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RESEARCH PAPER · Energy Storage · 2019

Predicting battery life with early cyclic data by machine learning

Shan Zhu, Naiqin Zhao, Junwei Sha

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32 direct citing papers in this saved network. Explore the next layer with second-order citations.

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42 direct citing works

Predicting battery life with early cyclic data by machine learningOriginalEquivalent circuit model recognition of electrochemical impedance spectroscopy via machine learning113Machine learning toward advanced energy storage devices and systems199Battery lifetime prediction and performance assessment of different modeling approaches113Machine learning-based assessment of the impact of the manufacturing process on battery electrode heterogeneity64Artificial Intelligence Applied to Battery Research: Hype or Reality?503Data-Driven Predictive Electrochemical Behavior of Lignin-Based Supercapacitors via Machine Learning46Early Quality Classification and Prediction of Battery Cycle Life in Production Using Machine Learning96A Heuristic Approach to Linking Experimental Descriptors with Product Selectivity in Electrochemical CO2 Reduction5State of health estimation for fast-charging lithium-ion battery based on incremental capacity analysis106Data Analytics and Information Technologies for Smart Energy Storage Systems: A State-of-the-Art Review19Introduction of a Battery Performance Characteristic Map Based on the Internal Impedance on Cell and System Level0A review of deep learning approach to predicting the state of health and state of charge of lithium-ion batteries288Physics-based, reduced order degradation model of lithium-ion batteries40Review on Machine Learning Methods for Remaining Useful Lifetime Prediction of Lithium-ion Batteries14Machine learning-assisted materials development and device management in batteries and supercapacitors: performance comparison and challenges66Overview on Theoretical Simulations of Lithium‐Ion Batteries and Their Application to Battery Separators160Design and Development of Battery Management System for Automated Car Using Machine Learning0Data Mining for Early Cycle Life Prediction in Lithium-Ion Battery Production5Machine Learning Assisted Prediction of the Mass Activity of Platinum-Based Anode Catalysts Supported on a Variety of Carbon Nanostructures for Direct Methanol Fuel Cells0Lithium-ion battery digitalization: Combining physics-based models and machine learning101Early battery lifetime prediction based on statistical health features and box-cox transformation17Data-driven approaches for the fast formation of lithium-ion pouch cells using operando gassing analysis4A robust adapted Flexible Parallel Neural Network architecture for early prediction of lithium battery lifespan9Applying the Water Sensitive City Index in a House of Quality Framework to Enhance Urban Water Governance and Sustainability0Battery fault diagnosis methods for electric vehicle lithium-ion batteries: Correlating codes and battery management system26AI to Contribute to the Development of More Efficient and Sustainable Battery Technologies0Materials “Economatics”: Combining Chemical, Financial, Environmental, and Social Factors Using Machine Learning6Machine Learning for Battery Energy Storage System (BESS)0A Machine Learning Approach for Prediction of Faradaic Efficiency in Electrochemical CO2 Reduction on Nitrogen‐Doped Carbon4Robust state of charge prediction for lithium-ion batteries in diverse operating environments via machine learning14Adaptive confidence-calibrated semi-supervised ensemble for early-cycle battery lifetime prediction1A model-agnostic meta-learning framework for few-shot prediction of battery end-of-life intervals0

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  1. Equivalent circuit model recognition of electrochemical impedance spectroscopy via machine learning

    Journal of Electroanalytical Chemistry · 2019-11-09

    Shan Zhu, Xinyang Sun, Xiaoyang Gao, Jianrong Wang, Naiqin Zhao, Junwei Sha

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  2. Machine learning toward advanced energy storage devices and systems

    iScience · 2020-12-13

    Tianhan Gao, Wei D. Lu

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  3. Battery lifetime prediction and performance assessment of different modeling approaches

    iScience · 2021-01-19

    Md Sazzad Hosen, Joris Jaguemont, Joeri Van Mierlo, Maitane Berecibar

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  4. Machine learning-based assessment of the impact of the manufacturing process on battery electrode heterogeneity

    Energy and AI · 2021-05-24

    Marc Duquesnoy, Iker Boyano, Larraitz Ganborena, Pablo Cereijo, Elixabete Ayerbe, Alejandro A. Franco

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  5. Artificial Intelligence Applied to Battery Research: Hype or Reality?

    Chemical Reviews · 2021-09-16

    Teo Lombardo, Marc Duquesnoy, Hassna El-Bouysidy, Fabian Årén, A. Gallo‐Bueno, Peter Bjørn Jørgensen, Arghya Bhowmik, Arnaud Demortière, Elixabete Ayerbe, Francisco Alcaide, Marine Reynaud, Javier Carrasco, Alexis Grimaud, Chao Zhang, Tejs Vegge, Patrik Johansson, Alejandro A. Franco

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  6. Data-Driven Predictive Electrochemical Behavior of Lignin-Based Supercapacitors via Machine Learning

    Energy & Fuels · 2021-12-12

    Swarn Jha, Sourav Bandyopadhyay, Siddhi Mehta, Matthew Yen, Tariq Chagouri, Evan Palmer, Hong Yu Liang

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  7. Early Quality Classification and Prediction of Battery Cycle Life in Production Using Machine Learning

    Journal of Energy Storage · 2022-02-20

    Sandro Stock, Sebastian Pohlmann, Florian J. Günter, Lucas Hille, Jan Hagemeister, Gunther Reinhart

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  8. A Heuristic Approach to Linking Experimental Descriptors with Product Selectivity in Electrochemical CO2 Reduction

    ChemPhysChem · 2022-03-15

    Ganesan Raman

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  9. State of health estimation for fast-charging lithium-ion battery based on incremental capacity analysis

    Journal of Energy Storage · 2022-04-05

    Ruomei Zhou, Rong Zhu, Cheng‐Geng Huang, Weiwen Peng

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  10. Data Analytics and Information Technologies for Smart Energy Storage Systems: A State-of-the-Art Review

    Sustainable Cities and Society · 2022-06-15

    Fuzhan Nasiri, Ryozo Ooka, Fariborz Haghighat, Navid Shirzadi, Mariagrazia Dotoli, Raffaele Carli, Paolo Scarabaggio, Amirmohammad Behzadi, Samira Rahnama, Alireza Afshari, Frédéric Kuznik, Enrico Fabrizio, Ruchi Choudhary, Sasan Sadrizadeh

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  11. Introduction of a Battery Performance Characteristic Map Based on the Internal Impedance on Cell and System Level

    IEEE International Conference on Compatibility, Power Electronics and Power Engineering · 2022-06-29

    Sebastian Pohlmann, Ali Mashayekh, Florian Schwitzgebel, Manuel Kuder, Antje Gieraths, Thomas Weyh

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  12. A review of deep learning approach to predicting the state of health and state of charge of lithium-ion batteries

    Journal of Energy Chemistry · 2022-07-02

    Kai Luo, Xiang Chen, Huiru Zheng, Zhicong Shi

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  13. Physics-based, reduced order degradation model of lithium-ion batteries

    Journal of Power Sources · 2022-08-17

    Aniruddha Jana, A. Surya Mitra, Supratim Das, William C. Chueh, Martin Z. Bazant, R. Edwin Garcı́a

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  14. Review on Machine Learning Methods for Remaining Useful Lifetime Prediction of Lithium-ion Batteries

    International Conference on Green Energy, Computing and Sustainable Technology (GECOST) · 2022-10-26

    Nicholas Su, Filbert H. Juwono, Wei Kitt Wong, Ing Ming Chew

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  15. Machine learning-assisted materials development and device management in batteries and supercapacitors: performance comparison and challenges

    Journal of Materials Chemistry A · 2023-01-01

    Swarn Jha, Matthew Yen, Yazmin Soto Salinas, Evan Palmer, John Anzel Villafuerte, Hong Yu Liang

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  16. Overview on Theoretical Simulations of Lithium‐Ion Batteries and Their Application to Battery Separators

    Advanced Energy Materials · 2023-02-15

    D. Miranda, Renato Gonçalves, Stefan Wuttke, Carlos M. Costa, Senentxu Lanceros-Méndez

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  17. Design and Development of Battery Management System for Automated Car Using Machine Learning

    Unknown journal · 2023-11-02

    Sneh Lata, R Ambika, K N Dharani, Gunji Venkata Naga Harshitha, S R HaemaPrabhaa

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  18. Data Mining for Early Cycle Life Prediction in Lithium-Ion Battery Production

    Procedia CIRP · 2024-01-01

    Sandro Stock, Mahmoud Ahmed, Fabian Konwitschny, Rüdiger Daub

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  19. Machine Learning Assisted Prediction of the Mass Activity of Platinum-Based Anode Catalysts Supported on a Variety of Carbon Nanostructures for Direct Methanol Fuel Cells

    SSRN Electronic Journal · 2024-01-01

    Ganesan Raman

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  20. Lithium-ion battery digitalization: Combining physics-based models and machine learning

    Renewable and Sustainable Energy Reviews · 2024-05-21

    Mahshid Nejati Amiri, Anne Håkansson, Odne Stokke Burheim, Jacob Joseph Lamb

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  21. Early battery lifetime prediction based on statistical health features and box-cox transformation

    Journal of Energy Storage · 2024-06-25

    Qiqi Wang, Min Xie, Fangfang Yang

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  22. Data-driven approaches for the fast formation of lithium-ion pouch cells using operando gassing analysis

    Journal of Power Sources · 2024-07-09

    Sandro Stock, J. Böhm, Manuel Ank, Philipp Rath, Markus Lienkamp, Rüdiger Daub

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  23. A robust adapted Flexible Parallel Neural Network architecture for early prediction of lithium battery lifespan

    Energy · 2024-08-22

    Lidang Jiang, Zhuoxiang Li, Changyan Hu, Junxiong Chen, Qingsong Huang, Ge He

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  24. Applying the Water Sensitive City Index in a House of Quality Framework to Enhance Urban Water Governance and Sustainability

    Advances in computational intelligence and robotics book series · 2025-02-18

    Ocotlán Díaz-Parra, Jorge A. Ruíz-Vanoye, Eric Simancas-Acevedo, Julio Cesar Ramos-Fernandez, Juan M. Xicoténcatl-Pérez, Francisco Marroquín-Gutiérrez, Julio César Salgado-Ramírez, Yaneth Reyes-Hernández

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  25. Battery fault diagnosis methods for electric vehicle lithium-ion batteries: Correlating codes and battery management system

    Process Safety and Environmental Protection · 2025-02-18

    G. Naresh, T. Praveenkumar, Dinesh Kumar Madheswaran, Edwin Geo Varuvel, Arivalagan Pugazhendhi, Thangamuthu Mohanraj, S. Jenoris Muthiya

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  26. AI to Contribute to the Development of More Efficient and Sustainable Battery Technologies

    Advances in computational intelligence and robotics book series · 2025-03-07

    José Miguel Liceaga-Ortiz-De-La-Peña, Juan M. Xicoténcatl-Pérez, Jorge A. Ruíz-Vanoye, Rubén Oswaldo Hernández-Terrazas, Israel Pérez Martínez, Marco Antonio García-Márquez, Julio Cesar Ramos-Fernandez, Julio César Salgado-Ramírez, Francisco Marroquín Gutiérrez

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  27. Materials “Economatics”: Combining Chemical, Financial, Environmental, and Social Factors Using Machine Learning

    ACS Nano · 2025-03-07

    Benjamin Poswell, Amanda Susan Barnard

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  28. Machine Learning for Battery Energy Storage System (BESS)

    Advances in computational intelligence and robotics book series · 2025-04-02

    S. Elango

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  29. A Machine Learning Approach for Prediction of Faradaic Efficiency in Electrochemical CO2 Reduction on Nitrogen‐Doped Carbon

    ChemPlusChem · 2025-04-15

    Ganesan Raman

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  30. Robust state of charge prediction for lithium-ion batteries in diverse operating environments via machine learning

    Measurement · 2025-04-25

    Basak Gok, Fırat Can Yılmaz, Mustafa Fazıl Serincan, Recep Önler

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  31. Adaptive confidence-calibrated semi-supervised ensemble for early-cycle battery lifetime prediction

    Applied Energy · 2026-02-12

    Jianlong Bi, Zicheng Fei, Jin Wang

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  32. A model-agnostic meta-learning framework for few-shot prediction of battery end-of-life intervals

    Journal of Energy Storage · 2026-06-17

    Kuo Yang, Zixi Liu, Yanjie Cai, Yugui Tang, Xiangdong Kong, Guanqiang Ruan

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