###### Abstract
Therefore, employing DP to solve the EMS problem offline is deemed suitable in this context. The specification of the objective function in the DP algorithm is critical for optimization. Hydrogen consumption is widely recognized in the literature as a key performance indicator for FCHV energy management and is often chosen as the objective function. [PERSON] et al. [21] optimized fuel cell sizing using DP with fuel consumption as the objective function. However, the finite lifespan of power components introduces additional constraints. An improved system durability often comes at the cost of a reduced fuel economy. Highlighting the interdependence between these indicators, both fuel economy and system durability are considered as optimization objectives. Thus, balancing fuel economy and durability is essential for effective optimization. [PERSON] et al. [22] addressed this by considering both factors and applying DP to achieve optimal results, which were implemented in real time using a soft-run strategy. [PERSON] et al. [23] developed an EMS for FCHVs using an improved DP method and air supply optimization. By modifying the EMS cost function based on the energy allocation between the fuel cell and optimized air supply conditions, the study achieved minimal hydrogen consumption. Achieving the optimal system design involves determining the appropriate sizes for the fuel cell stack and battery to minimize the total costs. Various methods have been developed based on vehicle performance requirements, offering robust solutions that align with industry standards. [PERSON] et al. [24] proposed a theoretical model describing the relationship between powertrain parameters and vehicle performance. The study concluded the following effective methods for minimizing hydrogen consumption: reducing auxiliary power, recovering braking energy, improving fuel cell efficiency, and lowering battery resistance. [PERSON] et al. (2018) introduced an optimal sizing approach for fuel cell electric vehicles within MATLAB/Simulink's Advanced Vehicle Simulator (ADVISOR), which achieved approximately a 10.5% improvement in gradeability performance as compared to a benchmark fuel-cell-powered vehicle. Focusing on fuel cell/supercapacitor hybrid vehicles, [PERSON] et al. (2018) developed a power-based sizing methodology that was based on static and dynamic driving conditions. This suggests that it is better to adopt a trade-off solution, instead of a solution corresponding to the minimum consumption. The performance of a propulsion system depends on its operation, which is governed by the EMS. The EMS directly influences the optimal sizing of system components, while the component sizes, in turn, affect the EMS performance, creating a coupling effect. To achieve the optimal vehicle performance, it is crucial to integrate the EMS design with component sizing, ensuring efficient power allocation and a balance between fuel economy and durability. [PERSON] et al. (2018) optimized the EMS of a range-extender fuel cell vehicle (FCREx) using PMP, identifying a 30 kWh battery and an 80 kW fuel cell as the most cost-effective configuration. [PERSON] et al. (2018) employed the Equivalent Consumption Minimization Strategy (ECMS) to determine fuel cell and battery control setpoints, demonstrating that sizing based solely on average or peak power demand does not ensure the optimal hydrogen consumption. [PERSON] et al. (2018) simultaneously optimized the component sizing and power management for a fuel cell/battery hybrid bus, showing that the optimal fuel cell size depends on the average and standard deviation of power demand, while the optimal battery size is influenced by the range of regenerative power. [PERSON] et al. (2018) proposed a dual-loop framework combining sequential quadratic programming (SQP) and vehicle performance constraints, improving fuel economy by 14%. [PERSON] et al.