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An evaluation study of different modelling techniques for calendar ageing prediction of lithium-ion batteries

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  1. Wang, Shunli & Takyi-Aninakwa, Paul & Jin, Siyu & Yu, Chunmei & Fernandez, Carlos & Stroe, Daniel-Ioan, 2022. "An improved feedforward-long short-term memory modeling method for the whole-life-cycle state of charge prediction of lithium-ion batteries considering current-voltage-temperature variation," Energy, Elsevier, vol. 254(PA).
  2. Dwivedi, Shalini & Akula, Aparna & Pecht, Michael, 2024. "Predictive analytics for prolonging lithium-ion battery lifespan through informed storage conditions," Energy, Elsevier, vol. 308(C).
  3. Jiang, Bo & Zhu, Jiangong & Wang, Xueyuan & Wei, Xuezhe & Shang, Wenlong & Dai, Haifeng, 2022. "A comparative study of different features extracted from electrochemical impedance spectroscopy in state of health estimation for lithium-ion batteries," Applied Energy, Elsevier, vol. 322(C).
  4. Zhou, Yuekuan, 2024. "AI-driven battery ageing prediction with distributed renewable community and E-mobility energy sharing," Renewable Energy, Elsevier, vol. 225(C).
  5. Wang, Shunli & Fan, Yongcun & Jin, Siyu & Takyi-Aninakwa, Paul & Fernandez, Carlos, 2023. "Improved anti-noise adaptive long short-term memory neural network modeling for the robust remaining useful life prediction of lithium-ion batteries," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
  6. Rakshith Subramanya & Matti Yli-Ojanperä & Seppo Sierla & Taneli Hölttä & Jori Valtakari & Valeriy Vyatkin, 2021. "A Virtual Power Plant Solution for Aggregating Photovoltaic Systems and Other Distributed Energy Resources for Northern European Primary Frequency Reserves," Energies, MDPI, vol. 14(5), pages 1-23, February.
  7. 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).
  8. Shunli Wang & Pu Ren & Paul Takyi-Aninakwa & Siyu Jin & Carlos Fernandez, 2022. "A Critical Review of Improved Deep Convolutional Neural Network for Multi-Timescale State Prediction of Lithium-Ion Batteries," Energies, MDPI, vol. 15(14), pages 1-27, July.
  9. Suqi Zhang & Ningjing Zhang & Ziqi Zhang & Ying Chen, 2022. "Electric Power Load Forecasting Method Based on a Support Vector Machine Optimized by the Improved Seagull Optimization Algorithm," Energies, MDPI, vol. 15(23), pages 1-17, December.
  10. Guo, Yuanjun & Yang, Zhile & Liu, Kailong & Zhang, Yanhui & Feng, Wei, 2021. "A compact and optimized neural network approach for battery state-of-charge estimation of energy storage system," Energy, Elsevier, vol. 219(C).
  11. Ma, Qianli & Wei, Wei & Mei, Shengwei, 2024. "Health-aware coordinate long-term and short-term operation for BESS in energy and frequency regulation markets," Applied Energy, Elsevier, vol. 356(C).
  12. Tang, Xiaopeng & Liu, Kailong & Lu, Jingyi & Liu, Boyang & Wang, Xin & Gao, Furong, 2020. "Battery incremental capacity curve extraction by a two-dimensional Luenberger–Gaussian-moving-average filter," Applied Energy, Elsevier, vol. 280(C).
  13. Li, Alan G. & West, Alan C. & Preindl, Matthias, 2022. "Towards unified machine learning characterization of lithium-ion battery degradation across multiple levels: A critical review," Applied Energy, Elsevier, vol. 316(C).
  14. Arafat, M.Y. & Hossain, M.J. & Alam, Md Morshed, 2024. "Machine learning scopes on microgrid predictive maintenance: Potential frameworks, challenges, and prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 190(PA).
  15. S, Vignesh & Che, Hang Seng & Selvaraj, Jeyraj & Tey, Kok Soon & Lee, Jia Woon & Shareef, Hussain & Errouissi, Rachid, 2024. "State of Health (SoH) estimation methods for second life lithium-ion battery—Review and challenges," Applied Energy, Elsevier, vol. 369(C).
  16. Ali Kadivar & Kaveh Niayesh, 2020. "Effects of Fast Elongation on Switching Arcs Characteristics in Fast Air Switches," Energies, MDPI, vol. 13(18), pages 1-23, September.
  17. Lluís Trilla & Lluc Canals Casals & Jordi Jacas & Pol Paradell, 2022. "Dual Extended Kalman Filter for State of Charge Estimation of Lithium–Sulfur Batteries," Energies, MDPI, vol. 15(19), pages 1-14, September.
  18. Kuzhiyil, Jishnu Ayyangatu & Damoulas, Theodoros & Planella, Ferran Brosa & Widanage, W. Dhammika, 2025. "Lithium-ion battery degradation modelling using universal differential equations: Development of a cost-effective parameterisation methodology," Applied Energy, Elsevier, vol. 382(C).
  19. Chunxiang Zhu & Zhiwei He & Zhengyi Bao & Changcheng Sun & Mingyu Gao, 2023. "Prognosis of Lithium-Ion Batteries’ Remaining Useful Life Based on a Sequence-to-Sequence Model with Variational Mode Decomposition," Energies, MDPI, vol. 16(2), pages 1-16, January.
  20. Peng, Qiao & Li, Wei & Fowler, Michael & Chen, Tao & Jiang, Wei & Liu, Kailong, 2024. "Battery calendar degradation trajectory prediction: Data-driven implementation and knowledge inspiration," Energy, Elsevier, vol. 294(C).
  21. Li, Penghua & Zhang, Zijian & Grosu, Radu & Deng, Zhongwei & Hou, Jie & Rong, Yujun & Wu, Rui, 2022. "An end-to-end neural network framework for state-of-health estimation and remaining useful life prediction of electric vehicle lithium batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
  22. Jia Guo & Yaqi Li & Kjeld Pedersen & Daniel-Ioan Stroe, 2021. "Lithium-Ion Battery Operation, Degradation, and Aging Mechanism in Electric Vehicles: An Overview," Energies, MDPI, vol. 14(17), pages 1-22, August.
  23. Neha Bhushan & Saad Mekhilef & Kok Soon Tey & Mohamed Shaaban & Mehdi Seyedmahmoudian & Alex Stojcevski, 2022. "Overview of Model- and Non-Model-Based Online Battery Management Systems for Electric Vehicle Applications: A Comprehensive Review of Experimental and Simulation Studies," Sustainability, MDPI, vol. 14(23), pages 1-31, November.
  24. Chen, Long & Ding, Shicong & Wang, Li & Zhu, Feng & Zhu, Xiayu & Zhang, Songtong & Dai, Haifeng & He, Xiangming & Cao, Gaoping & Qiu, Jinyi & Zhang, Hao, 2024. "Electrochemical model boosting accurate prediction of calendar life for commercial LiFePO4|graphite cells by combining solid electrolyte interface side reactions," Applied Energy, Elsevier, vol. 376(PA).
  25. Amiri, Mahshid N. & Håkansson, Anne & Burheim, Odne S. & Lamb, Jacob J., 2024. "Lithium-ion battery digitalization: Combining physics-based models and machine learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 200(C).
  26. Zhao, Jingyuan & Qu, Xudong & Li, Yuqi & Nan, Jinrui & Burke, Andrew F., 2025. "Real-time prediction of battery remaining useful life using hybrid-fusion deep neural networks," Energy, Elsevier, vol. 328(C).
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