Machine Learning in Lithium‐Ion Battery Cell Production: A Comprehensive Mapping Study

With 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. AI in battery research: Due to the high complexity of the lithium‐ion battery cell production chain and advancements in digitalization and information technology, machine learning (ML) approaches have gained attention in battery research over recent years. Based on a comprehensive mapping study, this article provides an overview of the different ML applications in battery cell production, outlining the current capabilities and the research perspectives.