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A comparative study of different features extracted from electrochemical impedance spectroscopy in state of health estimation for lithium-ion batteries

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  1. Xing, Xueqi & Yan, Tongtong & Xia, Min, 2025. "Adaptive shapley-embedded neural network ensemble for accurate state of health estimation using electrochemical impedance spectroscopy," Applied Energy, Elsevier, vol. 401(PC).
  2. Zhou, Wenbin & Cleaver, Christopher J. & Dunant, Cyrille F. & Allwood, Julian M. & Lin, Jianguo, 2023. "Cost, range anxiety and future electricity supply: A review of how today's technology trends may influence the future uptake of BEVs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
  3. Ko, Chi-Jyun & Chen, Kuo-Ching & Chen, Chih-Hung, 2025. "Advantageous characteristics of constant voltage charging: A good option to estimate battery states for lithium-ion batteries," Energy, Elsevier, vol. 322(C).
  4. Xinwei Sun & Yang Zhang & Yongcheng Zhang & Licheng Wang & Kai Wang, 2023. "Summary of Health-State Estimation of Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy," Energies, MDPI, vol. 16(15), pages 1-19, July.
  5. Moez Krichen & Yasir Basheer & Saeed Mian Qaisar & Asad Waqar, 2023. "A Survey on Energy Storage: Techniques and Challenges," Energies, MDPI, vol. 16(5), pages 1-29, February.
  6. Wu, Jian & Meng, Jinhao & Lin, Mingqiang & Wang, Wei & Wu, Ji & Stroe, Daniel-Ioan, 2024. "Lithium-ion battery state of health estimation using a hybrid model with electrochemical impedance spectroscopy," Reliability Engineering and System Safety, Elsevier, vol. 252(C).
  7. Zhang, Kui & Rayeem, Safwat Khair & Mai, Weijie & Tian, Jinpeng & Ma, Liang & Zhang, Tieling & Chung, C.Y., 2025. "Enhancing battery health estimation using incomplete charging curves and knowledge-guided deep learning," Reliability Engineering and System Safety, Elsevier, vol. 262(C).
  8. 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).
  9. Liu, Yupeng & Yang, Lijun & Liao, Ruijin & Hu, Chengyu & Xiao, Yanlin & He, Chunwang & Wu, Xu & Zhang, Yuan & Li, Siquan, 2025. "Degradation mechanism of sodium-ion batteries and state of health estimation via electrochemical impedance spectroscopy under temperature disturbances," Energy, Elsevier, vol. 332(C).
  10. Leila Amani & Amir Sheikhahmadi & Yavar Vafaee, 2025. "An Enhanced Method to Estimate State of Health of Li-Ion Batteries Using Feature Accretion Method (FAM)," Energies, MDPI, vol. 18(19), pages 1-27, September.
  11. Wu, Zezhou & He, Qiufeng & Li, Jiarun & Bi, Guoqiang & Antwi-Afari, Maxwell Fordjour, 2023. "Public attitudes and sentiments towards new energy vehicles in China: A text mining approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 178(C).
  12. Zuo, Hongyan & Liang, Jingwei & Zhang, Bin & Wei, Kexiang & Zhu, Hong & Tan, Jiqiu, 2023. "Intelligent estimation on state of health of lithium-ion power batteries based on failure feature extraction," Energy, Elsevier, vol. 282(C).
  13. Jiang, Bo & Tao, Siyi & Wang, Xueyuan & Zhu, Jiangong & Wei, Xuezhe & Dai, Haifeng, 2023. "Mechanics-based state of charge estimation for lithium-ion pouch battery using deep learning technique," Energy, Elsevier, vol. 278(PA).
  14. Fan, Wenjun & Wang, Xueyuan & Yuan, Yongjun & Zhou, Xiao & Jiang, Bo & Qian, Long & Wei, Xuezhe & Dai, Haifeng, 2026. "Consistency sorting of retired lithium-ion batteries: From the perspective of maximizing remaining useful discharge," Applied Energy, Elsevier, vol. 402(PB).
  15. Qian, Guangjun & Zhu, Zhicheng & Sun, Yuedong & Zheng, Yuejiu & Han, Xuebing & Ouyang, Minggao, 2025. "Cross-capacity internal temperature estimation in lithium-ion batteries using multiple impedance features from the negative electrode," Applied Energy, Elsevier, vol. 396(C).
  16. Yang, Yongsong & Xu, Yuchen & Nie, Yuwei & Li, Jianming & Liu, Shizhuo & Zhao, Lijun & Yu, Quanqing & Zhang, Chengming, 2024. "Deep transfer learning enables battery state of charge and state of health estimation," Energy, Elsevier, vol. 294(C).
  17. Wang, Tong & Wu, Yan & Zhu, Keming & Cen, Jianmeng & Wang, Shaohong & Huang, Yuqi, 2025. "Deep learning and polarization equilibrium based state of health estimation for lithium-ion battery using partial charging data," Energy, Elsevier, vol. 317(C).
  18. Hojin Cheon & Jihun Jeon & Byungil Jung & Hongseok Kim, 2025. "Battery Health Diagnosis via Neural Surrogate Model: From Lab to Field," Energies, MDPI, vol. 18(9), pages 1-15, May.
  19. Fu, Shiyi & Fan, Hongtao & Jin, Zhaorui & Ji, Fan & Tao, Yulin & Dong, Yachao & Chen, Xunyuan & Shao, Minghao & Yuan, Shuyu & Wang, Yu & Sun, Yaojie, 2026. "Recent progress in state of health estimation for lithium-ion batteries: From laboratory to practical application," Renewable and Sustainable Energy Reviews, Elsevier, vol. 226(PB).
  20. Zhaosheng Zhang & Shuo Wang & Ni Lin & Zhenpo Wang & Peng Liu, 2023. "State of Health Estimation of Lithium-Ion Batteries in Electric Vehicles Based on Regional Capacity and LGBM," Sustainability, MDPI, vol. 15(3), pages 1-20, January.
  21. Chen, Kui & Luo, Yang & Long, Zhou & Li, Yang & Nie, Guangbo & Liu, Kai & Xin, Dongli & Gao, Guoqiang & Wu, Guangning, 2025. "Big data-driven prognostics and health management of lithium-ion batteries:A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 214(C).
  22. Ko, Chi-Jyun & Chen, Kuo-Ching, 2024. "Constructing battery impedance spectroscopy using partial current in constant-voltage charging or partial relaxation voltage," Applied Energy, Elsevier, vol. 356(C).
  23. Qian, Guangjun & Zhu, Zhicheng & Guo, Peng & Liu, Lifang & Sun, Yuedong & Zheng, Yuejiu & Han, Xuebing & Ouyang, Minggao, 2026. "Non-destructive and adaptive negative electrode impedance estimation of lithium-ion batteries using ensemble learning," Applied Energy, Elsevier, vol. 402(PB).
  24. Yang, Bowen & Wang, Dafang & Yu, Beike & Wang, Facheng & Chen, Shiqin & Sun, Xu & Dong, Haosong, 2024. "Research on online passive electrochemical impedance spectroscopy and its outlook in battery management," Applied Energy, Elsevier, vol. 363(C).
  25. Dai, Houde & Wang, Jiaxin & Huang, Yiyang & Lai, Yuan & Zhu, Liqi, 2024. "Lightweight state-of-health estimation of lithium-ion batteries based on statistical feature optimization," Renewable Energy, Elsevier, vol. 222(C).
  26. Wei Li & Hang Li & Zheng He & Weijie Ji & Jing Zeng & Xue Li & Yiyong Zhang & Peng Zhang & Jinbao Zhao, 2022. "Electrochemical Failure Results Inevitable Capacity Degradation in Li-Ion Batteries—A Review," Energies, MDPI, vol. 15(23), pages 1-28, December.
  27. Chen, Wei & Bai, Jianshu & Wang, Guohua & Xie, Ningning & Ma, Linrui & Wang, Yazhou & Zhang, Tong & Xue, Xiaodai, 2023. "First and second law analysis and operational mode optimization of the compression process for an advanced adiabatic compressed air energy storage based on the established comprehensive dynamic model," Energy, Elsevier, vol. 263(PC).
  28. 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).
  29. Xia, Xuelei & Chen, Yang & Shen, Jiangwei & Liu, Yonggang & Zhang, Yuanjian & Chen, Zheng & Wei, Fuxing, 2025. "State of health estimation for lithium-ion batteries based on impedance feature selection and improved support vector regression," Energy, Elsevier, vol. 326(C).
  30. Chen, Bingyang & Wang, Kai & Xu, Degang & Xia, Juan & Fan, Lulu & Zhou, Jiehan, 2024. "Global–local attention network and value-informed federated strategy for predicting power battery state of health," Energy, Elsevier, vol. 313(C).
  31. Ou, Yuxin & Zhu, Jiangong & Chen, Siqi & Wu, Hang & Dai, Haifeng & Wei, Xuezhe, 2026. "Prospect and critical technologies for “fast charging + re-modulization” roadmap of the power battery system in electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 225(C).
  32. Han, Dongho & Kwon, Sanguk & Lee, Miyoung & Kim, Jonghoon & Yoo, Kisoo, 2023. "Electrochemical impedance spectroscopy image transformation-based convolutional neural network for diagnosis of external environment classification affecting abnormal aging of Li-ion batteries," Applied Energy, Elsevier, vol. 345(C).
  33. Ming Zhang & Yanshuo Liu & Dezhi Li & Xiaoli Cui & Licheng Wang & Liwei Li & Kai Wang, 2023. "Electrochemical Impedance Spectroscopy: A New Chapter in the Fast and Accurate Estimation of the State of Health for Lithium-Ion Batteries," Energies, MDPI, vol. 16(4), pages 1-16, February.
  34. Zhou, Yong & Dong, Guangzhong & Tan, Qianqian & Han, Xueyuan & Chen, Chunlin & Wei, Jingwen, 2023. "State of health estimation for lithium-ion batteries using geometric impedance spectrum features and recurrent Gaussian process regression," Energy, Elsevier, vol. 262(PB).
  35. Zhang, Liping & Chen, Caiyi & Luo, Delin, 2025. "A comprehensive framework of synchronous SOC-SOH joint estimation for lithium-ion battery with multi-depth expert networks," Energy, Elsevier, vol. 339(C).
  36. Zhu, Yuli & Jiang, Bo & Zhu, Jiangong & Wang, Xueyuan & Wang, Rong & Wei, Xuezhe & Dai, Haifeng, 2023. "Adaptive state of health estimation for lithium-ion batteries using impedance-based timescale information and ensemble learning," Energy, Elsevier, vol. 284(C).
  37. Siyi Tao & Bo Jiang & Xuezhe Wei & Haifeng Dai, 2023. "A Systematic and Comparative Study of Distinct Recurrent Neural Networks for Lithium-Ion Battery State-of-Charge Estimation in Electric Vehicles," Energies, MDPI, vol. 16(4), pages 1-17, February.
  38. Zhongxian Sun & Weilin He & Junlei Wang & Xin He, 2024. "State of Health Estimation for Lithium-Ion Batteries with Deep Learning Approach and Direct Current Internal Resistance," Energies, MDPI, vol. 17(11), pages 1-14, May.
  39. Li, Sida & Wei, Xuezhe & Jiang, Shangfeng & Yuan, Hao & Ming, Pingwen & Wang, Xueyuan & Dai, Haifeng, 2022. "Hydrogen crossover diagnosis for fuel cell stack: An electrochemical impedance spectroscopy based method," Applied Energy, Elsevier, vol. 325(C).
  40. Jia, Xianyi & Zhu, Jiangong & Knapp, Michael & Wang, Xiuwu & Yu, Chao & Xu, Wentao & Wu, Hang & Ehrenberg, Helmut & Wei, Xuezhe & Dai, Haifeng, 2026. "A review of battery failure: classification, mechanisms, analysis, and management," Renewable and Sustainable Energy Reviews, Elsevier, vol. 225(C).
  41. Hong, Jichao & Zhang, Huaqin & Zhang, Xinyang & Yang, Haixu & Chen, Yingjie & Wang, Facheng & Huang, Zhongguo & Wang, Wei, 2024. "Online accurate voltage prediction with sparse data for the whole life cycle of Lithium-ion batteries in electric vehicles," Applied Energy, Elsevier, vol. 369(C).
  42. Qian, Guangjun & Zheng, Yuejiu & Li, Xinyu & Sun, Yuedong & Han, Xuebing & Ouyang, Minggao, 2025. "State of health estimation for lithium-ion batteries using impedance-based simplified timescale information," Applied Energy, Elsevier, vol. 382(C).
  43. Wang, Yaxuan & Guo, Shilong & Cui, Yue & Deng, Liang & Zhao, Lei & Li, Junfu & Wang, Zhenbo, 2025. "A comprehensive review of machine learning-based state of health estimation for lithium-ion batteries: data, features, algorithms, and future challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 224(C).
  44. Miao, Mengqi & Yang, Pu & Yue, Shang & Zhou, Ruixu & Yu, Jianbo, 2024. "Multi-source self-supervised domain adaptation network for VRLA battery anomaly detection of data center under non-ideal conditions," Energy, Elsevier, vol. 299(C).
  45. Khosravi, Nima & Dowlatabadi, Masrour & Abdelghany, Muhammad Bakr & Tostado-Véliz, Marcos & Jurado, Francisco, 2024. "Enhancing battery management for HEVs and EVs: A hybrid approach for parameter identification and voltage estimation in lithium-ion battery models," Applied Energy, Elsevier, vol. 356(C).
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