Guo Zhechen is an assistant professor at Xi'an Jiaotong University. He is engaged in the research of battery safety management in electric vehicles, energy storage power stations and other facilities. He has carried out key technology research in modeling of battery thermal management systems, structural design optimization, fault diagnosis, intelligent control and other aspects. He has published more than ten papers, and he has led and contributed to numerous projects in this field.

The battery thermal management system is developing in the direction of fine control and safety. To achieve fine control of multilayer temperature uniformity 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. 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 precisely controlled. Besides, the energy consumption of the proposed method can be reduced by half. Detecting and addressing thermal fault in battery systems is crucial for averting potential safety hazards. A novel redundant thermal fault diagnosis and localization method is proposed. This approach combines the indirect electrical and thermal characteristics to accurately identify the faulty cell in a battery string under sparse temperature sensing. The experimental results show that the thermal fault can be rapidly detected upon triggering, and the faulty cell can be located accurately from both the electrical and thermal characteristics under various operating conditions. These technologies provide a reference for the management and control of future BTMS.