Research on Lithium-Ion Battery State of Health Prediction Based on XGBoost–ARIMA Joint Optimization

Research on Lithium-Ion Battery State of Health Prediction Based on XGBoost–ARIMA Joint Optimization

Due to the complex electrochemical reactions within lithium-ion batteries and the uncertainties with respect to external environmental factors, accurately assessing their State of Health (SOH) remains a significant challenge. To improve the precision of SOH estimation, we propose an intelligent esti...

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Bibliographic Details
Main Authors: Chen Fei, Zhuo Lu, Weiwei Jiang, Liang Zhao, Fan Zhang
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Batteries
Subjects:
lithium-ion battery
feature extraction
state of health estimation
XGBoost algorithm
ARIMA model
Online Access:https://www.mdpi.com/2313-0105/11/6/207
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https://www.mdpi.com/2313-0105/11/6/207

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