Affiliation:
1. Institute for Machine Tools and Industrial Management Technical University of Munich Boltzmannstr. 15 Garching 85748 Germany
2. Warwick Manufacturing Group University of Warwick CV4 7AL Coventry UK
3. The Faraday Institution Quad One Harwell Science and Innovation Campus Didcot UK
Abstract
AbstractWith the global quest for improved sustainability, partially realized through the electrification of the transport and energy sectors, battery cell production has gained ever‐increasing attention. An in‐depth understanding of battery production processes and their interdependence is crucial for accelerating the commercialization of material developments, for example, at the volume predicted to underpin future electric vehicle production. Over the last five years, machine learning approaches have shown significant promise in understanding and optimizing the battery production processes. Based on a systematic mapping study, this comprehensive review details the state‐of‐the‐art applications of machine learning within the domain of lithium‐ion battery cell production and highlights the fundamental aspects, such as product and process parameters and adopted algorithms. The compiled findings derived from multi‐perspective comparisons demonstrate the current capabilities and reveal future research opportunities in this field to further accelerate sustainable battery production.
Subject
Electrochemistry,Electrical and Electronic Engineering,Energy Engineering and Power Technology
Cited by
7 articles.
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