support vector regression with a Gaussian kernel for accurate prediction of the state of health of lithium-ion batteries used for electric vehicle applications[J]. Global Energy Interconnection, 2025, (5). DOI： 10.1016/j.gloei.2025.02.003.

摘要

Abstract

The state of health SoH of lithium ion batteries plays a predominant role in ensuring the safe and reliable operation of electric vehicles.In this

a novel SoH estimation approach using support vector regression with a Gaussian kernel optimized using the Bayesian optimization technique (BO-SVR with a Gaussian kernel) was proposed.Unlike

traditional approaches that use the internal resistance

and battery capacity as input parameters

this study utilized the equivalent discharging voltage difference interval and equivalent charging voltage difference interval

as they capture the dynamic voltage characteristics associated with the battery degradation.The model was simulated using MATLAB 2023a.The mean absolute error

root mean squared error

and mean squared error were considered as performance indicators.The simulation results indicated that the proposed BO-SVR with a Gaussian kernel model had superior performance to other kernel SVR and Gaussian Process Regression models

with a reduced RMSE of 0.0082

thus demonstrating its potential to predict the SoH more accurately.

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