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

Mahshid N. Amiri, Anne Håkansson, Odne S. Burheim and Jacob J. Lamb

Renewable and Sustainable Energy Reviews, 2024, vol. 200, issue C

Abstract: Digitalization of lithium-ion batteries can significantly advance the performance improvement of lithium-ion batteries by enabling smarter controlling strategies during operation and reducing risk and expenses in the design and development phase. Accurate physics-based models play a crucial role in the digitalization of lithium-ion batteries by providing an in-depth understanding of the system. Unfortunately, the high accuracy comes at the cost of increased computational cost preventing the employment of these models in real-time applications and for parametric design. Machine learning models have emerged as powerful tools that are increasingly being used in lithium-ion battery studies. Hybrid models can be developed by integrating physics-based models and machine learning algorithms providing high accuracy as well as computational efficiency. Therefore, this paper presents a comprehensive review of the current trends in integration of physics-based models and machine learning algorithms to accelerate the digitalization of lithium-ion batteries. Firstly, the current direction in explicit modeling methods and machine learning algorithms used in battery research are reviewed. Then a thorough investigation of contemporary hybrid models is presented addressing both battery design and development as well as real-time monitoring and control. The objective of this work is to provide details of hybrid methods including the various applications, type of employed models and machine learning algorithms, the architecture of hybrid models, and the outcome of the proposed models. The challenges and research gaps are discussed aiming to provide inspiration for future works in this field.

Keywords: Lithium-ion battery; Digitalization; Digital twin; Physics-based models; Electrochemical models; Machine learning; Hybrid models; Computational cost; Battery design; Battery control (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (2)

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DOI: 10.1016/j.rser.2024.114577

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