A Method for Battery Health Estimation Based on Charging Time Segment

Shao-Xun Liu, Ya-Fu Zhou, Yan-Liang Liu, Jing Lian and Li-Jian Huang
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Shao-Xun Liu: 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
Ya-Fu Zhou: 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
Yan-Liang Liu: 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
Jing Lian: 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
Li-Jian Huang: 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

Energies, 2021, vol. 14, issue 9, 1-15

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%.

Keywords: health indicator; charging time segment; improved cuckoo search; capacity attenuation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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