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RESEARCH PAPER · Applied Sciences · 2021

Solid-State Lithium Battery Cycle Life Prediction Using Machine Learning

Danpeng Cheng, Wuxin Sha, Linna Wang, Shun Tang, Aijun Ma, Yongwei Chen, Huawei Wang, Ping Lou, Songfeng Lu, Yuan‐Cheng Cao

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Solid-State Lithium Battery Cycle Life Prediction Using Machine LearningOriginalCapacity prediction of K-ion batteries: a machine learning based approach for high throughput screening of electrode materials30Metal-related electrocatalysts for Li–CO 2 batteries: an overview of the fundamentals to explore future-oriented strategies39Next-generation energy systems for sustainable smart cities: Roles of transfer learning118Machine Learning approach for Prediction of residual energy in batteries9A review on methods for state of health forecasting of lithium-ion batteries applicable in real-world operational conditions87Machine learning approaches to estimate the health state of next-generation energy storage4Analysis and evaluation of machine learning applications in materials design and discovery33Statistical and machine learning-based durability-testing strategies for energy storage26Machine learning promotes the development of all-solid-state batteries35Data-driven prognosis of failure detection and prediction of lithium-ion batteries58State of Health Estimation for Li-Ion Batteries Using Machine Learning Algorithms0Model-based battery thermal parameter optimization using symbolic regression7Prediction of Battery Remaining Useful Life Using Machine Learning Algorithms48Artificial intelligence for the understanding of electrolyte chemistry and electrode interface in lithium battery20A Novel Data‐Driven Approach to Lithium‐ion Battery Dynamic Charge State Capture for New Energy Electric Vehicles3Progress in the prognosis of battery degradation and estimation of battery states26A novel method for remaining useful life of solid-state lithium-ion battery based on improved CNN and health indicators derivation73Machine Learning in Lithium-Ion Battery: Applications, Challenges, and Future Trends56Review of recent progress in sintering of solid-state batteries: Application and modelling32Battery Management System Towards Solid-State Batteries9Modeling techniques for electric vehicle powertrains: A comparative review and future insights3Cured Memory RUL Prediction of Solid-State Batteries Combined Progressive-Topologia Fusion Health Indicators26Indirect health state prognosis of lithium-ion batteries based on VMD decomposition and neural network model6Evaluation of Spinel-Type Compounds as Potential Intercalation Hosts for Magnesium Batteries2Remaining useful life prediction for solid-state lithium batteries based on spatial–temporal relations and neuronal ODE-assisted KAN25Utilizing Machine Learning to Predict the Charge Storage Capability of Lithium-Ion Battery Materials0Artificial Intelligence Empowers Solid-State Batteries for Material Screening and Performance Evaluation48Unlocking low temperature-resistant lithium metal batteries: Mechanisms, challenges, AI and functional electrolytes design12LiCondAI: A Machine Learning Model for Predicting Lithium-Ion Conductivity in Solid Electrolytes5Advancements in battery state prediction methods: A comprehensive review of data-driven approaches and degradation mechanisms8Evaluation of life-cycle state of health for solid-state lithium batteries using a hybrid LSTM model2A 0.2 V Window Feature-Driven Cascaded Machine Learning Pipeline for Joint State of Health and Remaining Useful Life Estimation of Lithium-Ion Batteries2Pre-sodiation strategies for high reversibility of hard carbon anodes: A review6Interpretable Machine Learning for Solid-State Batteries8Artificial intelligence catalyzed full-chain innovation and transformation in sodium-ion batteries materials, manufacturing, operation and recycling2Multi-Agent Data Cleaning Framework of Solid-State Batteries using Large Language Models1Interfacial problems and optimization strategies at the oxide solid-state electrolyte-electrode interface2Intelligent Computing for Future Layered Cathodes in Sodium‐Ion Batteries0A critical review of state of charge estimation and lifetime prediction models for batteries used in electric vehicles0

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  1. Capacity prediction of K-ion batteries: a machine learning based approach for high throughput screening of electrode materials

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  2. Metal-related electrocatalysts for Li–CO 2 batteries: an overview of the fundamentals to explore future-oriented strategies

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  3. Next-generation energy systems for sustainable smart cities: Roles of transfer learning

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  4. Machine Learning approach for Prediction of residual energy in batteries

    Energy Reports · 2022-10-19

    T. Jayakumar, Natesh M. Gowda, R. Sujatha, Shankar Nayak Bhukya, Gopalakrishnan Padmapriya, S. Radhika, V. Mohanavel, M. Sudhakar, Ravishankar Sathyamurthy

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  5. A review on methods for state of health forecasting of lithium-ion batteries applicable in real-world operational conditions

    Journal of Energy Storage · 2022-12-16

    Friedrich von Bülow, Tobias Meisen

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  6. Machine learning approaches to estimate the health state of next-generation energy storage

    Elsevier eBooks · 2023-01-01

    Hyeong Suk Na, Abu Md Numan-Al-Mobin

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  7. Analysis and evaluation of machine learning applications in materials design and discovery

    Materials Today Communications · 2023-01-25

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  8. Statistical and machine learning-based durability-testing strategies for energy storage

    Joule · 2023-04-17

    Stephen J. Harris, Marcus M. Noack

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  9. Machine learning promotes the development of all-solid-state batteries

    Chinese Journal of Structural Chemistry · 2023-06-07

    Yong Long Qiu, Xu Zhang, Yun Tian, Zhen Ping Zhou

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  10. Data-driven prognosis of failure detection and prediction of lithium-ion batteries

    Journal of Energy Storage · 2023-06-18

    Hamed Sadegh Kouhestani, Lin Liu, Ruimin Wang, Abhijit Chandra

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  11. State of Health Estimation for Li-Ion Batteries Using Machine Learning Algorithms

    The Eurasia Proceedings of Science Technology Engineering and Mathematics · 2023-08-30

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  12. Model-based battery thermal parameter optimization using symbolic regression

    Journal of Energy Storage · 2023-10-17

    Rafael I. De la Sotta, P. A. Estévez, Jorge R. Vergara, Williams R. Calderón-Muñoz

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  13. Prediction of Battery Remaining Useful Life Using Machine Learning Algorithms

    Sustainability · 2023-10-25

    J. N. Chandra Sekhar, Bullarao Domathoti, Ernesto D.R. Santibañez González

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  14. Artificial intelligence for the understanding of electrolyte chemistry and electrode interface in lithium battery

    National Science Open · 2023-12-01

    Yawei Chen, Yue Liu, Zixu He, Liang Xu, Peiping Yu, Qintao Sun, Wanxia Li, Yulin Jie, Ruiguo Cao, Tao Cheng, Shuhong Jiao

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  15. A Novel Data‐Driven Approach to Lithium‐ion Battery Dynamic Charge State Capture for New Energy Electric Vehicles

    Advanced Theory and Simulations · 2024-01-10

    Zheng Li, Hao Huang, Ruxiang Liu, Jianlin Man, Yusong Shi, Huiping Du, Li Du

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  16. Progress in the prognosis of battery degradation and estimation of battery states

    Science China Materials · 2024-02-19

    Jun Hong Yuan, Zhili Qin, Haikun Huang, Xingdong Gan, Ziwei Wang, Yichen Yang, Shujiang Liu, An Wen, Chuang Bi, Baihai Li, Chenghua Sun

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  17. A novel method for remaining useful life of solid-state lithium-ion battery based on improved CNN and health indicators derivation

    Mechanical Systems and Signal Processing · 2024-07-01

    Yan Ma, Zhenxi Wang, Jinwu Gao, Hong Chen

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  18. Machine Learning in Lithium-Ion Battery: Applications, Challenges, and Future Trends

    SN Computer Science · 2024-07-22

    Alireza Valizadeh, Mohammad Hossein Amirhosseini

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  19. Review of recent progress in sintering of solid-state batteries: Application and modelling

    Journal of Energy Storage · 2024-09-26

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  20. Battery Management System Towards Solid-State Batteries

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  21. Modeling techniques for electric vehicle powertrains: A comparative review and future insights

    AIMS energy · 2025-01-01

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  22. Cured Memory RUL Prediction of Solid-State Batteries Combined Progressive-Topologia Fusion Health Indicators

    IEEE Transactions on Industrial Informatics · 2025-02-20

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  23. Indirect health state prognosis of lithium-ion batteries based on VMD decomposition and neural network model

    International Journal of Production Research · 2025-02-26

    Qinming Liu, Fengze Yun, Ming Dong, Yujie Wang

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  24. Evaluation of Spinel-Type Compounds as Potential Intercalation Hosts for Magnesium Batteries

    The Journal of Physical Chemistry C · 2025-03-10

    Rachita Panigrahi, Bhabani S. Mallik

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  25. Remaining useful life prediction for solid-state lithium batteries based on spatial–temporal relations and neuronal ODE-assisted KAN

    Reliability Engineering & System Safety · 2025-03-11

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  26. Utilizing Machine Learning to Predict the Charge Storage Capability of Lithium-Ion Battery Materials

    Eurasian Chemico-Technological Journal · 2025-04-10

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  27. Artificial Intelligence Empowers Solid-State Batteries for Material Screening and Performance Evaluation

    Nano-Micro Letters · 2025-06-06

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  28. Unlocking low temperature-resistant lithium metal batteries: Mechanisms, challenges, AI and functional electrolytes design

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  29. LiCondAI: A Machine Learning Model for Predicting Lithium-Ion Conductivity in Solid Electrolytes

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  30. Advancements in battery state prediction methods: A comprehensive review of data-driven approaches and degradation mechanisms

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  32. A 0.2 V Window Feature-Driven Cascaded Machine Learning Pipeline for Joint State of Health and Remaining Useful Life Estimation of Lithium-Ion Batteries

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  33. Pre-sodiation strategies for high reversibility of hard carbon anodes: A review

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  34. Interpretable Machine Learning for Solid-State Batteries

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  35. Artificial intelligence catalyzed full-chain innovation and transformation in sodium-ion batteries materials, manufacturing, operation and recycling

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  36. Multi-Agent Data Cleaning Framework of Solid-State Batteries using Large Language Models

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  37. Interfacial problems and optimization strategies at the oxide solid-state electrolyte-electrode interface

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  38. Intelligent Computing for Future Layered Cathodes in Sodium‐Ion Batteries

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  39. A critical review of state of charge estimation and lifetime prediction models for batteries used in electric vehicles

    Results in Engineering · 2026-07-22

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