Machine Learning-based Heat Generation Rate Estimation and Diagnosis for Lithium-ion Batteries

Heat generation rate is a significant safety indicator for lithium-ion battery thermal management which need to be monitored in real time. A distributed fiber optic sensor embedded smart battery configuration is proposed in this paper to acquire the multi-point temperature measurements inside and outside the battery. Hence, a machine learning-based heat generation rate estimation and diagnosis method for Lithium-ion batteries is proposed in this paper to estimate the heat generation rate leveraging the multi-point temperature measurements and detect the abnormal heat generation in real time. The proposed heat generation rate estimation method and smart configuration are experimentally validated to be effective and accurate, and the proposed abnormal heat generation diagnosis method is verified by simulation.