Fast estimation method for lithium battery state of health based on incremental capacity curve peak characteristics

State of Health (SOH) estimation for lithium-ion batteries is a complex task, often plagued by issues such as slow estimation speed and high practical application difficulty in existing methods. In this paper, capacity is established as the criterion for defining State of Health. We conducted an analysis of Incremental Capacity (IC) curves, examining the characteristic features of the incremental capacity curves throughout the entire lifecycle of lithium-ion batteries at different temperatures and charge-discharge rates. The investigation revealed that the peaks in the incremental capacity curves correspond to nearly constant State of Charge (SOC) values. Building upon this discovery, a rapid SOH estimation method based on IC curves is proposed. The method was validated using data from the complete lifecycle of 18,650 cells, yielding a maximum SOH estimation error of 3%. Furthermore, the proposed method was applied to 240 cells in an energy storage battery pack at a specific station, resulting in a maximum SOH estimation error of 3.9%. This demonstrates the feasibility of the method under practical conditions, highlighting its high precision in SOH estimation. The proposed approach holds significant promise for real-time and rapid SOH estimation and detection, offering valuable insights for battery health monitoring.

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Publication Details

Journal
Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering
Published
2024-11-19
DOI
https://doi.org/10.1177/09544070241298988
Primary Topic
Advanced Battery Technologies Research
Type
article
Field-Weighted Citation Impact
0.00

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article

Fast estimation method for lithium battery state of health based on incremental capacity curve peak characteristics

Xintian Liu, Bixiong Huang, Dong Liu, Yan Xuan
Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering
Advanced Battery Technologies Research
article

Fast estimation method for lithium battery state of health based on incremental capacity curve peak characteristics

Xintian Liu, Bixiong Huang, Dong Liu, Yan Xuan
article en

Abstract

State of Health (SOH) estimation for lithium-ion batteries is a complex task, often plagued by issues such as slow estimation speed and high practical application difficulty in existing methods. In this paper, capacity is established as the criterion for defining State of Health. We conducted an analysis of Incremental Capacity (IC) curves, examining the characteristic features of the incremental capacity curves throughout the entire lifecycle of lithium-ion batteries at different temperatures and charge-discharge rates. The investigation revealed that the peaks in the incremental capacity curves correspond to nearly constant State of Charge (SOC) values. Building upon this discovery, a rapid SOH estimation method based on IC curves is proposed. The method was validated using data from the complete lifecycle of 18,650 cells, yielding a maximum SOH estimation error of 3%. Furthermore, the proposed method was applied to 240 cells in an energy storage battery pack at a specific station, resulting in a maximum SOH estimation error of 3.9%. This demonstrates the feasibility of the method under practical conditions, highlighting its high precision in SOH estimation. The proposed approach holds significant promise for real-time and rapid SOH estimation and detection, offering valuable insights for battery health monitoring.

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile EngineeringVol. 239(14)
Shanghai University of Engineering Science (CN)
National Natural Science Foundation of China
Affordable and clean energy
Openalex Percentile: Top 78%
Advanced Battery Technologies Research
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Fast estimation method for lithium battery state of health based on incremental capacity curve peak characteristics — Xintian Liu, Bixiong Huang, et al. · Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering (2024) | TGRS Research Map | TGRS