###### Abstract  
Fuel economy and system durability are critical yet interdependent performance metrics for fuel cell hybrid vehicles (FCHVs). This paper devises an integrated framework for optimizing component sizing and energy management in a fuel cell/battery hybrid passenger vehicle. A unified cost function is proposed, combining fuel economy and system durability through a weighting coefficient, based on a comprehensive model of the hydrogen consumption and degradation characteristics of fuel cells and batteries. Utilizing the dynamic programming (DP) algorithm, the total cost is optimized to derive the optimal weighting factors and component sizing, effectively addressing the multi-objective optimization problem and balancing efficiency and durability. Furthermore, the impact of power prices on the optimal parameters is carefully examined. The simulation results indicate that a battery capacity of 44 Ah and a fuel cell maximum power of 80 kW represent the optimal sizing configuration. A weighting factor of 0.5 achieves the minimum equivalent total cost by effectively balancing fuel economy and system durability for the light-duty fuel cell passenger vehicle. Additionally, the battery price affects the weighting factor, indicating that future reductions in power source costs will shift focus away from system durability to fuel economy in FCHV optimization. These findings provide recommendations for FCHV manufacturers to advance the application of fuel cells in passenger vehicles. fuel cell hybrid vehicles; component sizing; energy management; dynamic programming; objective weighting 2  
noise [5]. Among fuel cell technologies, proton exchange membrane fuel cells (PEMFCs) are the most advanced, offering a high power density, efficiency, low operating temperature, and rapid startup [6]. The powertrain system of an FCHV typically includes a battery package and a fuel cell stack (FCS) [7]. This configuration extends the operational lifespan of the fuel cell while also meeting dynamic power demands and facilitating regenerative braking energy recovery. Depending on their design, some vehicles operate primarily as battery electric vehicles with a fuel cell system serving as a range extender, while others use an FCS with a stack power output comparable to the electric motor's power level. Effectively coordinating these two energy sources, optimizing energy flow allocation, and minimizing system costs have become critical challenges that need to be addressed. In hybrid systems, an energy management strategy (EMS) is developed to achieve optimal power allocation. EMSs are generally classified into the following three categories: rule-based, optimization-based, and learning-based approaches [8]. Rule-based EMSs typically rely on engineering expertise, offering a straightforward structure, strong real-time performance, and notable robustness [9]. Common examples include the fuzzy logic strategy [10], the wavelet transform strategy [11], and the charge-depletion-charge-sustain (CDCS) strategies [12]. However, these designs largely depend on engineering experience and lack rigorous mathematical validation, which may limit their performance in complex hybrid systems [13]. Meanwhile, learning-based approaches, such as deep reinforcement learning, have been employed to develop EMSs for FCHVs [14]. Some methods have integrated environmental and look-ahead road information [15]. Nevertheless, these methods require extensive driving data and substantial onboard computing power to adapt to various driving conditions [16]. Optimization-based EMSs provide improved vehicle optimization, as they guarantee optimality. Commonly used methods include Pontryagin's minimum principle (PMP) [17], convex programming [18], and dynamic programming (DP) [19]. The DP algorithm is a widely employed optimization method. However, its high computational complexity restricts its direct application in real-time control. During the phase of vehicle design, specifically in component sizing, the driving cycle is typically assumed to be fully known [20].