To address the problems of excessive temperature rise, non-uniform temperature distribution and performance degradation of power batteries under high-temperature, low-temperature and alternating cold-hot operating conditions, the paper elaborates on the heat generation mechanism and thermal characteristics of batteries, analyzes the performance requirements under different operating conditions, and systematically reviews the principles of high-temperature heat dissipation technologies, including air cooling, phase change material cooling, indirect liquid cooling, immersion liquid cooling, and heat pipe cooling. The research progress on external and internal low-temperature preheating technologies as well as combined cooling-heating thermal management schemes is also summarized. On this basis, combined with existing research advances, the future development directions and application prospects of high-temperature cooling, low-temperature preheating and hybrid thermal management technologies are discussed.

Solid-state batteries combine high safety with high energy density and hold broad application prospects in the field of electric vehicle power batteries; however, issues such as low ionic conductivity, high interfacial impedance, and lithium dendrite growth in solid-state electrolytes limit their practical application. Based on the application of solid-state electrolyte technology in power batteries, the paper systematically reviews the classification, performance characteristics and limitations of solid-state electrolyte materials. Focusing on dendrite suppression strategies, it analyzes research progress in areas such as the preparation of solid-state electrolyte films, the construction of organic frameworks, the design of novel electrolytes, and interface engineering. It also summarizes the application of process techniques—including particle size distribution control, coating modification, vacuum sintering, and high-energy ball milling—in optimizing ionic conductivity. Finally, the paper looks into the future for development of solid-state electrolyte technology.

To address the issue of lithium-ion power batteries experiencing drastic performance degradation in low-temperature environments, a composite thermal management strategy is proposed that combines high-frequency pulse self-heating with external film heating, supplemented by "constant temperature control". Internal heating uses high-frequency (3 kHz), high discharge rate (2C) pulse currents to rapidly raise the core temperature of the battery, while external distributed heating films (10 W) provide auxiliary heating. Tests are conducted in low-temperature environments of -10 ℃, -15 ℃, and-20 ℃. The results show that this composite heating strategy can significantly increase the heating rate and ensure good temperature uniformity within the battery pack. Compared to the control group without constant temperature measures, this system can maintain the battery operating temperature within an efficient range (22~25 ℃), increase the discharge voltage by about 0.9 V, and boost total discharge energy by 2.5%.

A real-time simulation platform for battery pack in the loop testing is proposed to address the issues of narrow coverage of existing battery pack testing conditions and insufficient performance verification capabilities. Firstly, the overall architecture of the testing system is proposed, and the vehicle dynamics model, strategy model and battery thermal management model are established. A real-time simulation platform for the battery pack in the environment is built, and functional testing and summer and winter working condition tests are conducted. Under the summer 100 km/h and winter 80 km/h operating conditions, the errors of vehicle speed, battery temperature and SOC between the simulation results and real vehicle data are all less than 5%. The battery-in-the-loop real-time simulation test platform can effectively simulate various real road conditions with good real-time performance, thereby significantly reducing the time required for actual vehicle testing.

To address the limited generalizability of electric-vehicle fault warning caused by large differences among vehicle models, restricted access to on-board private data and insufficient extraction of temporal features, the paper proposes a hybrid deep-learning fault-warning model based on grid-side charging-process data. First, model-independent voltage, current and other time-series data readily collected at charging piles are processed by the Transformer self-attention mechanism to capture global temporal dependencies and long-range correlations among key components such as batteries and motors. Scecond, a Bidirectional Long Short-Term Memory (BiLSTM) network models local temporal features in both directions to enhance the contextual representation of fault signatures. Finally, a Support Vector Machine (SVM) classifies the fused features to warn of fault levels during charging. Visualization of attention weights confirms that the model focuses effectively on fault-sensitive features such as voltage anomalies and abrupt temperature-gradient changes. The results show that the method enables unobtrusive "inspection while charging" fault warning using the charging process.

To address the issues that the insulation monitoring circuit of the On-Board Charger (OBC) cannot perform insulation monitoring before inverter activation and is prone to false alarms caused by capacitive loads, and an insulation monitoring circuit is developed by introducing a DC voltage component via a current mirror to monitor the insulation at the OBC inverter output. The parameter selection and design of critical electronic components in the circuit are analyzed. Simulation and experimental verifications are carried out by building simulation circuits and prototypes. The results show that the proposed scheme can complete self-check before OBC inverter output and effectively avoid false insulation monitoring alarms caused by capacitive loads.