## 1 Introduction  
Fuel consumption reduction and tailpipe emission are the main objectives of the transition to the electrified transportation industry. Fuel cell vehicles (FCVs) have a high superiority in the development of transportation electrification. Pure fuel cell vehicles have main drawbacks, which encourage designers to design FCVs with the support of battery, ultracapictor (UC), photovoltaic, or hybrid configurations. This hybrid configuration incurs additional complexity to the power-train control system, which means the energy storage systems (ESSs) should be optimally controlled. Hence, the online energy management strategy (EMS) in the fuel cell hybrid electric vehicles (FCHEVs) is a critical point in the electrification of the transportation industry [1; 2; 3]. The slow dynamic response of fuel cells (FCs) and its incapability of regenerative braking energy capture are the main drawbacks of FCVs, making them equipped with ESSs in the electrified transportation industry. However, none of the ESSs has high power and energy density at the same time. The batteries have a high energy density, and the UCs have a high power density. As a result, the combination of battery and UC can supply the vehicle's required power in multi-second peaks. In regenerative braking modes, the battery's low power density does not allow it to contribute completely to receiving the energy generated by the brake, but the UCs can capture this energy. Considering the above discussion, the combination of ESSs improves the performance of the FCHEVs [4; 5; 6; 7; 8]. The proton exchange membrane fuel cell (PEMFC) is the most promising FC type for application in the electrified transportation industry. The PEMFC has high efficiency,low operating temperature, high power density, nearly zero-emission, and a relatively fast startup [9; 10]. Among the possible state-of-the-art hybridized architectures of the ESSs, the FC/battery/UC structure is the most attractive configuration [11]. This HESS structure has the lowest hydrogen consumption cost and electricity price compared to the FC/battery and FC/UC structures [12], and a reliable energy management strategy is needed to maximize the mentioned advantages. The energy management strategies encompass rule-based (deterministic, fuzzy logic control) and optimization-based (real-time, global) approaches. The global optimization-based method requires prior knowledge of an entire driving cycle to minimize the cumulative fuel consumption. The real-time optimization-based approaches eliminate the requirement to obtain prior knowledge by reducing global optimization problems into a succession of local optimization problems. But the global optimal point can differ from a locally optimal point. For instance, the equivalent consumption minimization strategy (ECMS) outperforms the other real-time methods, but the optimal point in global methods might differ 10% in comparison with ECMS [13; 14]. The online rule-based strategies have lower implementation costs, lower occupied memory space, and higher determination speed, but engineering experiences affect these strategies' performance. Fuzzy logic control (FLC), wavelet-FLC, adaptive-FL, fuzzy logic on-off power control, state machine control (SMC), operational mode control (OMC), power follower, on-off power control, and modified power follower are based on the rule-based EMS [15]. The fuzzy logic performance is evaluated through the following studies. In [16], a triple fuzzy logic controller based on data fusion has been presented to the online energy management of FCHEV. The genetic algorithm has been used to optimize the membership function parameters of FLC for three driving cycles. Moreover, the probabilistic support vector machine method has been applied to detect various driving conditions. However, driving conditions have only been categorized into three groups. Moreover, the authors have selected the battery and UC states empirically. For investigation multi-level methods, the following features are compared.