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Understanding end-user requirements is pivotal in battery research. Notably, battery experiments conducted in academic laboratories frequently operate under conditions and parameters that diverge substantially from those encountered by EVs20. The pitfalls associated with the creation of real-life experimental conditions can hinder the scaling-up and manufacturing of batteries, as well as impede the seamless transfer of technology to industry. This section delves into some of the bottlenecks inherent in existing methodologies.
The integration of physics and machine learning proves advantageous for battery management due to the essential roles played by both disciplines. Managing batteries poses a real engineering challenge, requiring consideration of multiple factors simultaneously, including accuracy, robustness, computation cost, deployment cost, and more. Consequently, it becomes imperative to leverage available information in an optimized manner to address this multifaceted problem effectively26.
Based on our systematic survey and analyses of the existing challenges on battery health and safety, we identify the following perspectives of research in battery technology through the synergy of physics and machine learning.
The modeled physics, while not perfect, can be effectively improved with machine learning, addressing the limitations and assumptions inherent in physics-based modeling, such as the P2D model. Machine learning can efficiently utilize measured data to enhance physics-based modeling and mitigate the limitations of PDEs. By leveraging machine learning, the identification of particle number and size, as well as capturing particle dynamics during usage, can be improved for advanced battery management.
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