Lithium-ion batteries experience rapid temperature increases with a high risk of combustion and explosion during thermal runaway, and water mist has been considered as one of the most effective cooling strategies. The water mist field can be impacted by the safety valve airflow, subsequently affecting the cooling characteristics. In this paper, the water mist nozzle with a fixed working pressure is located 1 m above the 100 Ah LiFePO₄ battery to suppress the thermal runaway, and the cooling characteristics under various stages have been compared and analyzed. The results show that the development of thermal runaway can be inhibited before thermal runaway is initiated, and the water mist presents a better cooling effect after the battery safety valve is opened. The critical accumulation heat density of 155 kJ/kg has been identified, which is the threshold for thermal runaway suppression. The confrontation between water mist and the flame has been analyzed, and the water mist droplets cannot fall on the battery surface, resulting in a poor cooling rate of 0.57 kW. This means the suppression effect of water mist will be affected by the airflow impact of the safety valve.

Lithium-ion battery energy storage systems (BESSs) face significant safety challenges arising from internal short-circuit (ISC) faults, which can ultimately trigger thermal runaway. To address this, this paper proposes an ISC fault diagnosis method based on multivariate information entropy (MIE). The proposed approach fuses voltage and temperature time series from battery cells to extract fault features via MIE. Furthermore, a hierarchical diagnosis framework incorporating statistical confidence intervals is developed to enable robust ISC fault diagnosis. Experiments were conducted on 180 Ah lithium iron phosphate batteries, utilizing external resistors to simulate ISC faults of varying severity. The method was further validated using real-world fault data from an electric vehicle accident. Results demonstrate that the proposed method effectively distinguishes between normal and faulty cells, with MIE values exhibiting a monotonic increase as fault severity intensifies. In the real-world dataset, the method identifies the faulty cell 240 s before a discernible voltage drop, demonstrating its capability for early ISC detection.

Three-leg AC-DC Vienna rectifiers are susceptible to single open-switch faults, which make DC-link voltage ripple and make three-leg input AC currents distorted and unbalanced. Thus, this paper presents a quick identification method for single open-switch faults based on three-leg fault currents and output capacitors voltage difference. Fault-leg identification depended on zero-plateaus in the three-leg fault currents, whereas fault-side identification was dependent on reconstruction variables obtained through Clark transformation and phase shifting. In order to improve the reliability of the diagnosis system, the harmonic component of capacitor voltage difference is used to realize the missed diagnosis detection and adjust the time threshold automatically. This method requires no additional hardware and is easy to implement. Experimental results verify the effectiveness of this strategy. It is shown that the fault diagnosis method proposed in this paper has the advantages of fast diagnosis speed, high accuracy and good robustness.