Fine Thermal Control Based on Multilayer Temperature Distribution for Lithium-Ion Batteries

To achieve fine control of multilayer temperature uniformity and energy consumption in a battery thermal management system (BTMS), a model predictive control (MPC) based on the reduced-order model and the heat generation previewer is proposed in this work. A direct contact liquid cooling battery pack is adopted to verify the control strategy. The control-oriented reduced-order model is developed for online multilayer temperature distribution acquisition. A heat generation predictor coupling with a dual neural network is integrated into the MPC controller to provide accurate future disturbances preview. The results indicate that the BTMS can be controlled to the target temperature with less overshoot. Besides, the temperature difference of the cell, module, and pack level can be limited to 0.8 °C, 1 °C, and 2 °C, respectively, decreasing the state of health difference among the cells. For energy consumption, the proposed method improves up to 56.48%.