Fast Modeling of Battery Thermal Dynamics Based on Spatio-temporal Adaptation

The thermal effect has a significant impact on the performance and durability of lithium-ion batteries. This paper proposes a systematic approach for fast modeling of the distributed battery thermal process. In the proposed method, a well-recognized time/space (T/S) separation is adopted to decompose the spatio-temporal thermal dynamics. Under the T/S separation, an incremental-learning-based regulator is first employed for the recursive update of spatial basis functions (SBFs), which can represent the most recent spatial complexity. Then, a corresponding temporal model with incremental adaptive characteristics is developed to capture the temporal non-linearity. Under such a fully adaptive spatio-temporal modeling pattern, the desired temperature distribution can be well reconstructed and predicted with higher efficiency and flexibility. According to the Rademacher complexity, the generalization bound of the proposed model is derived to ensure stable modeling performance. Experimental studies indicate that the proposed method can achieve satisfactory modeling performance while its computational efficiency is outstanding compared to peer methods.