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Novel battery state-of-health online estimation method using multiple health indicators and an extreme learning machine

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Cited by:

  1. Jianyu Zhang & Kang Li, 2024. "State-of-Health Estimation for Lithium-Ion Batteries in Hybrid Electric Vehicles—A Review," Energies, MDPI, vol. 17(22), pages 1-16, November.
  2. Wen, Jianping & Chen, Xing & Li, Xianghe & Li, Yikun, 2022. "SOH prediction of lithium battery based on IC curve feature and BP neural network," Energy, Elsevier, vol. 261(PA).
  3. Yang, Bo & Qian, Yucun & Li, Qiang & Chen, Qian & Wu, Jiyang & Luo, Enbo & Xie, Rui & Zheng, Ruyi & Yan, Yunfeng & Su, Shi & Wang, Jingbo, 2024. "Critical summary and perspectives on state-of-health of lithium-ion battery," Renewable and Sustainable Energy Reviews, Elsevier, vol. 190(PA).
  4. Shu, Xing & Li, Guang & Shen, Jiangwei & Lei, Zhenzhen & Chen, Zheng & Liu, Yonggang, 2020. "A uniform estimation framework for state of health of lithium-ion batteries considering feature extraction and parameters optimization," Energy, Elsevier, vol. 204(C).
  5. Ma, Qiuhui & Zheng, Ying & Yang, Weidong & Zhang, Yong & Zhang, Hong, 2021. "Remaining useful life prediction of lithium battery based on capacity regeneration point detection," Energy, Elsevier, vol. 234(C).
  6. Sahar Khaleghi & Yousef Firouz & Maitane Berecibar & Joeri Van Mierlo & Peter Van Den Bossche, 2020. "Ensemble Gradient Boosted Tree for SoH Estimation Based on Diagnostic Features," Energies, MDPI, vol. 13(5), pages 1-16, March.
  7. Tian, Yong & Dong, Qianyuan & Tian, Jindong & Li, Xiaoyu & Li, Guang & Mehran, Kamyar, 2023. "Capacity estimation of lithium-ion batteries based on optimized charging voltage section and virtual sample generation," Applied Energy, Elsevier, vol. 332(C).
  8. Jinhyeong Park & Munsu Lee & Gunwoo Kim & Seongyun Park & Jonghoon Kim, 2020. "Integrated Approach Based on Dual Extended Kalman Filter and Multivariate Autoregressive Model for Predicting Battery Capacity Using Health Indicator and SOC/SOH," Energies, MDPI, vol. 13(9), pages 1-20, April.
  9. Zhang, Xiaoxi & Pan, Yongjun & Zhou, Junxiao & Li, Zhixiong & Liao, Tianjun & Li, Jie, 2024. "Forward and reverse design of adhesive in batteries via dynamics and machine learning algorithms for enhanced mechanical safety," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
  10. Giovane Ronei Sylvestrin & Joylan Nunes Maciel & Marcio Luís Munhoz Amorim & João Paulo Carmo & José A. Afonso & Sérgio F. Lopes & Oswaldo Hideo Ando Junior, 2025. "State of the Art in Electric Batteries’ State-of-Health (SoH) Estimation with Machine Learning: A Review," Energies, MDPI, vol. 18(3), pages 1-77, February.
  11. Li, Xiaoyu & Yuan, Changgui & Wang, Zhenpo, 2020. "State of health estimation for Li-ion battery via partial incremental capacity analysis based on support vector regression," Energy, Elsevier, vol. 203(C).
  12. Jhonny Rodriguez Rahal & Alexander Schwarz & Benjamín Sahelices & Ronny Weis & Simon Duque Antón, 2025. "The asset administration shell as enabler for predictive maintenance: a review," Journal of Intelligent Manufacturing, Springer, vol. 36(1), pages 19-33, January.
  13. Li, Yong & Wang, Liye & Feng, Yanbiao & Liao, Chenglin & Yang, Jue, 2024. "An online state-of-health estimation method for lithium-ion battery based on linear parameter-varying modeling framework," Energy, Elsevier, vol. 298(C).
  14. Khan, Ayesha & Naqvi, Ijaz Haider & Bhargava, Cherry & Lin, Chun-Pang & Boles, Steven Tyler & Kong, Lingxi & Pecht, Michael, 2025. "Safety and reliability analysis of lithium-ion batteries with real-time health monitoring," Renewable and Sustainable Energy Reviews, Elsevier, vol. 212(C).
  15. Theissler, Andreas & Pérez-Velázquez, Judith & Kettelgerdes, Marcel & Elger, Gordon, 2021. "Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
  16. Li, Yi & Zou, Changfu & Berecibar, Maitane & Nanini-Maury, Elise & Chan, Jonathan C.-W. & van den Bossche, Peter & Van Mierlo, Joeri & Omar, Noshin, 2018. "Random forest regression for online capacity estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 232(C), pages 197-210.
  17. Khaleghi, Sahar & Karimi, Danial & Beheshti, S. Hamidreza & Hosen, Md. Sazzad & Behi, Hamidreza & Berecibar, Maitane & Van Mierlo, Joeri, 2021. "Online health diagnosis of lithium-ion batteries based on nonlinear autoregressive neural network," Applied Energy, Elsevier, vol. 282(PA).
  18. Xu, Tingting & Peng, Zhen & Wu, Lifeng, 2021. "A novel data-driven method for predicting the circulating capacity of lithium-ion battery under random variable current," Energy, Elsevier, vol. 218(C).
  19. Li, Guanzheng & Li, Bin & Li, Chao & Wang, Shuai, 2023. "State-of-health rapid estimation for lithium-ion battery based on an interpretable stacking ensemble model with short-term voltage profiles," Energy, Elsevier, vol. 263(PE).
  20. Fan, Yuqian & Zhao, Jifei & Li, Yi & Wang, Jianping & Yang, Fangfang & Tan, Xiaojun, 2025. "Integrated framework for battery cell state-of-health estimation in complex modules: Combining current distribution analysis and novel terminal voltage estimation L-EKF modeling," Energy, Elsevier, vol. 314(C).
  21. Lai, Xin & Yi, Wei & Cui, Yifan & Qin, Chao & Han, Xuebing & Sun, Tao & Zhou, Long & Zheng, Yuejiu, 2021. "Capacity estimation of lithium-ion cells by combining model-based and data-driven methods based on a sequential extended Kalman filter," Energy, Elsevier, vol. 216(C).
  22. Dong, Zhe & Liu, Miao & Guo, Zhiwu & Huang, Xiaojin & Zhang, Yajun & Zhang, Zuoyi, 2019. "Adaptive state-observer for monitoring flexible nuclear reactors," Energy, Elsevier, vol. 171(C), pages 893-909.
  23. Jianqiang Gong & Bin Xu & Fanghua Chen & Gang Zhou, 2025. "Predictive Modeling for Electric Vehicle Battery State of Health: A Comprehensive Literature Review," Energies, MDPI, vol. 18(2), pages 1-37, January.
  24. Singh, Karanjot & Tjahjowidodo, Tegoeh & Boulon, Loïc & Feroskhan, Mir, 2022. "Framework for measurement of battery state-of-health (resistance) integrating overpotential effects and entropy changes using energy equilibrium," Energy, Elsevier, vol. 239(PA).
  25. Lin Chen & Deqian Chen & Manping He & Haihong Pan & Bing Ji, 2025. "A Novel Online State-of-Health Estimation Method for Lithium-Ion Batteries with Multi-Input Metabolic Long Short-Term Memory Framework," Energies, MDPI, vol. 18(5), pages 1-18, February.
  26. Zhang, Chaolong & Luo, Laijin & Yang, Zhong & Du, Bolun & Zhou, Ziheng & Wu, Ji & Chen, Liping, 2024. "Flexible method for estimating the state of health of lithium-ion batteries using partial charging segments," Energy, Elsevier, vol. 295(C).
  27. Li, Yi & Liu, Kailong & Foley, Aoife M. & Zülke, Alana & Berecibar, Maitane & Nanini-Maury, Elise & Van Mierlo, Joeri & Hoster, Harry E., 2019. "Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
  28. Ma’d El-Dalahmeh & Maher Al-Greer & Mo’ath El-Dalahmeh & Michael Short, 2020. "Time-Frequency Image Analysis and Transfer Learning for Capacity Prediction of Lithium-Ion Batteries," Energies, MDPI, vol. 13(20), pages 1-19, October.
  29. Zhang, Hao & Gao, Jingyi & Kang, Le & Zhang, Yi & Wang, Licheng & Wang, Kai, 2023. "State of health estimation of lithium-ion batteries based on modified flower pollination algorithm-temporal convolutional network," Energy, Elsevier, vol. 283(C).
  30. Zhou, Yuekuan, 2024. "AI-driven battery ageing prediction with distributed renewable community and E-mobility energy sharing," Renewable Energy, Elsevier, vol. 225(C).
  31. Li, Xiaoyu & Yuan, Changgui & Li, Xiaohui & Wang, Zhenpo, 2020. "State of health estimation for Li-Ion battery using incremental capacity analysis and Gaussian process regression," Energy, Elsevier, vol. 190(C).
  32. Wang, Huan & Li, Yan-Fu & Zhang, Ying, 2023. "Bioinspired spiking spatiotemporal attention framework for lithium-ion batteries state-of-health estimation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
  33. Xue, Jingsong & Ma, Wentao & Feng, Xiaoyang & Guo, Peng & Guo, Yaosong & Hu, Xianzhi & Chen, Badong, 2023. "Stacking integrated learning model via ELM and GRU with mixture correntropy loss for robust state of health estimation of lithium-ion batteries," Energy, Elsevier, vol. 284(C).
  34. Deng, Yuanwang & Ying, Hejie & E, Jiaqiang & Zhu, Hao & Wei, Kexiang & Chen, Jingwei & Zhang, Feng & Liao, Gaoliang, 2019. "Feature parameter extraction and intelligent estimation of the State-of-Health of lithium-ion batteries," Energy, Elsevier, vol. 176(C), pages 91-102.
  35. Zhou, Yanting & Wang, Yanan & Wang, Kai & Kang, Le & Peng, Fei & Wang, Licheng & Pang, Jinbo, 2020. "Hybrid genetic algorithm method for efficient and robust evaluation of remaining useful life of supercapacitors," Applied Energy, Elsevier, vol. 260(C).
  36. Meng, Jinhao & Cai, Lei & Stroe, Daniel-Ioan & Luo, Guangzhao & Sui, Xin & Teodorescu, Remus, 2019. "Lithium-ion battery state-of-health estimation in electric vehicle using optimized partial charging voltage profiles," Energy, Elsevier, vol. 185(C), pages 1054-1062.
  37. Jiakun An & Wei Guo & Tingyan Lv & Ziheng Zhao & Chunguang He & Hongshan Zhao, 2023. "Joint Prediction of the State of Charge and the State of Health of Lithium-Ion Batteries Based on the PSO-XGBoost Algorithm," Energies, MDPI, vol. 16(10), pages 1-14, May.
  38. Zhang, Zhendong & Kong, Xiangdong & Zheng, Yuejiu & Zhou, Long & Lai, Xin, 2019. "Real-time diagnosis of micro-short circuit for Li-ion batteries utilizing low-pass filters," Energy, Elsevier, vol. 166(C), pages 1013-1024.
  39. Li, Yang & Wang, Shunli & Chen, Lei & Qi, Chuangshi & Fernandez, Carlos, 2023. "Multiple layer kernel extreme learning machine modeling and eugenics genetic sparrow search algorithm for the state of health estimation of lithium-ion batteries," Energy, Elsevier, vol. 282(C).
  40. Ethelbert Ezemobi & Andrea Tonoli & Mario Silvagni, 2021. "Battery State of Health Estimation with Improved Generalization Using Parallel Layer Extreme Learning Machine," Energies, MDPI, vol. 14(8), pages 1-15, April.
  41. Muhammed Cavus & Dilum Dissanayake & Margaret Bell, 2025. "Next Generation of Electric Vehicles: AI-Driven Approaches for Predictive Maintenance and Battery Management," Energies, MDPI, vol. 18(5), pages 1-41, February.
  42. Chen, Lin & Wang, Huimin & Liu, Bohao & Wang, Yijue & Ding, Yunhui & Pan, Haihong, 2021. "Battery state-of-health estimation based on a metabolic extreme learning machine combining degradation state model and error compensation," Energy, Elsevier, vol. 215(PA).
  43. Khaleghi, Sahar & Hosen, Md Sazzad & Karimi, Danial & Behi, Hamidreza & Beheshti, S. Hamidreza & Van Mierlo, Joeri & Berecibar, Maitane, 2022. "Developing an online data-driven approach for prognostics and health management of lithium-ion batteries," Applied Energy, Elsevier, vol. 308(C).
  44. Yong Tian & Qianyuan Dong & Jindong Tian & Xiaoyu Li, 2023. "Capacity Estimation of Lithium-Ion Batteries Based on Multiple Small Voltage Sections and BP Neural Networks," Energies, MDPI, vol. 16(2), pages 1-18, January.
  45. Shu, Xing & Shen, Jiangwei & Chen, Zheng & Zhang, Yuanjian & Liu, Yonggang & Lin, Yan, 2022. "Remaining capacity estimation for lithium-ion batteries via co-operation of multi-machine learning algorithms," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
  46. Rauf, Huzaifa & Khalid, Muhammad & Arshad, Naveed, 2022. "Machine learning in state of health and remaining useful life estimation: Theoretical and technological development in battery degradation modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
  47. Yu, Miao & Zhu, Yuhao & Gu, Xin & Li, Jinglun & Shang, Yunlong, 2024. "Co-estimation and definition for states of health and charge of lithium-ion batteries using expansion," Energy, Elsevier, vol. 308(C).
  48. Penelope K. Jones & Ulrich Stimming & Alpha A. Lee, 2022. "Impedance-based forecasting of lithium-ion battery performance amid uneven usage," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
  49. Kurucan, Mehmet & Özbaltan, Mete & Yetgin, Zeki & Alkaya, Alkan, 2024. "Applications of artificial neural network based battery management systems: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
  50. Piotr Szewczyk & Andrzej Łebkowski, 2022. "Comparative Studies on Batteries for the Electrochemical Energy Storage in the Delivery Vehicle," Energies, MDPI, vol. 15(24), pages 1-28, December.
  51. Li, Wei & Li, Yongsheng & Garg, Akhil & Gao, Liang, 2024. "Enhancing real-time degradation prediction of lithium-ion battery: A digital twin framework with CNN-LSTM-attention model," Energy, Elsevier, vol. 286(C).
  52. Park, Jinhyeong & Kim, Kunwoo & Park, Seongyun & Baek, Jongbok & Kim, Jonghoon, 2021. "Complementary cooperative SOC/capacity estimator based on the discrete variational derivative combined with the DEKF for electric power applications," Energy, Elsevier, vol. 232(C).
  53. Matthieu Dubarry & David Beck, 2021. "Analysis of Synthetic Voltage vs. Capacity Datasets for Big Data Li-ion Diagnosis and Prognosis," Energies, MDPI, vol. 14(9), pages 1-24, April.
  54. Jen-Hao Teng & Rong-Jhang Chen & Ping-Tse Lee & Che-Wei Hsu, 2023. "Accurate and Efficient SOH Estimation for Retired Batteries," Energies, MDPI, vol. 16(3), pages 1-17, January.
  55. Ma, Yan & Li, Jiaqi & Gao, Jinwu & Chen, Hong, 2024. "State of health prediction of lithium-ion batteries under early partial data based on IWOA-BiLSTM with single feature," Energy, Elsevier, vol. 295(C).
  56. Yang, Bo & Guo, Zhengxun & Yang, Yi & Chen, Yijun & Zhang, Rui & Su, Keyi & Shu, Hongchun & Yu, Tao & Zhang, Xiaoshun, 2021. "Extreme learning machine based meta-heuristic algorithms for parameter extraction of solid oxide fuel cells," Applied Energy, Elsevier, vol. 303(C).
  57. Meng-Xiang Yan & Zhi-Hui Deng & Lianfeng Lai & Yong-Hong Xu & Liang Tong & Hong-Guang Zhang & Yi-Yang Li & Ming-Hui Gong & Guo-Ju Liu, 2025. "A Sustainable SOH Prediction Model for Lithium-Ion Batteries Based on CPO-ELM-ABKDE with Uncertainty Quantification," Sustainability, MDPI, vol. 17(11), pages 1-28, June.
  58. Meng, Jinhao & Cai, Lei & Stroe, Daniel-Ioan & Ma, Junpeng & Luo, Guangzhao & Teodorescu, Remus, 2020. "An optimized ensemble learning framework for lithium-ion Battery State of Health estimation in energy storage system," Energy, Elsevier, vol. 206(C).
  59. Shu, Xing & Li, Guang & Shen, Jiangwei & Lei, Zhenzhen & Chen, Zheng & Liu, Yonggang, 2020. "An adaptive multi-state estimation algorithm for lithium-ion batteries incorporating temperature compensation," Energy, Elsevier, vol. 207(C).
  60. Jiang, Nanhua & Zhang, Jiawei & Jiang, Weiran & Ren, Yao & Lin, Jing & Khoo, Edwin & Song, Ziyou, 2024. "Driving behavior-guided battery health monitoring for electric vehicles using extreme learning machine," Applied Energy, Elsevier, vol. 364(C).
  61. Chen, Lin & Ding, Yunhui & Liu, Bohao & Wu, Shuxiao & Wang, Yaodong & Pan, Haihong, 2022. "Remaining useful life prediction of lithium-ion battery using a novel particle filter framework with grey neural network," Energy, Elsevier, vol. 244(PA).
  62. Sui, Xin & He, Shan & Vilsen, Søren B. & Meng, Jinhao & Teodorescu, Remus & Stroe, Daniel-Ioan, 2021. "A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery," Applied Energy, Elsevier, vol. 300(C).
  63. Qian, Cheng & Xu, Binghui & Chang, Liang & Sun, Bo & Feng, Qiang & Yang, Dezhen & Ren, Yi & Wang, Zili, 2021. "Convolutional neural network based capacity estimation using random segments of the charging curves for lithium-ion batteries," Energy, Elsevier, vol. 227(C).
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