Combing physics-based thermal model and machine learning for battery temperature estimation: The impact of model accuracy

Temperature significantly impacts the safety, performance, and degradation of lithium-ion batteries (LIBs), and therefore should be monitored properly by the battery management system (BMS). Hybrid estimation methods by combining physics-based thermal models and machine learning (ML) algorithms, become very promising for sensorless temperature estimation given the limited number of onboard temperature sensors. In this hybrid estimation framework, the physics-based thermal model provides prior knowledge for the ML algorithm to help achieve an accurate final estimation. Therefore, the impact of model accuracy on the overall estimation performance needs to be investigated comprehensively. To this end, this paper investigated the performance of the hybrid estimation framework under different model accuracies, which stem from parameter uncertainties and unmodeled dynamics. Results suggest that the hybrid estimation model can still achieve high accuracy even though trained with inaccurate prior knowledge, demonstrating its robustness to different uncertainties.