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An improved model combining machine learning and Kalman filtering architecture for state of charge estimation of lithium-ion batteries

Yan Li Min Ye Qiao Wang Gaoqi Lian Baozhou Xia

新能源与智能载运（英文）2025，Vol.3Issue(2)：73-83,11.

新能源与智能载运（英文）2025，Vol.3Issue(2)：73-83,11.DOI:10.1016/j.geits.2024.100163

An improved model combining machine learning and Kalman filtering architecture for state of charge estimation of lithium-ion batteries

Yan Li ¹Min Ye ¹Qiao Wang ²Gaoqi Lian ¹Baozhou Xia¹

作者信息

1. National Engineering Research Center for Highway Maintenance Equipment,Chang'an University,Xi'an,710064,China
2. Institute for Power Electronics and Electrical Drives(ISEA),RWTH Aachen University,Aachen,52062,Germany
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摘要

关键词

Lithium-ion battery/State of charge estimation/Support vector regression/Simulated annealing optimization/Kalman filter

Key words

Lithium-ion battery/State of charge estimation/Support vector regression/Simulated annealing optimization/Kalman filter

引用本文复制引用

Yan Li,Min Ye,Qiao Wang,Gaoqi Lian,Baozhou Xia..An improved model combining machine learning and Kalman filtering architecture for state of charge estimation of lithium-ion batteries[J].新能源与智能载运（英文）,2025,3(2):73-83,11.

基金项目

This research was funded by the Key Research and Development Program of Shaanxi Province(2023-GHYB-05 and 2023-YBSF-104). （2023-GHYB-05 and 2023-YBSF-104）

新能源与智能载运（英文）

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