Thermal runaway of the battery pack is a main safety accident of lithium-ion batteries. To improve the safety of lithium-ion batteries, it is of great significance to explore the features and development mechanism of thermal runaway. However, the previous studies mainly focus on a single triggering condition of thermal runaway, and fail to achieve a high modeling accuracy. Hence, this paper probes deep into the features and spread mechanism of thermal runaway for electric car batteries. Firstly, the thermo-physical (TP) parameters were acquired from the batteries, and several key parameters were identified, including the heat transfer parameters in the chamber of accelerating rate calorimeter (ARC), and the thermal parameters of battery debris. Next, the thermal runaway features of the batteries were discussed in stages, and the internal heat yield of batteries was calculated for each stage. After that, the thermal runaway spread was modeled, and a discussion was held on the influence of post-thermal runaway TP parameter changes over the spread features of thermal runaway. The proposed model was proved effective and accurate through experiments.

The research on the vehicle thermal management (VTM) system is very important for ensuring the driving reliability of electric cars, however, currently there’re few research concerned about this topic, and the existing ones mostly focus on matching and optimizing parameters to improve the management of driving kinetic energy, and the heat dissipation and cooling performance of the cars; however, there isn’t a uniform standard for evaluating these performances, and the research on closed thermal energy management and control based on the evaluation results is pending. This paper studied the simulation and multi-objective optimization of the VTM system of electric cars, and proposed accurate methods and ideas for evaluating the heat dissipation efficiency of the engine cooling system, the cooling efficiency of the air conditioning system, and the thermal management performance of the VTM of electric cars. Based on the model predictive control (MPC) algorithm of vehicle motion control, this paper constructed temperature control optimization objective functions for electric cars under various thermal adaptation working conditions such as low-speed slope climbing, medium-speed gentle slope climbing, high-speed driving, and idling; and it designed several strategies for the coordinated control of the VTM system of electric cars. At last, this paper used test results to verify the effectiveness of the proposed strategies.