Xing Shu

Accurate voltage degradation prediction is essential for health management and lifetime extension of proton exchange membrane fuel cell (PEMFC) systems. During long-term constant-current operation, stack voltage evolves nonlinearly and is influenced by coupled variations in temperature, pressure, flow rate, and humidity, while many deep learning-based models lack physical interpretability. This study proposes an interpretable Transformer-based framework for PEMFC voltage degradation prediction under constant-current operation. The framework integrates outlier correction, interpolation, Savitzky–Golay filtering, Z-score normalization, sliding-window reconstruction, Transformer-based prediction, and feature-ablation interpretation. Using multivariate sensor measurements and historical voltage as inputs, the Transformer was compared with RNN, LSTM, and GRU baselines under identical preprocessing and evaluation conditions. The models were evaluated chronologically by continuously applying the sliding-window model over the held-out final 20% of the aging sequence. The Transformer achieved the best performance, with MAE of 8.2 × 10⁻⁴, RMSE of 1.18 × 10⁻³, MAPE of 0.0256%, and R² of 0.9961. Compared with the second-best RNN model, it reduced MAE, RMSE, and MAPE by 9.89%, 7.81%, and 9.86%, respectively. Feature ablation showed that flow- and pressure-related variables contributed 50.08% and 26.78% of the total importance, respectively.

Reliable operation of proton exchange membrane fuel cells (PEMFCs) is crucial for their widespread commercialization, and accurate fault diagnosis is the key to ensuring their long-term stable operation. However, traditional fault diagnosis methods not only lack sufficient interpretability, making it difficult for users to trust their diagnostic decisions, but also one-dimensional (1D) feature extraction methods highly rely on manual experience to design and extract features, which are easily affected by noise. This paper proposes a new interpretable fault diagnosis algorithm that integrates Gramian angular field (GAF) transform, convolutional neural network (CNN), and gradient-weighted class activation mapping (Grad-CAM) for enhanced fault diagnosis and analysis of proton exchange membrane fuel cells. The algorithm is systematically validated using experimental data to classify three critical health states: normal operation, membrane drying, and hydrogen leakage. The method first converts the 1D sensor signal into a two-dimensional GAF image to capture the temporal dependency and converts the diagnostic problem into an image recognition task. Then, the customized CNN architecture extracts hierarchical spatiotemporal features for fault classification, while Grad-CAM provides visual explanations by highlighting the most influential regions in the input signal. The results show that the diagnostic accuracy of the proposed model reaches 99.8%, which is 4.18%, 9.43% and 2.46% higher than other baseline models (SVM, LSTM, and CNN), respectively. Furthermore, the explainability analysis using Grad-CAM effectively mitigates the “black box” problem by generating visual heatmaps that pinpoint the key feature regions the model relies on to distinguish different health states. This validates the model’s decision-making rationality and significantly enhances the transparency and trustworthiness of the diagnostic process.

An ultra-high-speed electric air compressor (UHSEAC) generates a load torque component during operation, leading to unstable output. As air passes through the UHSEAC, the air density changes significantly, resulting in increased uneven distribution. This causes rapid changes in gas parameters, making them difficult to extract. Consequently, the behavior of load torque component variations under ultra-high-speed operating conditions (UHSOC) remains unclear. The influence of UHSOC on the load torque component has not been studied. In this article, the fluid model of the UHSEAC is established and verified, and the load torque component is extracted based on this model. The influence of speed and torque on the load torque component under UHSOC is then studied. The results show that the frequency of the load torque component increases by 14.29% as the speed increases from 70,000 rpm to 90,000 rpm. Additionally, as the torque increases from 1.3 Nm to 1.7 Nm, the amplitude of the load torque component increases by 69.56%. These findings provide valuable guidance for future scholars seeking to optimize UHSEAC design to reduce load torque components.

In the vehicle-to-everything scenario, the fuel cell bus can accurately obtain the surrounding traffic information, and quickly optimize the energy management problem while controlling its own safe and efficient driving. This paper proposes an energy management strategy (EMS) that considers speed control based on deep reinforcement learning (DRL) in complex traffic scenarios. Using SUMO simulation software (Version 1.15.0), a two-lane urban expressway is designed as a traffic scenario, and a hydrogen fuel cell bus speed control and energy management system is designed through the soft actor–critic (SAC) algorithm to effectively reduce the equivalent hydrogen consumption and fuel cell output power fluctuation while ensuring the safe, efficient and smooth driving of the vehicle. Compared with the SUMO–IDM car-following model, the average speed of vehicles is kept the same, and the average acceleration and acceleration change value decrease by 10.22% and 11.57% respectively. Compared with deep deterministic policy gradient (DDPG), the average speed is increased by 1.18%, and the average acceleration and acceleration change value are decreased by 4.82% and 5.31% respectively. In terms of energy management, the hydrogen consumption of SAC–OPT-based energy management strategy reaches 95.52% of that of the DP algorithm, and the fluctuation range is reduced by 32.65%. Compared with SAC strategy, the fluctuation amplitude is reduced by 15.29%, which effectively improves the durability of fuel cells.

Hydrogen has the physical and chemical characteristics of being flammable, explosive and prone to leakage, and its safety is the main issue faced by the promotion of hydrogen as an energy source. The most common scene in vehicle application is the longitudinal wind generated by driving, and the original position of hydrogen concentration sensors (HCSs) did not consider the influence of longitudinal wind on the hydrogen leakage trajectory. In this paper, the computational fluid dynamics (CFD) software STAR CCM 2021.1 is used to simulate the hydrogen leakage and diffusion trajectories of fuel cell vehicles (FCVs) at five different leakage locations the longitudinal wind speeds of 0 km/h, 37.18 km/h and 114 km/h, and it is concluded that longitudinal wind prolongs the diffusion time of hydrogen to the headspace and reduces the coverage area of hydrogen in the headspace with a decrease of 81.35%. In order to achieve a good detection effect of fuel cell vehicles within the longitudinal wind scene, based on the simulated hydrogen concentration–time matrix, the scene clustering method based on vector similarity evaluation was used to reduce the leakage scene set by 33%. Then, the layout position of HCSs was optimized according to the proposed multi-scene full coverage response time minimization model, and the response time was reduced from 5 s to 1 s.