Equivalent Consumption Minimization Strategy Research Articles

A predictive control strategy based on A-ECMS to handle Zero-Emission Zones: Performance assessment and testing using an HiL equipped with vehicular connectivity

Recently, several metropolitan cities introduced Zero-Emissions Zones where the use of the Internal Combustion Engine is forbidden to reduce localized pollutants emissions. This is particularly problematic for Plug-in Hybrid Electric Vehicles, which usually work in depleting mode. So, the risk of not having enough energy stored to carry out the driving mission and then paying a fee is substantial. This work presents a viable solution by exploiting vehicular connectivity to retrieve navigation data of the urban event along a selected route. The battery energy needed, in the form of a minimum State of Charge (SoC), is calculated by a Speed Profile Prediction algorithm and a Backward Vehicle Model. That value is then fed to both a Rule-Based Strategy, developed specifically for this application, and an Adaptive Equivalent Consumption Minimization Strategy (A-ECMS). The effectiveness of this approach has been tested with a Connected Hardware-in-the-Loop (C-HiL) on a driving cycle measured on-road, stimulating the predictions with multiple re-routings. The tests have been conducted with different initial SoC values for each strategy, showing a maximum error in the SoC prediction of 2.4% and up to 26.1% of CO2 saving with the A-ECMS.

Driving-Behavior-Aware Optimal Energy Management Strategy for Multi-Source Fuel Cell Hybrid Electric Vehicles Based on Adaptive Soft Deep-Reinforcement Learning

The majority of existing energy management strategies (EMSs), merely considering external driving conditions, often allocate demand power in an irrational way, resulting in a waste of energy and a short service life of power sources. Therefore, it is necessary to integrate driving behavior in EMS to reduce the fuel consumption and improve the lifespan of power sources. In this paper, a driving-behavior-aware adaptive deep-reinforcement-learning (DRL) based EMS is proposed for a three-power-source fuel cell hybrid electric vehicle (FCHEV). To fully utilize each power source, a hierarchical power splitting method is adopted by an adaptive fuzzy filter. Then, a high-performance driving behavior recognizer is employed, and Pontryagin’s minimum principle (PMP) method is used to compute the optimal equivalent factor (EF) of each driving behavior. To realize a trade-off between global learning and real-time implementation, an improved multi-learning-space DRL-based algorithm, applying driving-behavior-aware adaptive equivalent consumption minimization strategy (A-ECMS) and soft learning mechanism, is proposed and verified by a series of simulations. Simulation results show that, compared with the benchmark method ECMS, the proposed P-DQL method can reduce the hydrogen consumption by 49.9% on average, and the total cost to use by 31.4%, showing a promising ability to increase fuel economy and reduce hydrogen consumption and the total cost to use of FCHEV.

A decision-making approach for the health-aware energy management of ship hybrid power plants

Although autonomous shipping has attracted increasing interest, its further development requires innovative solutions to operate autonomous ships without the direct intervention of human operators. This study aims to develop a health-aware energy management (HAEM) approach for ship hybrid power plants, integrating the health monitoring information from reliability tools with the energy management tools. This approach employs the equivalent consumption minimisation strategy (ECMS) along with a Dynamic Bayesian network (DBN), as well as the utopia decision-making method and a model for the ship hybrid power plant. The HAEM approach is demonstrated for a parallel hybrid power plant of a pilot boat considering realistic operating profiles. The results demonstrate that by employing HAEM approach for the investigated ship power plant operating for 300 h reduces its failure rate almost fourfold at the cost of fuel consumption increase of around 1.5%, compared to the respective operation with the ECMS. This study is expected to contribute towards the development of supervisory control of autonomous power plants.

A Multipurpose Simulation Approach for Hybrid Electric Vehicles to Support the European CO2 Emissions Framework

Hybrid Electric Vehicles (HEVs) are a prominent solution for reducing CO2 emissions from transport in Europe. They are equipped with at least two propulsion energy converters, an Internal Combustion Engine (ICE) and one or more Electric Machines (EMs), operated in a way to exploit synergies and achieve fuel efficiency. Because of the variety in configurations and strategies, the use of simulation is essential for vehicle development and characterisation of energy consumption. This paper introduces a novel simulation approach to estimate the CO2 emissions from different hybrid architectures (series, parallel, power-split) and electrification degrees (mild, full, plug-in and range extender) that is relatively simple, flexible and accurate. The approach identifies the optimal power split between the energy converters for any given time in a driving cycle according to three evaluation levels: supervisor, ICE manager and optimiser. The latter relies on the Equivalent Consumption Minimisation Strategy (ECMS) and the limitations imposed by the other two layers. Six light-duty HEVs with different hybrid architectures were tested to support the development of the approach. The results show an indicative accuracy of ±5%, enabling to run assessments of hybrid powertrain solutions and supporting regulatory and consumer information initiatives.

Optimization of battery energy storage system size and power allocation strategy for fuel cell ship

AbstractThe fuel cell system (FCS) is commonly combined with an energy storage system (ESS) for enhancing the performance of the ship. Consequently, the battery ESS size and power allocation strategy are critical for the hybrid energy system. This paper focuses on designing a method to solve these two problems. First, a battery degradation model is employed to assess the ESS lifetime. Subsequently, the sizing problem and the optimal power allocation are integrated into a cost‐minimization problem, which is solved by a double‐loop optimization approach. The inside loop utilizes the battery degradation model to calculate ESS lifetime. In the outside loop, a power allocation strategy based on the hybrid Particle Swarm Optimization algorithm and Gray Wolf Optimization algorithm is presented. Finally, the power allocation strategy is extended to real‐time implementation by the equivalent consumption minimization strategy (ECMS) and an improved ECMS is proposed to make the FCS operates near the maximal efficiency point. Compared with ECMS, the operating cost reduces by 0.26%. The result indicates that the proposed method can optimize the ESS size efficiently, and the power allocation strategy can assure the stable operation of the fuel cell ship.

ECMS-MPC Energy Management Strategy for Plug-In Hybrid Electric Buses Considering Motor Temperature Rise Effect

Due to working in urban conditions, the plug-in hybrid electric bus (PHEB) has a high vehicle load rate during peak hours and when climbing long slopes. Therefore, the motor of PHEB is prone to overload and overheating, which affects the power performance and fuel economy of the PHEB. Thus, designing an energy management strategy (EMS) for PHEB that considers motor temperature is a crucial research topic. In this article, a strategy combining the equivalent consumption minimization strategy (ECMS) with the model predictive control (MPC) is proposed for PHEB to solve the problem of satisfying the fuel economy while preventing the motor temperature from being too high. First, the components of PHEB are analyzed and modeled. Second, ECMS is applied to the MPC framework, and the motor temperature is added to the cost function as an important optimization term. Third, the gray wolf optimization algorithm (GWOA) is selected to solve the minimum value of objective function offline. Finally, the accuracy of the model and the effectiveness of the proposed strategy in saving fuel are verified by MATLAB/Simulink and the hardware-in-the-loop tests. The result shows that compared with rule-based EMS, the fuel consumption of the proposed strategy is reduced by 15.81% and 15.45% under the urban dynamometer driving schedule and new combined driving cycle, respectively. Moreover, the proposed strategy can well restrain the motor temperature from being too high and keep it within a safe range.

Optimal Power Flows Control for Home Energy Management With Renewable Energy and Energy Storage Systems

This article presents a home energy management system (HEMS) based on the proposed adaptive artificial bee colony (AABC) optimization method, of managing a residential microgrid connected to the main grid. The studied microgrid consists of a photovoltaic system (PVS), a battery storage system (BSS), and electricity loads. The proposed AABC-based HEMS optimizes power flow ratio among PVS, BSS, and the main grid with respect to the load demand and electricity rate in order to minimize electricity consumption cost. Three dc-dc converters are actively controlled with an active dual-loop control architecture to dominate the power distribution among multi-power sources. The performances of the proposed optimal power flows control (PFC) under different electricity load levels and PVS generation conditions are examined. In the PFC experiments, the designed HEMS is implemented on a digital signal processor. The experimental results demonstrate the effectiveness and superiority of the proposed AABC-based HEMS compared with the rule-based control strategy and the equivalent consumption minimization strategy in terms of electricity consumption cost.

Multiobjective Optimization of Safety, Comfort, Fuel Economy, and Power Sources Durability for FCHEV in Car-Following Scenarios

Safety, comfort, and energy-saving oriented car-following issue for fuel cell/battery hybrid electric vehicle (FCHEV) is a comprehensive problem of vehicle dynamic and fuel economy. Combining adaptive cruise control (ACC) and energy management strategy (EMS) is proven to be one promising method to realize multiobjective co-optimization. However, in majority of existing researches, degradation of power sources is always ignored and trade-off among different objectives remains a burning issue, in this article, a Pareto-based EMS under car-following scenarios is proposed. Specifically, degradation models of fuel cell/battery are established and incorporated into multiobjective optimization functions. Then, based on back-stepping technique and equivalent consumption minimization strategy (ECMS), an integrated framework of ACC and EMS is developed, which realizes the coordination between vehicle external longitudinal dynamics control and internal powertrain energy management. For getting the trade-off among the abovementioned objectives, a Pareto-involved multiobjective optimization method is proposed to optimize control parameters of the integrated framework of ACC and EMS. The simulation results of Urban Dynamometer Driving Schedule (UDDS) test highlight that compared with the weighted-sum methods, the proposed method can utmost reduce average tracking error by 15.14%, variation of speed by 8.61%, equivalent fuel consumption cost by 7.56%, and comprehensive power sources degradation cost by 17.78%.

Intelligent energy management through neuro-fuzzy based adaptive ECMS approach for an optimal battery utilization in plugin parallel hybrid electric vehicle

The parallel hybrid electric vehicles (PHEV’s) superior performance relies on the associated energy management strategy. This work intends to improve the performance of real-time implementable equivalent consumption minimization strategy (ECMS) so that, optimal battery and fuel energy utilization can be guaranteed in the PHEV. In the ECMS algorithm, the incorporation of intelligent technique is the proposed approach for a novel robust equivalence factor correction concerning the battery state of charge (SOC) for various uncertain driving conditions. With this motive, the significance and necessity of dynamic adjustment in equivalence factor value are first elaborated to achieve the global optimal fuel economy. The two intelligent approaches of fuzzy logic and genetic algorithmically optimized adaptive neuro-fuzzy inference system (ANFIS) have been compared in the adaptive ECMS. Their performance is validated using the prepared three hybrid standard driving cycles and also for training the fuzzy inference system (FIS). The obtained result infers the fuzzy-PI-based AECMS provided the least terminal SOC variance of 3.25 whereas 30.03 for the conventional fixed PI. The proposed ANFIS-ECMS delivers terminal SOC of 29.41%, 28.22% and 28.37% for the three driving cycles which is having variance of 0.42. Even in the real-time validation of the ANFIS-AECMS, achieves a terminal SOC of 27.53% and a fuel economy of 33.37 km/l for the self-developed real-world driving cycle. The entire result of this work proves the proposed strategy achieves significant improvement in battery and fuel energy utilization and also the reduction in emissions compared with rule-based, conventional fixed PI ECMS and fuzzy-PI-based ECMS.

Optimal Energy Management for Hydrogen Economy in a Hybrid Electric Vehicle

Fuel cell hybrid electric vehicles (FCEVs) are mainly electrified by the fuel cell (FC) system. As a supplementary power source, a battery or supercapacitor (SC) is employed (besides the FC) to enhance the power response due to the slow dynamics of the FC. Indeed, the performance of the hybrid power system mainly depends on the required power distribution manner among the sources, which is managed by the energy management strategy (EMS). This paper considers an FCEV based on the proton exchange membrane FC (PEMFC)/battery/SC. The energy management strategy is designed to ensure optimum power distribution between the sources considering hydrogen consumption. Its main objective is to meet the electric motor’s required power with economic hydrogen consumption and better electrical efficiency. The proposed EMS combines the external energy maximization strategy (EEMS) and the bald eagle search algorithm (BES). Simulation tests for the Extra-Urban Driving Cycle (EUDC) and New European Driving Cycle (NEDC) profiles were performed. The test is supposed to be performed in typical conditions t = 25 °C on a flat road without no wind effect. In addition, this strategy was compared with the state machine control strategy, classic PI, and equivalent consumption minimization strategy. In terms of optimization, the proposed approach was compared with the original EEMS, particle swarm optimization (PSO)-based EEMS, and equilibrium optimizer (EO)-based EEMS. The results confirm the ability of the proposed strategy to reduce fuel consumption and enhance system efficiency. This strategy provides 26.36% for NEDC and 11.35% for EUDC fuel-saving and efficiency enhancement by 6.74% for NEDC and 36.19% for EUDC.

Research on Energy Management Strategy of Range Extender Electric Vehicle considering Temperature Effect under Different Heat Demands

In order to further improve the energy utilization of the range extender electric vehicle (REEV), the energy management strategy of the REEV under different heat demands and operating temperatures is studied. Firstly, for reducing the extra fuel consumption caused by engine operating temperature, the influence of engine lubricating oil temperature on the equivalence factor is analyzed, and a novel method is proposed, which can determine the equivalence factor by three parameters, including engine temperature, operating condition type, and battery state of charge (SOC). Secondly, to reduce the influence of frequent engine start/stop on fuel consumption, a method to establish the penalty factor based on battery SOC is proposed. Finally, the engine temperature rise law under low-temperature environment and the compressor drive mode determination method under high-temperature environment are studied according to the heat demand under different ambient temperatures. An adaptive equivalent consumption minimization strategy (ECMS) considering the effect of temperature under different heat demands is proposed on the basis of the above research. The simulation under the worldwide harmonized light vehicle test cycle (WLTC) drive cycle reveals that by using the adaptive ECMS, the vehicle operating cost is reduced by 5.69% in normal temperature environment, 0.53%-2.39% in a low-temperature environment, and 0.2%-2.01% in a high-temperature environment.

Comparative Assessment of Supervisory Control Algorithms for a Plug-In Hybrid Electric Vehicle

The study examines alternative on-board energy management system (EMS) supervisory control algorithms for plug-in hybrid electric vehicles. The optimum fuel consumption was sought between an equivalent consumption minimization strategy (ECMS) algorithm and a back-engineered commercial rule-based (RB) one, under different operating conditions. The RB algorithm was first validated with experimental data. A method to assess different algorithms under identical states of charge variations, vehicle distance travelled, and wheel power demand criteria is first demonstrated. Implementing this method to evaluate the two algorithms leads to fuel consumption corrections of up to 8%, compared to applying no correction. We argue that such a correction should always be used in relevant studies. Overall, results show that the ECMS algorithm leads to lower fuel consumption than the RB one in most driving conditions. The difference maximizes at low average speeds (<40 km/h), where the RB leads to more frequent low load engine operation. The two algorithms lead to fuel consumption differences of 3.4% over the WLTC, while the maximum difference of 24.2% was observed for a driving cycle with low average speed (18.4 km/h). Further to fuel consumption performance optimization, the ECMS algorithm also appears superior in terms of adaptability to different driving cycles.

Adaptive Model-Predictive-Control-Based Real-Time Energy Management of Fuel Cell Hybrid Electric Vehicles

To compete with battery electric vehicles, fuel cell (FC) hybrid electric vehicles (FCHEVs) are required to offer better performance in fuel economy and FC durability. To this end, this article proposes a novel real-time adaptive model predictive control (AMPC)-based energy management strategy (EMS) for FCHEVs to improve their fuel efficiency and mitigate the degradation of their onboard FC hybrid systems. First, a linear parameter-varying (LPV) prediction model of the FC hybrid system that considers the system parameter variation is developed. The model offers sufficient accuracy while enabling the real-time implementation capability of the AMPC. Then, an AMPC strategy is proposed to optimally distribute the load current of the FCHEV between the FC and the battery in real time. In each control interval of the AMPC, the LPV prediction model is updated online to adapt to the variations of the battery state of charge. The constrained optimization problem of the AMPC is then formulated to achieve a desired tradeoff among four performance metrics and is further transformed into a quadratic programming problem, which can be solved in real time. Hardware-in-the-loop tests are performed on a downscaled FC hybrid system with the proposed AMPC-based EMS, a commonly used rule-based EMS, an equivalent consumption minimization strategy, and an improved MPC-based EMS, respectively. Results show that among the four real-time EMSs, the AMPC-based EMS achieves the best performance in reducing hydrogen consumption and FC current fluctuation and the smallest optimality gap with respect to an offline dynamic programming-based optimal EMS.

Adaptive energy management for fuel cell hybrid power system with power slope constraint and variable horizon speed prediction

An adaptive energy management strategy (EMS) is proposed to improve the economy and reliability of the fuel cell vehicle. Firstly, a variable horizon speed prediction method based on the principal component analysis and the K-means clustering is constructed. Then, an adaptive equivalent consumption minimization strategy (AECMS) with power slope constraints was designed to minimize the hydrogen consumption while ensuring reliability. Finally, a proportional-integral controller is used to track the air flow and pressure of the fuel cell engine (FCE) under energy distribution. Simulation results under West Virginia University Suburban (WVUSUB) show that the proposed strategy can improve the speed prediction accuracy by 2.80% and 25.57%, and reduce the hydrogen consumption by 2.79% and 2.66%, respectively, compared with the fixed 12 s and 15 s horizon. Moreover, the control error of oxygen excess ratio and the cathode pressure under energy distribution are 0.0102 (0.51%) and 189.4 Pa (0.0935%), respectively, indicating better reliability than the strategy without constraint.

Minimum hydrogen consumption‐based energy management strategy for hybrid fuel cell unmanned aerial vehicles using direction prediction optimal foraging algorithm

AbstractIn hybrid energy storage systems of fuel cell unmanned aerial vehicles (UAVs), achieving energy management while minimizing hydrogen consumption is the main goal for economic aspects and endurance enhancement. The external energy maximization strategy (EEMS) and the equivalent consumption minimization strategy (ECMS) are commonly used energy management strategies. However, they use a gradient descent approach, which converges slowly and does not guarantee the optimal solution. Thus, this paper proposes an optimization method based on a direction prediction optimal foraging algorithm (OFA/DP), which has the advantages of high optimization capability and simple parameter definition. In this study, the hybrid energy storage system comprises fuel cells and lithium‐ion batteries for powering UAVs. To verify the validity of the proposed strategy, it is compared with rule‐based and optimized methods of state machine control, fuzzy logic control based on frequency separation, ECMS, EEMS, and genetic algorithm. The obtained results confirm the superiority of the proposed OFA/DP‐based EEMS method with an efficiency of 88.65% and a minimum hydrogen consumption of 19.06 g. Furthermore, it achieves optimal power distribution and leads to 38.62% minimization in hydrogen consumption.

A Novel A-ECMS Energy Management Strategy Based on Dragonfly Algorithm for Plug-in FCEVs.

The mechanical coupling of multiple powertrain components makes the energy management of 4-wheel-drive (4WD) plug-in fuel cell electric vehicles (PFCEVs) relatively complex. Optimizing energy management strategies (EMSs) for this complex system is essential, aiming at improving the vehicle economy and the adaptability of operating conditions. Accordingly, a novel adaptive equivalent consumption minimization strategy (A-ECMS) based on the dragonfly algorithm (DA) is proposed to achieve coordinated control of the powertrain components, front and rear motors, as well as the fuel cell system and the battery. To begin with, the equivalent consumption minimization strategy (ECMS) with extraordinary instantaneous optimization ability is used to distribute the vehicle demand power into the front and rear motor power, considering the different motor characteristics. Subsequently, under the proposed novel hierarchical energy management framework, the well-designed A-ECMS based on DA empowers PFCEVs with significant energy-saving advantages and adaptability to operating conditions, which are achieved by precise power distribution considering the operating characteristics of the fuel cell system and battery. These provide state-of-the-art energy-saving abilities for the multi-degree-of-freedom systems of PFCEVs. Lastly, a series of detailed evaluations are performed through simulations to validate the improved performance of A-ECMS. The corresponding results highlight the optimal control performance in the energy-saving performance of A-ECMS.

Predictive equivalent consumption minimization strategy for power-split hybrid electric vehicles using Monte Carlo algorithm

Bu çalışma, güç paylaşımlı bir hibrit elektrikli araç (HEA) için, Monte Carlo (MC) algoritmasına dayalı olarak tahmin edilen sürüş çevrimi hızlarını kullanan öngörülü eşdeğer tüketim minimizasyonu stratejisi (Ö-ETMS) önermektedir. Önerilen Ö-ETMS, enerji kaynakları arasındaki güç dağılımını en iyi şekilde belirlemek için MC algoritması tarafından tahmin edilen hız profillerinden tam olarak yararlanmaktadır. Bu çalışmada; MC tabanlı Ö-ETMS metodunu doğrulamak için, New European Driving Cycle (NEDC), Worldwide Harmonised Light Vehicles Test Procedure (WLTP), Urban Dynamometer Driving Schedule (UDDS), Highway Fuel Economy Test (HWFET), New York City Cycle (NYCC), California Unified Cycle (LA-92) ve tüm döngülerin kombinasyonu (ALL-CYC) çevrimleri kullanılmış; toplam yedi tekrarlı sürüş döngüsü altında bir dizi simülasyon çalışması gerçekleştirilmiştir. MC tabanlı Ö-ETMS stratejisi, standart ETMS ile karşılaştırılmıştır. NEDC çevriminde %6,01, WLTP çevriminde %9,09, UDDS çevriminde %6,33, HWFET çevriminde %5,14, NYCC çevriminde %1,96, LA-92 çevriminde %11,47 ve ALL-CYC çevriminde %7,92 oranla yakıt tasarrufu elde edilmiştir. Bu makaledeki sonuçlar, önerilen stratejinin yaygın olarak kullanılan temel yönteme kıyasla, rekabetçi bir yakıt tasarrufu sağladığını ortaya koymaktadır.

Energy management system optimization based on an LSTM deep learning model using vehicle speed prediction

The energy management of a Hybrid Electric Vehicle (HEV) is a global optimization problem, and its optimal solution inevitably entails knowing the entire mission profile. The exploitation of Vehicle-to-Everything (V2X) connectivity can pave the way for reliable short-term vehicle speed predictions. As a result, the capabilities of conventional energy management strategies can be enhanced by integrating the predicted vehicle speed into the powertrain control strategy. Therefore, in this paper, an innovative Adaptation algorithm uses the predicted speed profile for an Equivalent Consumption Minimization Strategy (A-V2X-ECMS). Driving pattern identification is employed to adapt the equivalence factor of the ECMS when a change in the driving patterns occurs, or when the State of Charge (SoC) experiences a high deviation from the target value. A Principal Component Analysis (PCA) was performed on several energetic indices to select the ones that predominate in characterizing the different driving patterns. Long Short-Term Memory (LSTM) deep neural networks were trained to choose the optimal value of the equivalence factor for a specific sequence of data (i.e., speed, acceleration, power, and initial SoC). The potentialities of the innovative A-V2X-ECMS were assessed, through numerical simulation, on a diesel Plug-in Hybrid Electric Vehicle (PHEV) available on the European market. A virtual test rig of the investigated vehicle was built in the GT-SUITE software environment and validated against a wide database of experimental data. The simulations proved that the proposed approach achieves results much closer to optimal than the conventional energy management strategies taken as a reference.

Equivalent Consumption Minimization Strategy Based on Belt Drive System Characteristic Maps for P0 Hybrid Electric Vehicles

A P0 system is used in hybrid automobiles to improve engine economy and performance. An essential element of the P0 system for effectively transmitting power to the drive train is the belt drive system (BDS). The features of electric machine (EM) and internal combustion engines (ICE) are taken into account by standard energy management systems, such as the equivalent consumption minimization strategy (ECMS). In order to maximize the effectiveness of the P0 system, this work provides a novel formulation of the ECMS, which considers the power loss map of the BDS in addition to the characteristic maps of EM and ICE. A test bench is built up to characterize the BDS, and it is verified using an open-loop Hardware in the Loop (HIL) in the WLTP driving cycle. To find the most appropriate equivalence factors for the ECMS, which would ordinarily be tuned through trial and error, a genetic algorithm (GA) is used. With regard to the standard ECMS, the proposed methodology intends to reduce fuel usage and CO2 emissions. Two belts in BDS were tested in the WLTP to achieve CO2 savings of 1.1 and 0.9 [g/km], indicating the enhancement of system performance by using the BDS power loss maps in ECMS.

Operating Cost Comparison of a Single-Stack and a Multi-Stack Hybrid Fuel Cell Vehicle Through an Online Hierarchical Strategy

One of the recently suggested solutions for enhancing the fuel economy and lifetime in a fuel cell (FC) hybrid electric vehicle (HEV) is the use of a multi-stack (MS) structure for the FC system. However, to fully realize the potential of this structure, the design of an appropriate energy management strategy (EMS) is necessary. This paper aims to compare the operating cost, including hydrogen consumption and degradation of the FC, between a single-stack (SS) and an MSFC-HEV. To do so, a hierarchical EMS, composed of two layers, is devised for the MS system. In the first layer, a rule-based strategy determines how many FCs should be ON according to the requested power, battery state of charge (SOC), and FCs degradations. In the second layer, an equivalent consumption minimization strategy (ECMS) is developed to determine the output power of each activated FC according to the cost function and constraints. Regarding the SS structure, ECMS is employed for power distribution. The purpose of this strategy is to decrease fuel consumption and FC system degradation costs in both structures. The performance of the ECMS is compared with dynamic programming (DP) as a global optimization strategy for validation purposes. The obtained results using experimental data show that an FC-HEV with an MS structure reaches less hydrogen and degradation costs than an SS one.