LAPSE:2023.32071
Published Article

LAPSE:2023.32071
A Method for Battery Health Estimation Based on Charging Time Segment
April 19, 2023
Abstract
The problem of low accuracy and low convenience in the existing state of health (SOH) estimation method for vehicle lithium-ion batteries has become one of the important problems in the electric vehicle field. This paper proposes an improved cuckoo search particle filter (ICS-PF) algorithm based on a charging time segment from equal voltage data to estimate battery health status. Appropriate voltage ranges of charging time segments are selected according to the battery charging law, and in the meantime, the charging time segments are collected as a health indicator to establish the corresponding relationship with battery capacity attenuation value. An improved cuckoo search particle filter algorithm based on the traditional particle filter (PF) and cuckoo search (CS) algorithm is proposed by enhancing the search step size and discovery probability to estimate the capacity attenuation. The estimation result shows that this method is superior to the traditional particle filter and cuckoo search particle filter (CS-PF) method, as the maximum estimation error is less than 2%.
The problem of low accuracy and low convenience in the existing state of health (SOH) estimation method for vehicle lithium-ion batteries has become one of the important problems in the electric vehicle field. This paper proposes an improved cuckoo search particle filter (ICS-PF) algorithm based on a charging time segment from equal voltage data to estimate battery health status. Appropriate voltage ranges of charging time segments are selected according to the battery charging law, and in the meantime, the charging time segments are collected as a health indicator to establish the corresponding relationship with battery capacity attenuation value. An improved cuckoo search particle filter algorithm based on the traditional particle filter (PF) and cuckoo search (CS) algorithm is proposed by enhancing the search step size and discovery probability to estimate the capacity attenuation. The estimation result shows that this method is superior to the traditional particle filter and cuckoo search particle filter (CS-PF) method, as the maximum estimation error is less than 2%.
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Keywords
capacity attenuation, charging time segment, health indicator, improved cuckoo search
Subject
Suggested Citation
Liu SX, Zhou YF, Liu YL, Lian J, Huang LJ. A Method for Battery Health Estimation Based on Charging Time Segment. (2023). LAPSE:2023.32071
Author Affiliations
Liu SX: State Key Laboratory of Structural Analysis for Industrial Equipment, Faculty of Vehicle Engineering and Mechanics, School of Automotive Engineering, Dalian University of Technology, Dalian 116024, China
Zhou YF: State Key Laboratory of Structural Analysis for Industrial Equipment, Faculty of Vehicle Engineering and Mechanics, School of Automotive Engineering, Dalian University of Technology, Dalian 116024, China
Liu YL: State Key Laboratory of Structural Analysis for Industrial Equipment, Faculty of Vehicle Engineering and Mechanics, School of Automotive Engineering, Dalian University of Technology, Dalian 116024, China
Lian J: State Key Laboratory of Structural Analysis for Industrial Equipment, Faculty of Vehicle Engineering and Mechanics, School of Automotive Engineering, Dalian University of Technology, Dalian 116024, China
Huang LJ: State Key Laboratory of Structural Analysis for Industrial Equipment, Faculty of Vehicle Engineering and Mechanics, School of Automotive Engineering, Dalian University of Technology, Dalian 116024, China
Zhou YF: State Key Laboratory of Structural Analysis for Industrial Equipment, Faculty of Vehicle Engineering and Mechanics, School of Automotive Engineering, Dalian University of Technology, Dalian 116024, China
Liu YL: State Key Laboratory of Structural Analysis for Industrial Equipment, Faculty of Vehicle Engineering and Mechanics, School of Automotive Engineering, Dalian University of Technology, Dalian 116024, China
Lian J: State Key Laboratory of Structural Analysis for Industrial Equipment, Faculty of Vehicle Engineering and Mechanics, School of Automotive Engineering, Dalian University of Technology, Dalian 116024, China
Huang LJ: State Key Laboratory of Structural Analysis for Industrial Equipment, Faculty of Vehicle Engineering and Mechanics, School of Automotive Engineering, Dalian University of Technology, Dalian 116024, China
Journal Name
Energies
Volume
14
Issue
9
First Page
2612
Year
2021
Publication Date
2021-05-02
ISSN
1996-1073
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Original Submission
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PII: en14092612, Publication Type: Journal Article
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LAPSE:2023.32071
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https://doi.org/10.3390/en14092612
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Apr 19, 2023
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