## 1 Introduction  
Electric vehicles (EVs) have experienced rapid development in recent decades, driven by economic and environmental concerns surrounding internal combustion engine vehicles. Among the solutions developed, battery electric vehicles (BEVs) have become the most widely adopted, although considerable research has also been dedicated to Fuel Cell Electric Vehicles (FCEVs). Although BEVs appear to be the most cost-effective solution to date, there is significant development around FCEVs, predicting a reduction in hydrogen production costs, particularly for green hydrogen obtained from renewable energy sources. FCEVs offer several advantages compared to BEVs, such as faster refueling times, extended range, and greater suitability for long-range and heavy-duty applications. The main design of an FCEV includes a fuel cell system and a battery pack; however, this configuration faces a well-known drawback: its efficiency remains significantly limited [1]. The efficiency and performance of Fuel Cell Electric Vehicles (FCEVs) largely depend on the implemented Energy Management System (EMS), which optimizes power distribution between the fuel cell and the battery according to various operational criteria. Over the past decade, numerous EMS strategies have been explored in the literature, including rule-based, optimization-based, and learning-based approaches. Selecting the most appropriate EMS is complex; the optimal choice depends on factors such as powertrain configuration, energy storage system (ESS) sizing, driving cycle, and the operational variables that the control system seeks to optimize. Several studies have compared different EMS approaches for FCEVs based on distinct criteria. For instance, in [2], a comparative analysis of energy and exergy efficiency was conducted between fuzzy logic and power-following control strategies across various driving cycles, revealing that fuzzy logic is more efficient for both energy and exergy. In [3], researchers evaluated trainable integrated prediction EMS (TIP-EMS) against predictive strategies over 27 driving cycles, finding that TIP-EMS achieved superior energy conservation and efficiency. Study [4] compared real-time EMS strategies, specifically an Adaptive Equivalent Consumption Minimization Strategy (A-ECMS) and a Stochastic Dynamic Programming (SDP) approach, under random driving cycles, assessing robustness. The results indicated that A-ECMS performs poorly when responding to unknown demand profiles. A complete evaluation of six EMS under realistic driving cycles is performed in [5], but it focuses on the EMS comparison using a simplified vehicle model and only considers fuel cell degradation. Despite these comparative studies, the authors of this work found no comprehensive methodology for selecting the most suitable EMS tailored to specific powertrain configurations, ESS sizing, and driving cycles. Additionally, although some technical studies consider fuel cell efficiency and aging [6, 7, 8], few address both fuel cell and battery aging together. This paper presents a novel methodology for selecting an EMS that minimizes both fuel cell and battery aging while reducing hydrogen and energy consumption in a heavy-duty FCEV. A key contribution of this work is the development of a simulation model in AVL Cruise M\({}^{\text{TM}}\) (hereafter abbreviated as AVL), which incorporates the specific powertrain architecture and components of a particular FCEV. This model is validated using a detailed low-abstraction FCEV model in MATLAB Simulink ([[https://es.mathworks.com/help/sps/semiconductors-and-converters.html](https://es.mathworks.com/help/sps/semiconductors-and-converters.html)]([https://es.mathworks.com/help/sps/semiconductors-and-converters.html](https://es.mathworks.com/help/sps/semiconductors-and-converters.html)) (accessed on 9 January 2025)), featuring commutation-level modeling of the powertrain's power converters.