The thermal runaway hazards pose a serious threat to the application and transport of lithium-ion batteries on the aircraft. Hence, the researches of thermal safety in flight condition are necessary. In this study, the tests were conducted in a dynamic pressure chamber to study the effects of ambient pressure and heating power on the thermal runaway characteristics. The results show that the fierce behaviors of jet fire, deflagration, and explosion only were observed in high ambient pressure with high heating power. The open time of the safety valve is advanced as pressure from 95 kPa to 20 kPa. The parameters of heat release rate (HRR), total heat release (THR), cell surface temperature, peak concentration of CO₂, and mass loss decrease as the descend of external pressure or heating power. The peak values of hydrocarbon (CH_x) and CO increase with the descent of pressure but decrease as the reduction of heating power. The effects of ambient pressure on the thermal runaway (TR) fire behaviors mainly attribute to the low oxygen density. The time of heating and smoking may account for the difference of TR behaviors with various heating power. It is revealed that the fire risk and the hazards of toxic/flammable gas emissions are tightly relative to the TR behaviors. These results provide valuable proposals and inspiration for the safety warning and hazard reduction under low pressure.

Thermal runaway characteristics and hazards of lithium-ion batteries under low ambient pressure in-flight conditions are studied in a dynamic pressure chamber. The influence of ambient pressures (95 kPa and 20 kPa) and packaging forms (cylindrical and pouch commercial batteries) were especially investigated. The results show that the values of heat release, temperature, and $\mathrm{C}{\mathrm{O}}_2$ concentration decrease with the reduction in pressure from 95 kPa to 20 kPa, while the total hydrocarbon and $\mathrm{C}\mathrm{O}$ increase. Without violent fire, explosion, and huge jet flames, the thermal hazards of TR fire under 20 kPa are lower, but the amount of toxic/flammable gas emissions increases greatly. The amount of $\mathrm{C}\mathrm{O}$ and hydrocarbons varies inversely with the thermal hazards of fire. Under low-pressure environments of cruise altitude, the thermal hazards of TR fire for pouch cells and the toxic/potentially explosive hazards of gas emissions of cylindrical cells need more attention. The performance of TR hazards for two packaging types of battery is also different. Pouch cells have higher thermal hazards of fire and lower combustible/toxic emitted gases than cylindrical cells. The thermal runaway intensity of individual cells decreases under lower ambient pressure, but the burning intensity increases dramatically when thermal runaway occurs in a battery pack. The open time of a safety valve (rupture of the bag) is shortened, but the trigger time for a thermal runaway varies for different formats of batteries under 20 kPa. Those results may be helpful for the safety warning and hazard protection design of Li batteries under low-pressure conditions.

Accurate prediction of the State of Health (SOH) of lithium-ion batteries is essential for improving the safety and longevity of energy storage systems. This paper introduces ExpertMixer, a novel model based on a fused expert network for SOH estimation. By combining the strengths of state space models and recurrent neural networks, the model effectively handles the joint optimization of long-sequence dependency modeling and complex dynamic feature extraction. To improve temporal representation, ExpertMixer utilizes sampling time-based rotary position encoding (RoPE). It consists of two expert modules: a Mamba module designed to capture global degradation trends and an LSTM module focused on modeling local dynamic fluctuations. These are adaptively fused through a learnable gating mechanism that supports multi-scale feature integration. Experiments performed on the NASA PCoE dataset show that ExpertMixer achieves optimal performance on the NASA L subset, with an average MAE of 1.047 and RMSE of 1.603. It surpasses the traditional CNN BiGRU model, which had an MAE of 2.286, by 54.2%, and improves upon the advanced SambaMixer model, which had an MAE of 1.072, by 2.3%. Under low-temperature conditions using Battery 47, the model reduces the prediction error for nonlinear degradation to an MAE of 0.539, significantly exceeding all compared methods. Ablation studies verify the effectiveness of the dual-expert structure and fusion mechanism; removing the gating module results in an 18.7% decrease in performance. This research offers a new framework for lithium battery life prediction that demonstrates improved accuracy and generalization capability, suggesting potential practical value for intelligent energy storage management.

Existing deep learning methods for lithium-ion battery State of Health (SOH) prediction rely almost exclusively on discharge voltage–current curves, ignoring electrochemical impedance spectroscopy (EIS) data that directly reflects internal degradation mechanisms. Fusing these two modalities is non-trivial: discharge curves are high-dimensional temporal sequences residing on a continuous dynamical manifold, while impedance features are low-dimensional static snapshots with fundamentally different statistical distributions. However, naive concatenation introduces modal conflicts rather than complementary gains. We propose the Hybrid Sensing Synergy Architecture (HSSA), which combines a Mamba backbone ($O\left(L\right)$ complexity) for discharge curve modeling with a Q-former module that aligns impedance features into the temporal representation space via learnable query tokens and cross-attention. A prepend fusion strategy injects the aligned queries as prefix tokens, enabling the backbone to condition on internal electrochemical context from the first time step. On the NASA battery dataset, HSSA achieves MAE of 0.887 (large-scale, 11 batteries, a 9.8% improvement over unimodal Mamba), 1.457 (medium-scale, five batteries, a 28.0% improvement), and 2.705 (small-scale, four batteries, an 8.7% improvement), demonstrating consistent improvements across all data regimes. On out-of-sample battery B28, HSSA achieves 65.3% improvement. Ablation studies confirm that Q-former alignment is essential and prepend fusion significantly outperforms concatenation-based alternatives.

With the growing adoption of lithium-ion batteries, the risk of battery thermal runaway is increasing, so effective temperature regulation for battery systems is essential. The air cooling system for battery thermal management offers advantages such as a simple structure and low cost, making it a promising solution for electric aircraft, electric vehicles, and other applications. In this work, the influence of inlet air velocity, inlet size, and inlet/outlet area ratio on the maximum temperature and maximum temperature difference in the battery pack was simulatively studied. When the inlet air velocity was 4 m/s, inlet size was 73 mm × 25 mm, and inlet/outlet area ratio was 1.25, the heat dissipation effect of battery pack was excellent, and the maximum temperature was 324.8 K. This research offers a crucial foundation for designing and setting the operational parameters of air cooling thermal management systems in lithium-ion battery packs.