Following the introduction of Industry 4.0 and the development of information technologies, manufacturing companies have been undergoing a profound transformation. This transformation envisions the realization of the smart factory as a fully connected, flexible production system regulated by data. Digitalization and collection of the critical parameters are the vital prerequisites for this vision. Electrode manufacturing is regarded as the core phase in the battery cell production, having most of the properties determining the electrochemical performance of the battery cell established in this phase. There are a high number of parameters involved in electrode manufacturing. The digitalization of these parameters is associated with a considerable amount of effort and costs. Introducing a tailored digitalization concept provides the first step toward smart battery cell production. The tailored digitalization concept is based on the importance of the parameters from the quality management perspective and their complexity with regard to digitalization. The prioritization of parameters enables a successive quality‐oriented digitalization strategy. The concept is built on a two‐step literature‐based and expert‐based approach. The results include a comprehensive list of parameters and their prioritization for digitalization and integration in a tracking and tracing concept.

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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.

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