General Data Physics Coupling Pipeline for Next-generation Battery Modeling and Monitoring to Enhance Human Health and Sustainability

There is a pressing demand for the advancement of battery management systems in electronic devices, which play a crucial role in ensuring both the safety of battery systems and human well-being in modern society. This project is committed to establishing an extensive data physics coupling pipeline for the modeling and monitoring of batteries in next-generation battery management. It is planned to be investigated based on collaborative efforts between the Massachusetts Institute of Technology (MIT) and Aalborg University (AAU), involving thorough multi-disciplinary research. The primary purpose is to enhance the accuracy, reliability, generalization, physics interpretability, and integration of battery management algorithms. The ultimate purpose is to leverage the developed technology to contribute to the reduction of exhaust emissions, enhance financial gains, elevate customer satisfaction, and mitigate safety risks. These efforts collectively contribute to the enhancement of human health and sustainability. The project integrates the entire process of next-generation battery management, encompassing development, application, and evaluation. It commences by devising a novel and general pipeline, featuring a residual learner and a physics-constrained domain adaptive learner, to enhance battery modeling and monitoring. The pipeline is then applied to battery modeling, state estimation, health prognostics, and lifetime prediction, incorporating model adjustment and optimization to improve accuracy and generalization. Subsequently, the project evaluates the benefits of the developed pipeline, focusing on the exhaust emissions reduction and financial gains increment. Additionally, the enhancement of human health and environmental sustainability will be assessed. The project duration is 4 years, with the first 36 months dedicated to MIT and the subsequent 12 months to AAU.