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.

High-performance proton exchange membrane fuel cell (PEMFC) vehicles are important for realizing carbon neutrality in transportation. However, the optimal power density of the fuel cell performance is difficult to achieve due to the internal complex operating conditions of a fuel cell stack. Moreover, there is a lack of effective models to solve the coupled multi-physical fields (force, temperature and humidity, etc.) in the PEMFC, particularly considering the gas diffusion layer (GDL) compression. Thus, a force-temperature-humidity coupled modeling method is introduced to evaluate the effects of key operating conditions for the fuel cell performance parameter matching. Firstly, the interfacial contact resistance and GDL porosity are obtained by a force-temperature coupled simulation using a finite element analysis (FEA) modeling, then the obtained results are introduced into a temperature-humidity coupled simulation using a computational fluid dynamics (CFD) modeling. An iteration algorithm is proposed to realize the force-temperature-humidity coupled simulation for the PEMFC performance. The main characteristics of the PEMFC performance parameters are revealed and the optimum matching criteria of the main performance parameters (temperature, stoichiometric ratio and relative humidity) are determined. The presented co-simulation method is significant and effective for realizing the PEMFC performance parameter matching condition, and it provides a design direction for an optimal power density of a fuel cell stack.