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- Title
State-of-Health Estimate for the Lithium-Ion Battery Using Chi-Square and ELM-LSTM.
- Authors
Jianfeng Jiang; Shaishai Zhao; Chaolong Zhang
- Abstract
The state-of-health (SOH) estimation is of extreme importance for the performance maximization and upgrading of lithium-ion battery. This paper is concerned with neural-network-enabled battery SOH indication and estimation. The insight that motivates this work is that the chi-square of battery voltages of each constant current-constant voltage phrase and mean temperature could reflect the battery capacity loss effectively. An ensemble algorithm composed of extreme learning machine (ELM) and long short-term memory (LSTM) neural network is utilized to capture the underlying correspondence between the SOH, mean temperature and chi-square of battery voltages. NASA battery data and battery pack data are used to demonstrate the estimation procedures and performance of the proposed approach. The results show that the proposed approach can estimate the battery SOH accurately. Meanwhile, comparative experiments are designed to compare the proposed approach with the separate used method, and the proposed approach shows better estimation performance in the comparisons.
- Subjects
LITHIUM-ion batteries; ARTIFICIAL neural networks; MACHINE learning; SHORT-term memory; CHI-squared test
- Publication
World Electric Vehicle Journal, 2021, Vol 12, Issue 4, p1
- ISSN
2032-6653
- Publication type
Article
- DOI
10.3390/wevj12040228