In order to address the issue of suppressing thermal runaway (TR) in power battery, a thermal generation model for power batteries was established and then modified based on experimental data. On the basis of simulation calculations, a scheme was designed to suppress thermal runaway of the battery module and battery pack, and samples were produced for testing. The results of the test and simulation calculations were very consistent, confirming the accuracy of the simulation calculation model. The results of thermal runaway test also demonstrate that the measures designed to suppress thermal runaway are effective and meet the design requirements.

Electric drive vehicles offer a hopeful solution for significant decreases in greenhouse gas emissions and reliance on fossil fuels. Exploring efficient battery preheating techniques is essential due to the fact that harshly cold climates substantially decrease the discharge efficiency of electric vehicles, frequently necessitating lengthy warming periods for the battery upon startup. To this end, this paper reviews currently popular preheating strategies used in passenger electric vehicles. Initially, the postmortem analysis of the cycled anodes charged at low temperatures is reviewed briefly. Following this, detailed descriptions of different external preheating system designs for battery warm-up are presented. Furthermore, the improved charging strategies with the preheating devices such as traditional PTC water heaters and HVCH are elucidated. In addition, the use of electro thermal element preheating in early EVs and prototype vehicles for experimental research is discussed. Keywords: External preheating technique; Current fluctuation; Water heater; High-voltage coolant heater

With the rapid electrification of the automotive industry, the traction battery pack has become the most safety-critical subsystem of an electric vehicle, and any insulation or communication fault must be identified and managed reliably. This work proposes a fuzzy-theory-based diagnosis and control strategy for battery packs that detects and confirms unpredictable faults under diverse operating conditions. The method couples a fuzzy inference system (FIS) with a calibrated expert database: an adaptive fuzzy matching mechanism continuously adjusts the process and observation noise in the state-of-charge and terminal-voltage correction, while triangular membership functions, an IF–THEN rule base, Mamdani inference and centroid defuzzification convert the diagnostic signals into graded fault levels, with rule weights being refined after each confirmed event. Fault confirmation follows the debounce intervals and consecutive-cycle counts defined in the unified threshold tables, and the resulting fault levels activate power-limitation responses. Validation through simulation and vehicle experiments reveals that the judgment time for insulation faults lengthens by 4.6 s (from 3.0 s to 7.6 s), whereas the corresponding accuracy climbs from 81.1% to 92.3%; for communication faults, the accuracy rises from 91.4% to 97.7%, and the interval between fault confirmation and completed power limitation is 16.1 s in simulation, 8.2 s on the test bench, and 24 s under real-world driving. This modest rise in diagnostic time remains within design tolerances, while the achieved accuracy outperforms that of comparable studies and the protective actions taken after confirmation safeguard vehicle occupants and the overall system.

With the rapid increase in electric vehicle (EV) ownership, the strategic planning and layout of charging infrastructure have become essential to encourage EV adoption. This study introduces a comprehensive multi-objective optimization method for selecting locations and designing layouts for high-power and extreme fast charging stations. By thoroughly accounting for user charging demands, economic expenses, and traffic conditions, a multi-objective optimization mathematical model is created aiming to minimize user time and costs while maximizing service capacity and user satisfaction. The model combines queuing theory, network topology analysis, and genetic algorithms to simultaneously handle discrete variables related to station placement, continuous variables for charging pile setup, and complex constraints. Using Panyu District in Guangzhou as a case study, a simulation model with 20,000 electric vehicles and 20 high-power and extreme fast charging stations is developed, focusing on the optimal arrangement of 120 kW, 240 kW, and 480 kW charging piles. The simulation results demonstrate that the optimized charging station layout scheme (13 units of 120 kW, 6 units of 240 kW, and 1 unit of 480 kW) lowers overall costs by 6.74%, reduces user charging waiting time from 1.54 h to 0.65 h, improves user satisfaction by 8.1%, and cuts the peak-to-valley difference in charging load from 900 kW to 450 kW. This work offers both theoretical insights and practical recommendations for the effective planning of electric vehicle charging infrastructure.