Online Estimating State of Health of Lithium-Ion Batteries Using Hierarchical Extreme Learning Machine

• Published in: IEEE transactions on transportation electrification Vol. 8; no. 1; pp. 965 - 975
• Main Authors: Chen, Lin, Ding, Yunhui, Wang, Huimin, Wang, Yijue, Liu, Bohao, Wu, Shuxiao, Li, Hao, Pan, Haihong
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.03.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Batteries; Battery charge measurement; Datasets; Degradation; Dynamic loads; Estimation; Health indicator (HI); hierarchical extreme learning machine (HELM); Lithium-ion batteries; lithium-ion battery; Machine learning; Neural networks; Product safety; Rechargeable batteries; Resistance; state of health (SoH); Temperature measurement; Training
• DOI: 10.1109/TTE.2021.3107727
• ISSN: 2332-7782; 2577-4212

Battery state-of-health (SoH) monitoring is of great importance to ensure the safety and reliability of battery systems. This study proposed an innovative SoH estimation method using hierarchical extreme learning machine (HELM) to improve the estimation robustness and accuracy without the complex parameter model was directly applied to establish the HELM-oriented online SoH estimation framework. First, the increase in mean ohmic resistance was constructed as a novel health indicator (HI) to characterize battery aging. Then, the HI was adopted for offline training to build an HELM model, which captures the underlying correlation between the extracted HI and capacity degradation. Finally, the datasets of four batteries at three different temperatures with dynamic loading profiles were used for validation. The results show that the SoH estimation errors are no more than 1.5%, while the training and estimation datasets are from the same temperature; when the SoH estimation is conducted at different temperatures, the maximum error is only 3.36%. The results indicate that the proposed method had good generalization and reliability for SoH estimation, which is applicable for dynamic scenarios with different temperatures.