## 1 Introduction  
To face economic and environmental challenges, future ground vehicles must be less energy consuming and less polluting. A fuel cell (FC) enables a vehicle to produce zero local emissions. FC-based electric vehicles are hence a viable alternative to internal combustion engine-based vehicles in ground transportation [1]. Among the different FC technologies, the proton exchange membrane fuel cell (PEM-FC) is the most promising solution for vehicle application due to low operating temperatures and fast start-up [2]. However, the PEM-FC has a few drawbacks. The efficiency of the FC decreases at low load. Its cost is relatively high, and its lifetime is restricted. Moreover, the system dynamics are limited. As a result, the FC cannot be used alone in a vehicle [3]. The current commercial FC-based electric vehicles generally use a battery (BAT) as a secondary energy source [4]. For instance, a 100 kW FC stack is used in combination with a 24-kW battery in the Honda ix35 FC vehicle. Nonetheless, a battery has also some drawbacks, such as a limited lifetime and a restricted specific power density [5]. Another possibility would be to replace the battery with super capacitors (SCs), which have a specific power density 100 times higher than battery. Furthermore, SCs allow over half a million charge-discharge cycles, while the current battery technologies do not allow more than some thousands charge-discharge cycles. However, the specific energy density of SCs is weak in comparison with battery. As a consequence, an electric vehicle composed of a FC as primary source and a BAT-SC hybrid energy storage system as a secondary source is of interest to take advantage of each source. However, such a system requires a smart energy management strategy (EMS) to coordinate all sources. The energy management of such a system aims to design an efficient control to manage the three sources in the best way. In the literature, several works have been performed on the energy management of an FC-BAT-SC-based electric vehicle. Two approaches are generally considered: (1) rule-based EMS and (2) optimization-based EMS. The rule-based EMSs are determined by heuristic rules, while the optimization-based EMSs are based on the principle of optimality. In rule-based methods, the EMSs are defined using deterministic rules, such as developed by [PERSON] et al. [6], or fuzzy rules [7; 8]. [PERSON] et al. have, for instance, proposed to use low-pass filters to split the traction power between the three sources by taking into account the dynamics of the traction current. Despite their ease of implementation in real-time, rule-based EMSs have several drawbacks. They are defined for a specific vehicle architecture and particularly depend on the chosen driving profile to tune the rules and thresholds, which leads to time-consuming development. Moreover, optimization cannot be satisfied. The computational burden of adaptive fuzzy logic is furthermore too high to be implemented directly in real time [9]. That is why most of the research are now more focused on offline and online optimization-based EMS. In offline optimization-based EMS, the data of the future driving cycle are known a priori. In this way, an optimal global solution can be obtained to minimize the performance index. In the literature, different kinds of offline optimization-based EMSs have been performed for FC-BAT-SC-based electric vehicles [10; 11; 12; 13]. Nevertheless, as its name suggests, this kind of EMS cannot be implemented in real time. In real-time optimization-based EMS, the objective is to get as close as possible to the optimal solution with a realistic real-time computational burden. Since the future driving cycle is not known, the solution, based on real-time data, can only be suboptimal. For such a kind of EMS, a trade-off between three criteria--implementation, safety, and performance--has to be done. \"Implementation\" is necessary because the EMS has to be able to be implemented in real time.