A Review of the Applications of Explainable Machine Learning for Lithium–Ion Batteries: From Production to State and Performance Estimation

Mona Faraji Niri (), Koorosh Aslansefat, Sajedeh Haghi, Mojgan Hashemian, Rüdiger Daub and James Marco
Additional contact information
Mona Faraji Niri: WMG, University of Warwick, Coventry CV4 7AL, UK
Koorosh Aslansefat: School of Computer Science, University of Hull, Hull HU6 7RX, UK
Sajedeh Haghi: Institute for Machine Tools and Industrial Management, Technical University of Munich, Garching, Boltzmannstr. 15, 85748 Munich, Germany
Mojgan Hashemian: Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal
Rüdiger Daub: Institute for Machine Tools and Industrial Management, Technical University of Munich, Garching, Boltzmannstr. 15, 85748 Munich, Germany
James Marco: WMG, University of Warwick, Coventry CV4 7AL, UK

Energies, 2023, vol. 16, issue 17, 1-38

Abstract: Lithium–ion batteries play a crucial role in clean transportation systems including EVs, aircraft, and electric micromobilities. The design of battery cells and their production process are as important as their characterisation, monitoring, and control techniques for improved energy delivery and sustainability of the industry. In recent decades, the data-driven approaches for addressing all mentioned aspects have developed massively with promising outcomes, especially through artificial intelligence and machine learning. This paper addresses the latest developments in explainable machine learning known as XML and its application to lithium–ion batteries. It includes a critical review of the XML in the manufacturing and production phase, and then later, when the battery is in use, for its state estimation and control. The former focuses on the XML for optimising the battery structure, characteristics, and manufacturing processes, while the latter considers the monitoring aspect related to the states of health, charge, and energy. This paper, through a comprehensive review of theoretical aspects of available techniques and discussing various case studies, is an attempt to inform the stack-holders of the area about the state-of-the-art XML methods and encourage those to move from the ML to XML in transition to a NetZero future. This work has also highlighted the research gaps and potential future research directions for the battery community.

Keywords: lithium–ion battery; machine learning; explainability; XML; interpretability; manufacturing processes; state of health; state of charge (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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