Economic energy management strategy design and simulation for a dual-stack fuel cell electric vehicle

This paper presents the design and simulation validation of two energy management strategies for dual-stack fuel cell electric vehicles. With growing concerns about environmental issues and the fossil energy crisis, finding alternative methods for vehicle propulsion is necessary. Proton exchange membrane (PEM) fuel cell systems are now considered to be one of the most promising alternative energy sources. In this work, the challenge of further improving the fuel economy and extending the driving range of a fuel cell vehicle is addressed by a dual-stack fuel cell system with specific energy management strategies. An efficiency optimization strategy and an instantaneous optimization strategy are proposed. Simulation validation for each strategy is conducted based on a dual-stack fuel cell electric vehicle model which follows the new European driving cycle (NEDC). Simulation results show that a dual-stack fuel cell system with proposed energy management strategies can significantly improve the fuel economy of a fuel cell vehicle and thus lengthen the driving range while being able to keep the start-stop frequency of the fuel cell stack within a reasonable range.     

A comparison of rule-based and model predictive controller-based power management strategies for fuel cell/battery hybrid vehicles considering degradation

Traditional power management systems for hybrid vehicles often focus on the optimization of one particular cost factor, such as fuel consumption, under specific driving scenarios. The cost factor is usually based on the beginning-of-life performance of system components. Typically, such strategies do not account for the degradation of the different components of the system over their lifetimes. This study incorporates the effect of fuel cell and battery degradation within their cost factors and investigates the impact of different power management strategies on fuel cell/battery loads and thus on the operating cost over the vehicle's lifetime. A simple rule-based power management system was compared with a model predictive controller (MPC) based system under a connected vehicle scenario (where the future vehicle speed is known a priori within a short time horizon). The combined cost factor consists of hydrogen consumption and the degradation of both the fuel cell stack and the battery. The results show that the rule-based power management system actually performs better and achieves lower lifetime cost compared to the MPC system even though the latter contains more information about the drive cycle. This result is explained by examining the changing dynamics of the three cost factors over the vehicle's lifetime. These findings reveal that a limited knowledge of traffic information might not be as useful for the power management of certain fuel cell/battery hybrid vehicles when degradation is taken into consideration, and a simple tuned rule-based controller is adequate to minimize the lifetime cost.     

Electric powertrain modeling of a fuel cell hybrid electric vehicle and development of a power distribution algorithm based on driving mode recognition

This paper proposes a novel fuzzy controller based on an adaptive membership function for optimum power management of a fuel cell hybrid electric vehicle (FCHEV). In the first phase, an electric powertrain model of the FCHEV is derived and a fuzzy controller is proposed. Then, the fuzzy controller is optimized using a genetic algorithm. The optimization process is accomplished through simulation for a given driving cycle. Since, however, the optimized result may vary according to the applied driving cycle for optimization, it is impossible for one optimized result to cover various driving cycles. In the second phase, an adaptive membership function based on a stochastic approach is proposed to guarantee optimum performance from the presented fuzzy controller, even though the driving cycle changes. This controller is referred to as the ‘Stochastic fuzzy controller’ (SFC) in this study. The SFC employs a stochastic approach where membership functions can be transformed statistically using a probability evaluated from driving pattern recognition. Then, driving cycle analysis is performed through off-line simulation and hardware in a loop simulation (HILS) test for four driving cycles. Finally, the SFC shows the best performance in terms of minimum fuel consumption and state-of-charge (SoC) maintenance.     

Real time power management strategy for fuel cell hybrid electric bus based on Lyapunov stability theorem

The fuel cell/battery durability and hybrid system stability are major considerations for the power management of fuel cell hybrid electric bus (FCHEB) operating on complicated driving conditions. In this paper, a real time nonlinear adaptive control (NAC) with stability analyze is formulated for power management of FCHEB. Firstly, the mathematical model of hybrid power system is analyzed, which is established for control-oriented design. Furthermore, the NAC-based strategy with quadratic Lyapunov function is set up to guarantee the stability of closed-loop power system, and the power split between fuel cell and battery is controlled with the durability consideration. Finally, two real-time power management strategies, state machine control (SMC) and fuzzy logic control (FLC), are implemented to evaluate the performance of NAC-based strategy, and the simulation results suggest that the guaranteed stability of NAC-based strategy can efficiently prolong fuel cell/battery lifespan and provide better fuel consumption economy for FCHEB.     

A robust online energy management strategy for fuel cell/battery hybrid electric vehicles

Traditional optimization-based energy management strategies (EMSs) do not consider the uncertainty of driving cycle induced by the change of traffic conditions, this paper proposes a robust online EMS (ROEMS) for fuel cell hybrid electric vehicles (FCHEV) to handle the uncertain driving cycles. The energy consumption model of the FCHEV is built by considering the power loss of fuel cell, battery, electric motor, and brake. An offline linear programming-based method is proposed to produce the benchmark solution. The ROEMS instantaneously minimizes the equivalent power of fuel cell and battery, where an equivalent efficiency of battery is defined as the efficiency of hydrogen energy transforming to battery energy. To control the state of charge of battery, two control coefficients are introduced to adjust the power of battery in objective function. Another penalty coefficient is used to amend the power of fuel cell, which reduces the load change of fuel cell so as to slow the degradation of fuel cell. The simulation results indicate that ROEMS has good performance in both fuel economy and load change control of fuel cell. The most important advantage of ROEMS is its robustness and adaptivity, because it almost produces the optimal solution without changing the control parameters when driving cycles are changed.     

Particle swarm optimization applied to the co-design of a fuel cell air circuit

This paper presents an optimization approach applied to a whole fuel cell (FC) air supply system including its geometry and its control. The aim is to optimize its power consumption along with its mass. Particle swarm optimization (PSO) algorithm is used to define the design parameters of both permanent magnet synchronous motor (PMSM) and a fuzzy logic controller (FLC). The results are compared with those obtained by a sequential optimization process and advantages of co-design optimization approach are clearly shown. Indeed, a significant reduction of the objective function (made up on both motor mass and energy consumption) on a considered operating cycle can be obtained.     

A comparison study of battery size optimization and an energy management strategy for FCHEVs based on dynamic programming and convex programming

Aiming to address the hydrogen economy and system efficiency of a fuel cell hybrid electric vehicle, this paper proposes comparison research of battery size optimization and an energy management strategy. One approach is based on a bi-loop dynamic programming strategy, which selects the optimal one by initializing the battery parameters in the outer loop and performs energy distribution in the inner loop. The other approach is a framework based on convex programming, which can simultaneously design energy management strategies and optimize battery size. In the dynamic programming algorithm, the influence of the different discrete steps of state variables on the results is analysed, and a discrete step that can guarantee the accuracy of the algorithm and reduce computational time is selected. The results based on the above two algorithms and considering the transient response limitations of the fuel cell are analysed as well. Finally, two driving cycles are chosen to verify and compare the performance of the proposed methodology. Simulation results show that the dynamic programming-based energy management strategy and battery size provide more accurate results, and the transient response of the fuel cell has little effect on the optimization results of the battery size and energy management strategies.     

Recent development in design a state-of-art proton exchange membrane fuel cell from stack to system: Theory,integration and prospective

As an efficient energy converter, the proton exchange membrane fuel cell (PEMFC) is developed to couple various applications, including portable applications, transportation, stationary power generation, unmanned underwater vehicles, and air independent propulsion. PEMFC is a complex system consisting of different components that can be influenced by many factors, such as material properties, geometric designs operating conditions, and control strategies. The interaction between components and subsystems could affect the performance, durability, and lifespan of PEMFC system. To design a high performance, long lifespan, high durability PEMFC, it's essential to comprehensively understand the coupling effect of different factors on the overall performance and durability of PEMFCs. This review will present existing research on basis of four aspects, involving fuel cell stack design, subsystems design and management, mass transfer enhancement, and system integration. Firstly, the multi-physics intergradation and component design of PEMFC are reviewed with the designing mechanisms and recent progress. Besides, mass transfer enhancement methods are discussed by bipolar plate design and membrane electrode assembly optimization. Then, water management, thermal management, and fuel management are summarized to provide design guidance for PEMFC. The specifications design and system management for various engineering applications are briefly presented.     

Cost-optimal design and energy management of fuel cell electric trucks

Road freight transport on hilly routes represents a significant challenge for the advancement of fuel cell electric trucks because of the high-performance requirements for fuel consumption, vehicle lifetime, and battery charge control. Therefore, it is essential to optimize the vehicle design and energy management, which greatly influence the driving performance and total cost of ownership. This paper focuses on the cost-optimal design and energy management of fuel cell electric trucks, considering five key influencing factors: powertrain component sizing, driving cycle, vehicle weight, component degradation, and market prices. The cost optimization relies on a novel predictive energy management scheme based on dynamic programming and the systematic calibration of control parameters. The paper analyzes the simulation results to highlight three main findings for fuel cell electric trucks: 1) cost-optimal energy management is essential to define the best trade-off between fuel consumption and component degradation; 2) the total cost of ownership is significantly influenced by component sizing, driving cycles, vehicle weight, and market prices; 3) predictive energy management is highly beneficial in challenging road topographies for substantial cost-saving and lower component size requirements.     

Performance optimization of hybrid hydrogen fuel cell-electric vehicles in real driving cycles

In this research study, a fuel cell-electric hybrid car is studied. This car includes an electric motor that is connected to a fuel cell and a complex which includes a battery pack and an Ultracapacitor. The assessment of this hybrid vehicle is conducted by using various driving cycles such as FTP-75 driving cycle, NEDC driving cycle and SFTP-SC03 driving cycle. Battery state of charge (SoC) and hydrogen fuel consumption are the effective parameters influencing the vehicle performance. For analysing the performance of this vehicle, an innovative computational model is considered. In this innovative computational model, an accurate control strategy is considered in order to control the power demand, staying the battery packs and the Ultracapacitor state of charge in a limited domain. Results show that in NEDC driving cycle, by means of using Ultracapacitor in this model, 3.3% reduction in fuel consumption and 20.2% decrease in the difference between initial and final State of Charge (SoC) in battery pack can be achieved. In addition, a robust regenerative braking control strategy is used in order to recover some parts of the wasted energy in braking driving modes.     

Performance enhancement of energy extraction capability for fuel cell implementations with improved Cuckoo search algorithm

This paper addresses an improved optimization method to enhance the energy extraction capability of fuel cell implementations. In this study, the proposed method called Dynamic Cuckoo Search Algorithm (DCSA) is tested in a stand-alone fuel cell in order to control the system power under dynamic temperature response. In the operational process, a fuel cell is connected to a load through a dc-dc boost converter, and DCSA is utilized to adjust the switching duration in dc-dc converter by using voltage, current and temperature parameters. In this way, it controls the output voltage to maximize power delivery capability at the demand-side and eliminates the drawback of conventional cuckoo search algorithm (CSA) which cannot change duty cycle under operating temperature variations. In this regard, DCSA shows a significant improvement in terms of system response and achieves a more efficient power extraction than the conventional CSA method. In order to demonstrate the system performance, the stand-alone fuel cell system is constructed in Simulink environment via a processor-in the-loop (PIL) based digital implementation and analyzed by using different optimization methods. In the analysis section, the results of the proposed method are compared with conventional methods (perturb&observe mppt, incremental conductance mppt, and particle swarm optimization). In this context, convergence speed and efficiency analysis for both methods verify that the DCSA gives original results compared to conventional methods.     

A novel predictive power flow control strategy for hydrogen city rail train

Hydrogen-based vehicular traction has already reached a mature technological level and can replace the more polluting diesel engines. The adoption of this technology can also alleviate the carbon footprint issue of the rail trains running on non-electrified lines.This study presents a model and a numerical performance analysis of an electric hybrid train in an urban context. The train uses hydrogen as fuel and operates over non-electrified lines with zero local emission.The electric traction motors of the train are fed by a hybrid power unit consisting of several hydrogen fuel cell stacks operating independently in on/off mode and a set of flywheel energy storage devices.Each component of the power train is modeled separately and its operating limits are chosen on the base of technical literature.An innovative predictive logic to manage power flows is defined and proposed with the aim to minimize the fuel consumption. Furthermore, this approach uses a regenerative electrical braking and eliminates dissipative devices, like rheostats, which are commonly utilized onboard electric trains.This predictive approach is based on the optimal management of the power unit components according to the advanced knowledge of the data of the rail vehicle, the characteristics of path, drive cycle and payload for an established route.The fuel cell stacks operate accordingly to the average traction power requirement in each railway line section, whereas the flywheel energy storage system manages the dynamic power.A parametric model of the system and a respective software tool have been developed; this implementation, that incorporates many tunable parameters of the train and rail path, is able to simulate the rail train operating on a specific railway path by implementing the novel control strategy.An existing single track non-electrified line, designed again for urban service, has been selected as a case study to evaluate the performance of the proposed system.The specific fuel consumptions obtained with the novel control strategy and with a single fuel cell system operating at constant power are compared under the same operating conditions.The results highlight that significant fuel savings can be achieved.     

Power management and design optimization of fuel cell/battery hybrid vehicles

Power management strategy is as significant as component sizing in achieving optimal fuel economy of a fuel cell hybrid vehicle (FCHV). We have formulated a combined power management/design optimization problem for the performance optimization of FCHVs. This includes subsystem-scaling models to predict the characteristics of components of different sizes. In addition, we designed a parameterizable and near-optimal controller for power management optimization. This controller, which is inspired by our stochastic dynamic programming results, can be included as design variables in system optimization problems. Simulation results demonstrate that combined optimization can efficiently provide excellent fuel economy.     

Lifespan-consciousness and minimum-consumption coupled energy management strategy for fuel cell hybrid vehicles via deep reinforcement learning

Energy management strategy (EMS) based on optimized deep reinforcement learning plays a critical role in minimizing fuel consumption and prolonging the fuel cell stack lifespan for fuel cell hybrid vehicles. The deep Q-learning (DQL) and deep deterministic policy gradient (DDPG) algorithms with priority experience replay are proposed in this research. The factors of fuel economy and power fluctuation are incorporated into the multi-objective reward functions to decline the fuel consumption and extend the lifetime of fuel cell stack. In addition, the degradation rate is introduced to reflect the lifetime of fuel cell stack. Furthermore, compared to the referenced optimally energy management strategy (dynamic planning), the DQL-based and DDPG-based EMS with prioritized experience replay (DQL-PER, DDPG-PER) are evaluated in hydrogen consumption and cumulative degradation of fuel cell stack under four driving cycles, FTP75, US06-2, NEDC and LA92-2, respectively. The training results reveal that the DQL-PER-based EMS performances better under FTP75 and US06-2 driving cycles, whereas DDPG-PER-based EMS has better performance under NEDC driving cycle, which provide a potential for applying the proposed algorithm into multi-cycles.     

A comparison of real-time energy management strategies of FC/SC hybrid power source: Statistical analysis using random cycles

This paper presents a comparative study of two promising real-time energy management strategies for fuel cell electric vehicle applications: adaptive equivalent consumption minimization strategy (A-ECMS), and stochastic dynamic programming (SDP). An off-line algorithm –classic dynamic programming - provides reference results. On-line and off-line strategies are tested both in simulation and using a dedicated test bench completely consistent with an electric scooter powertrain.The hybrid power source combines a fuel cell, a supercapacitor pack and two related power converters. The system model is carefully calibrated using experimental data. This allows meaningful identification of parameters of the various strategies. The model data is determined using a motorcycle certification driving cycle.The robustness of each strategy is then analyzed using a large number of random driving cycles. Experimental and simulation results show that a specific SDP approach, based on Markov chain modeling, has the best overall performance in real-world driving conditions. It achieves the minimum average hydrogen consumption while respecting the state-sustaining constraint. Conversely, the A-ECMS results lack robustness and show poor performance indexes when facing unknown real world power demand profile. In conclusion, the present results indicate SDP is an interesting approach for future hybrid source energy allocation.     

Variable structure battery-based fuel cell hybrid power system and its incremental fuzzy logic energy management strategy

A hybrid power system consists of a fuel cell and an energy storage device like a battery and/or a supercapacitor possessing high energy and power density that beneficially drives electric vehicle motor. The structures of the fuel cell-based power system are complicated and costly, and in energy management strategies (EMSs), the fuel cell's characteristics are usually neglected. In this study, a variable structure battery (VSB) scheme is proposed to enhance the hybrid power system, and an incremental fuzzy logic method is developed by considering the efficiency and power change rate of fuel cell to balance the power system load. The principle of VSB is firstly introduced and validated by discharge and charge experiments. Subsequently, parameters matching of the fuel cell hybrid power system according to the proposed VSB are designed and modeled. To protect the fuel cell as well as ensure the efficiency, a fuzzy logic EMS is formulated via setting the fuel cell operating in a high efficiency and generating an incremental power output within the affordable power slope. The comparison between a traditional deterministic rules-based EMS and the designed fuzzy logic was implemented by numerical simulation in three different operation conditions: NEDC, UDDS, and user-defined driving cycle. The results indicated that the incremental fuzzy logic EMS smoothed the fuel cell power and kept the high efficiency. The proposed VSB and incremental fuzzy logic EMS may have a potential application in fuel cell vehicles.     

Experimental study of a fuel cell power train for road transport application

The development of fuel cell electric vehicles requires the on-board integration of fuel cell systems and electric energy storage devices, with an appropriate energy management system. The optimization of performance and efficiency needs an experimental analysis of the power train, which has to be effected in both stationary and transient conditions (including standard driving cycles).In this paper experimental results concerning the performance of a fuel cell power train are reported and discussed. In particular characterization results for a small sized fuel cell system (FCS), based on a 2.5 kW PEM stack, alone and coupled to an electric propulsion chain of 3.7 kW are presented and discussed. The control unit of the FCS allowed the main stack operative parameters (stoichiometric ratio, hydrogen and air pressure, temperature) to be varied and regulated in order to obtain optimized polarization and efficiency curves. Experimental runs effected on the power train during standard driving cycles have allowed the performance and efficiency of the individual components (fuel cell stack and auxiliaries, dc–dc converter, traction batteries, electric engine) to be evaluated, evidencing the role of output current and voltage of the dc–dc converter in directing the energy flows within the propulsion system.     

Sizing of a fuel cell electric vehicle: A pinch analysis-based approach

Polymer electrolyte fuel cells are considered as a promising alternative to mitigate the CO₂ emission in the transport sector. To achieve an efficient and cost-effective system, hybridisation of the energy storage system with a fuel cell is important. Efficient management of energy is the key in order to achieve an efficient and cost-effective configuration for fuel cell electric vehicle. Optimum sizing of the power source and energy storage system, which is capable of meeting the load requirement of the driving cycle is the key challenge for achieving efficient and cost-effective system. In this work, an alternative methodology based on the principles of pinch analysis is proposed, for sizing the energy storage system and the fuel cell for fuel cell-based electric vehicle, and validated for the Worldwide Harmonized Light Vehicle Test Cycle (WLTC) class-3 driving cycle.     

Estimation of indirect CO2 emissions of a hydrogen‐powered medium size vehicle for the NEDC cycle

Reduction in greenhouse effect gases emission is a major source of concern nowadays. Internal combustion engines, as the most widely used power generation mean for transportation, represent a large share of such gases, which motivates active research efforts for alternative solutions. In this regard, PEM fuel cells represent a promising prospect and are thoroughly investigated, whether experimentally or through numerical simulation. The present work presents a simulation of the power potential of a PEM fuel cell, which is integrated to the full power electric traction chain of a medium size car. The cell potential is modelled by taking into account the different types of polarization. The driving performances of the vehicle and its hydrogen consumption are evaluated through a simple mathematical model and an application is performed for the New European Driving Cycle (NEDC) standard driving cycle. A preliminary sizing of the proton exchange membrane fuel cell (PEMFC) membrane area for the chosen vehicle is presented, along with that of a hydrogen storage tank for a typical autonomy. The main goal of the simulation is to estimate CO₂ indirect emissions due to the production of the needed hydrogen for the cycle via an electrolyser, compared with the case of a gasoline fueled vehicle. This is performed solely on a ‘fuel tank to wheel’ basis in order to have comparable figures. The results indicate that the environmental advantage of hydrogen cars is quite questionable if hydrogen is produced using carbon‐based energy sources. Copyright © 2009 John Wiley & Sons, Ltd.     

Modeling and multi-objective optimization of a stand-alone photovoltaic-wind turbine-hydrogen-battery hybrid energy system based on hysteresis band

Hybrid renewable energy system (HRES) can provide power without emission for off-grid areas. Due to intermittency of renewable energy, energy storage system (ESS) is essential for reliable power supply, while its cost is still relatively high. Appropriate power management strategy (PMS) can help to delay the degradation of energy storage devices and reduce the system cost. In this study, power management strategy and configuration optimization of the system are focused and the study includes three main contributions. First, mathematical models of the system, including photovoltaics (PVs), wind turbines (WTs), batteries, fuel cells (FCs), electrolyzers (ELZs), and hydrogen tanks are developed. The degradation of fuel cells and electrolyzers is considered in the modeling process. Second, power management strategy considering hysteresis band is employed to control energy flow to delay fuel cell and electrolyzer degradation. Third, a multi-objective optimization function including the system net annual value (NAV), loss of power supply probability (LPSP) and excess energy (E_excess) is established. Non-dominating Sorting Genetic Algorithm II (NSGA-II) is used to solve objective function. The results demonstrate that using hysteresis band help improve the system performance and reduce the cost. In addition, by setting the goal of excess energy, system reliability is well preserved with a LPSP as low as 0.92%. Compared with other optimization algorithms such as MOEA/D, NSGA-II has a smaller SI value of 422.10 and a larger DI value of 830.78, therefore the Pareto solution obtained by NSGA-II has a more uniform distribution and larger coverage.