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

Aug 2, 2026

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Machine learning is gaining strong momentum in lithium-ion battery research due to its ability to improve multiple technical stages of the field. The review discusses key practical applications across battery design, manufacturing, operational service, and end-of-life, while also outlining major obstacles such as limited data, preprocessing and cleaning difficulties, small sample sizes, computational cost, model generalization limits, black-box behavior, scalability for large datasets, data bias, and interdisciplinary integration. Future directions are analyzed, including transfer learning and N-shot learning for small datasets.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages18

Reading Time45 min

AccessFree to download

Abstract

Keywords and Abbreviations

Introduction

Background on Lithium-Ion Battery Research

Overview of Machine Learning and its Relevance in Scientific Research

Machine Learning Applications and Techniques

Challenges and Mitigation Strategies

Future Trends

University of Dundee Machine Learning in Lithium-Ion Battery Valizadeh, Alireza; Amirhosseini, Mohammad Hossein Published in: SN Computer Science DOI: 10.1007/s42979-024-03046-2 Publication date: 2024 Licence: CC BY Document Version Publisher's PDF, also known as Version of record Link to publication in Discovery Research Portal Citation for published version (APA): Valizadeh, A. , & Amirhosseini, M. H. (2024) . Machine Learning in Lithium-Ion Battery: Applications, Challenges, and Future Trends. SN Computer Science, 5, Article 717. [https://doi.org/10.1007/s42979-024-03046-2](https://doi.org/10.1007/s42979-024-03046-2) General rights Copyright and moral rights for the publications made accessible in Discovery Research Portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. Take down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Download date: 11. Oct. 2024 SN Computer Science (2024) 5:717 [https://doi.org/10.1007/s42979-024-03046-2](https://doi.org/10.1007/s42979-024-03046-2) REVIEW ARTICLE Machine Learning in Lithium-Ion Battery: Applications, Challenges, and Future Trends Alireza Valizadeh1 · Mohammad Hossein Amirhosseini2 Received: 3 January 2024 / Accepted: 8 June 2024 © The Author(s) 2024 Abstract Machine Learning has garnered significant attention in lithium-ion battery research for its potential to revolutionize various aspects of the field. This paper explores the practical applications, challenges, and emerging trends of employing Machine Learning in lithium-ion battery research. Delves into specific Machine Learning techniques and their relevance, offering insights into their transformative potential. The applications of Machine Learning in lithium-ion-battery design, manufacturing, service, and end-of-life are discussed. The challenges including data availability, data preprocessing and cleaning challenges, limited sample size, computational complexity, model generalization, black-box nature of Machine Learning models, scalability of the algorithms for large datasets, data bias, and interdisciplinary nature and their mitigations are also discussed. Accordingly, by discussing the future trends, it provides valuable insights for researchers in this field. For example, a future trend is to address the challenge of small datasets by techniques such as Transfer Learning and N-shot Learning. This paper not only contributes to our understanding of Machine Learning applications but also empowers professionals in this field to harness its capabilities effectively. Keywords Lithium-ion battery · Machine learning · Data-driven approach · Artificial intelligence Abbreviations AI CNN EBSD EIS ELM EV GPR HPC LIB LSTM ML Artificial Intelligence Convolutional Neural Network Electron Backscatter Diffraction electrochemical impedance spectroscopy Extreme Learning Machine Electric Vehicle Gaussian Process Regression High-performance computing Lithium Ion Battery Long Short-Term Memory Machine Learning 􀀍 Alireza Valizadeh [1524560@alumni.brunel.ac.uk](1524560@alumni.brunel.ac.uk) 􀀍 Mohammad Hossein Amirhosseini [M.H.Amirhosseini@uel.ac.uk](M.H.Amirhosseini@uel.ac.uk) 1 NEX Power Ltd, 9 Centurion Court, Kiln Farm, Milton Keynes MK11 3JB, UK 2 Department of Computer Science and Digital Technologies, School of Architecture, Computing and Engineering, University of East London, London, UK NASA National Aeronautics and Space Administration NMC LiNi0 . 8Mn0 . 1Co 0.1 O2 PINN Physics-Informed Neural Networks RF Random Forest RL Reinforcement RNN Recurrent Neural Network RUL Remaining Useful Life SOC State of Charge SOH State of Health SVM Support Vector Ma

FAQs

这篇综述主要关注锂离子电池研究中的哪些机器学习应用环节？

涵盖机器学习在锂离子电池设计、制造、运行服务以及退役（end-of-life）等阶段的应用。

综述指出了哪些机器学习在锂离子电池领域面临的关键挑战？

包括数据可用性不足、数据预处理与清洗困难、小样本规模、计算复杂度、模型泛化能力有限、模型黑箱性、算法对大数据集的扩展性不足以及数据偏差等。

未来趋势部分如何应对“小数据集”问题？

通过迁移学习（Transfer Learning）以及 N-shot learning 等方法来缓解小数据集带来的建模困难。

Aug 2, 2026

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