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

English Version Reader LoginJournal Login

您当前的位置：

首页 >

文章列表页 >

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
- Global Energy Interconnection Issue 5, (2025)
- 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
  CLC：
- Published：2025
- 稿件说明：
移动端阅览
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：

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.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Enhancing photovoltaic power prediction using a CNN-LSTM-attention hybrid model with Bayesian hyperparameter optimization

Estimation of state of health based on charging characteristics and back-propagation neural networks with improved atom search optimization algorithm

Related Author

Ning Zhou

Bowen Shang

Mingming Xu

Lei Peng

Yafei Zhang

Yu Zhang

Yuhang Zhang

Tiezhou Wu

Related Institution

State Grid Henan Electric Power Research Institute

State Grid Henan Electric Power Corporation

Hubei University of Technology,Hubei Key Laboratory of Solar Energy Efficient Utilization and Energy Storage Operation Control

AI问答

⁰