Drive-train simulator for a fuel cell hybrid vehicle

The model formulation, development process, and experimental validation of a new vehicle powertrain simulator called LFM (Light, Fast, and Modifiable) are presented. The existing powertrain simulators were reviewed and it was concluded that there is a need for a new, easily modifiable simulation platform that will be flexible and sufficiently robust to address a variety of hybrid vehicle platforms. First, the structure and operating principle of the LFM simulator are presented, followed by a discussion of the subsystems and input/output parameters. Finally, a validation exercise is presented in which the simulator's inputs were specified to represent the University of Delaware's fuel cell hybrid transit vehicle and “driven” using an actual drive cycle acquired from it. Good agreement between the output of the simulator and the physical data acquired by the vehicle's on-board sensors indicates that the simulator constitutes a powerful and reliable design tool.     

Comparative study of energy management systems for a hybrid fuel cell electric vehicle - A novel mutative fuzzy logic controller to prolong fuel cell lifetime

Hybrid fuel cell battery electric vehicles require complex energy management systems (EMS) in order to operate effectively. Poor EMS can result in a hybrid system that has low efficiency and a high rate of degradation of the fuel cell and battery pack. Many different types of EMS have been reported in the literature, such as equivalent consumption minimisation strategy and fuzzy logic controllers, which typically focus on a single objective optimisations, such as minimisation of H₂ usage. Different vehicle and system specifications make the comparison of EMSs difficult and can often lead to misleading claims about system performance. This paper aims to compare different EMSs, against a range of performance metrics such as charge sustaining ability and fuel cell degradation, using a common modelling framework developed in MATLAB/Simulink - the Electric Vehicle Simulation tool-Kit (EV-SimKit). A novel fuzzy logic controller is also presented which mutates the output membership function depending on fuel cell degradation to prolong fuel cell lifetime – the Mutative Fuzzy Logic Controller (MFLC). It was found that while certain EMSs may perform well at reducing H₂ consumption, this may have a significant impact on fuel cell degradation, dramatically reducing the fuel cell lifetime. How the behaviour of common EMS results in fuel cell degradation is also explored. Finally, by mutating the fuzzy logic membership functions, the MFLC was predicted to extend fuel cell lifetime by up to 32.8%.     

Energy distribution analyses of an additional traction battery on hydrogen fuel cell hybrid electric vehicle

Hydrogen is the most abundant element in the world and produces only water vapor as a result of chemical reaction that occurred in fuel cells. Therefore, fuel cell electric vehicles, which use hydrogen as fuel, continue its growing trend in the sector. In this study, an energy distribution comparison is carried out between fuel cell electric vehicle and fuel cell hybrid electric vehicle. Hybridization of fuel cell electric vehicle is designed by equipped a traction battery (15 kW). Modeled vehicles were prepared under AVL Cruise program with similar chassis and same fuel cell stacks for regular determining process. Numerical analyses were presented and graphed with instantaneous results in terms of sankey diagrams with a comparison task. WLTP driving cycle is selected for both vehicles and energy input/output values given with detailed analyses. The average consumption results of electric and hydrogen usage is found out as 4.07 kWh and 1.125 kg/100 km respectively for fuel cell electric vehicle. On the other hand, fuel cell hybrid electric vehicle’s average consumption results figured out as 3.701 kWh for electric and 0.701 kg/100 km for hydrogen consumption. As a result of this study, fuel cell hybrid electric vehicle was obtained better results rather than fuel cell electric vehicle according to energy and hydrogen consumption with 8% and 32%, respectively.     

An innovative optimal power allocation strategy for fuel cell, battery and supercapacitor hybrid electric vehicle

An optimal design of a three-component hybrid fuel cell electric vehicle comprised of fuel cells, battery, and supercapacitors is presented. First, the benefits of using this hybrid combination are analyzed, and then the article describes an active power-flow control strategy from each energy source based on optimal control theory to meet the demand of different vehicle loads while optimizing total energy cost, battery life and other possible objectives at the same time. A cost function that minimizes the square error between the desired variable settings and the current sensed values is developed. A gain sequence developed compels the choice of power drawn from all devices to follow an optimal path, which makes trade-offs among different targets and minimizes the total energy spent. A new method is introduced to make the global optimization into a real-time based control. A model is also presented to simulate the individual energy storage systems and compare this invention to existing control strategies, the simulation results show that the total energy spent is well saved over the long driving cycles, also the fuel cell and batteries are kept operating in a healthy way.     

Exergoeconomic analysis and optimization of a new hybrid fuel cell vehicle

This paper introduces thermodynamic and economic analyses on a newly developed energy system for powering hybrid vehicles based on both energy and exergy concepts. The proposed hybrid propulsion system incorporates a liquefied ammonia tank, ammonia dissociation and separation unit (DSU), an internal combustion engine (ICE), and a fuel cell (FC) system. The exhaust gases released from the ICE are exploited to supply the necessary thermal energy to decompose ammonia thermally into hydrogen and nitrogen on board. The ICE is fuelled with a blend of ammonia and hydrogen generated from the DSU. The additional hydrogen released from the DSU will also be provided to the fuel cell system to run the FC and generate electric power, which will be supplied to the electric motor to provide the required traction to the vehicle. An optimization study is also performed to identify optimum design variables. The parametric studies are included in this investigation to evaluate the influence of varying the different operational parameters on the system energy and exergy efficiencies and both total cost rate and exergoeconomic factor values of the system.     

Energy management strategy based on fuzzy logic for a fuel cell hybrid bus

Fuel cell vehicles, as a substitute for internal-combustion-engine vehicles, have become a research hotspot for most automobile manufacturers all over the world. Fuel cell systems have disadvantages, such as high cost, slow response and no regenerative energy recovery during braking; hybridization can be a solution to these drawbacks. This paper presents a fuel cell hybrid bus which is equipped with a fuel cell system and two energy storage devices, i.e., a battery and an ultracapacitor. An energy management strategy based on fuzzy logic, which is employed to control the power flow of the vehicular power train, is described. This strategy is capable of determining the desired output power of the fuel cell system, battery and ultracapacitor according to the propulsion power and recuperated braking power. Some tests to verify the strategy were developed, and the results of the tests show the effectiveness of the proposed energy management strategy and the good performance of the fuel cell hybrid bus.     

Energy control of supercapacitor/fuel cell hybrid power source

This paper deals with a flatness based control principle in a hybrid system utilizing a fuel cell as a main power source and a supercapacitor as an auxiliary power source. The control strategy is based on regulation of the dc bus capacitor energy and, consequently, voltage regulation. The proposed control algorithm does not use a commutation algorithm when the operating mode changes with the load power variation and, thus, avoids chattering effects. Using the flatness based control method, the fuel cell dynamic and its delivered power is perfectly controlled, and the fuel cell can operate in a safe condition. In the hybrid system, the supercapacitor functions during transient energy delivery or during energy recovery situations. To validate the proposed method, the control algorithms are executed in dSPACE hardware, while analogical current loops regulators are employed in the experimental environment. The experimental results prove the validity of the proposed approach.     

Experimental investigation of supercapacitor based regenerative energy storage for a fuel cell vehicle equipped with an alternator

Recently, researchers have devoted more attention to supercapacitors (SCs) to integrate with batteries in energy storage systems (ESSs) for vehicle applications. In this study, we attempted to characterize the use of SCs in the ESS for a PEM fuel cell vehicle equipped with an alternator to maximize the performance of regenerative braking. We applied lithium-ion batteries (LIBs) and SCs as energy storage devices to examine their effect on ESS. Then we used a hysteresis brake to apply controllable braking force on the flywheel to form hybrid braking (HB) and made efforts to study its behavior to suggest a braking control strategy. We also ran the whole system over the rotational speed to cover the range of driving speed. At last, we sized the SCs for the most commonly used fuel cell electric vehicle (FCEV) in Korea, i.e., Hyundai NEXO, based on the results obtained from the above study by alternator efficiencies.     

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.     

Analysis of a fuel cell hybrid commuter railway vehicle

This study presents paper presents an analysis of the potential CO₂ savings that could be gained through the introduction of hydrogen-powered fuel cells on a commuter-style railway route. Vehicle is modelled as a fuel cell series hybrid. The analysis consists of power/energy flow models of a fuel cell stack, battery pack and hybrid drive controller. The models are implemented in a custom C# application and are capable of providing key parametric information of the simulated journey and individual energy drive components. A typical commuter return journey between Stratford Upon Avon and Birmingham is investigated. The fuel cell stack and battery pack behaviour is assessed for different stack sizes, battery sizes and control strategies to evaluate the performance of the overall system with the aim of understanding the optimum component configuration. Finally, the fuel (H₂) requirements are compared with typical diesel and hybrid-diesel powered vehicles with the aim of understanding the potential energy savings gained from such a fuel cell hybrid vehicle.     

Optimal vehicle control strategy of a fuel cell/battery hybrid city bus

In this article, an optimal vehicle control strategy based on a time-triggered controller area network (TTCAN) system for a polymer electrolyte membrane (PEM) fuel cell/nickel-metal hydride (Ni-MH) battery powered city bus is presented. Aiming at improving the fuel economy of the city bus, the control strategy comprises an equivalent consumption minimization strategy (ECMS) and a braking energy regeneration strategy (BERS). On the basis of the introduction of a battery equivalent hydrogen consumption model incorporating a charge-sustaining coefficient, an analytical solution to the equivalent consumption minimization problem is given. The proposed strategy has been applied in several city buses for the Beijing Olympic Games of 2008. Results of the “China city bus typical cycle” testing show that, the ECMS and the BERS lowered hydrogen consumption by 2.5% and 15.3% respectively, compared with a rule-based strategy. The BERS contributes much more than the ECMS to the fuel economy, because the fuel cell system does not leave much room for the optimal algorithm in improving the efficiency.     

Optimization based energy management strategy for fuel cell/battery/ultracapacitor hybrid vehicle considering fuel economy and fuel cell lifespan

Optimization of energy management strategy (EMS) for fuel cell/battery/ultracapacitor hybrid electrical vehicle (FCHEV) is primarily aimed on reducing fuel consumption. However, serious power fluctuation has effect on the durability of fuel cell, which still remains one challenging barrier for FCHEVs. In this paper, we propose an optimized frequency decoupling EMS using fuzzy control method to extend fuel cell lifespan and improve fuel economy for FCHEV. In the proposed EMS, fuel cell, battery and ultracapacitor are employed to supply low, middle and high-frequency components of required power, respectively. For accurately adjusting membership functions of proposed fuzzy controllers, genetic algorithm (GA) is adopted to optimize them considering multiple constraints on fuel cell power fluctuation and hydrogen consumption. The proposed EMS is verified by Advisor-Simulink and experiment bench. Simulation and experimental results confirm that the proposed EMS can effectively reduce hydrogen consumption in three typical drive cycles, limit fuel cell power fluctuation within 300 W/s and thus extend fuel cell lifespan.     

On-line fuzzy energy management for hybrid fuel cell systems

Fuel Cell Hybrid Vehicles (FCHV) can reach near zero emission by removing the conventional internal combustion from the vehicle powertrain. Nevertheless, before seeing competitive and efficient FCHV on the market, at market prices, different technical, economic, and social challenges should be overcome. A typical hybrid fuel cell powertrain combines a fuel cell stack and a dedicated energy storage system along with their necessary power converters. Energy storage systems are used in order to enhance the well-to-wheel efficiency and thus reducing the hydrogen consumption. An efficient management of power flows on the vehicle, allows optimizing the recovery of energy braking. Moreover, working in the fuel cell maximum efficiency leads to reduced thermal losses and thus to the downsizing of the heat exchangers. This paper presents an enhanced control of the power flows on a FCHV in order to reduce the hydrogen consumption, by generating and storing the electrical energy only at the most suitable moments on a given driving cycle. While the off-line optimization-based on dynamic programming algorithm offers the necessary optimal comparison reference on a known demand, the proposed strategy which can be implemented on-line, is based on a fuzzy logic decision system. The fine tuning of the fuzzy system parameters (mainly the membership functions and the gains), is made using a genetic algorithm and the fuzzy supervisor shows performing results for different load profiles.     

A new topology of fuel cell hybrid power source for efficient operation and high reliability

This paper analyzes a new fuel cell Hybrid Power Source (HPS) topology having the feature to mitigate the current ripple of the fuel cell inverter system. In the operation of the inverter system that is grid connected or supplies AC motors in vehicle application, the current ripple normally appears at the DC port of the fuel cell HPS. Consequently, if mitigation measures are not applied, this ripple is back propagated to the fuel cell stack. Other features of the proposed fuel cell HPS are the Maximum Power Point (MPP) tracking, high reliability in operation under sharp power pulses and improved energy efficiency in high power applications. This topology uses an inverter system directly powered from the appropriate fuel cell stack and a controlled buck current source as low power source used for ripple mitigation. The low frequency ripple mitigation is based on active control. The anti-ripple current is injected in HPS output node and this has the LF power spectrum almost the same with the inverter ripple. Consequently, the fuel cell current ripple is mitigated by the designed active control. The ripple mitigation performances are evaluated by indicators that are defined to measure the mitigation ratio of the low frequency harmonics. In this paper it is shown that good performances are obtained by using the hysteretic current control, but better if a dedicated nonlinear controller is used. Two ways to design the nonlinear control law are proposed. First is based on simulation trials that help to draw the characteristic of ripple mitigation ratio vs. fuel cell current ripple. The second is based on Fuzzy Logic Controller (FLC). The ripple factor is up to 1% in both cases.     

Model-based power control strategy development of a fuel cell hybrid vehicle

An integrated procedure for math modeling and power control strategy design for a fuel cell hybrid vehicle (FCHV) is presented in this paper. Dynamic math model of the powertrain is constructed firstly, which includes four modules: fuel cell engine, DC/DC inverter, motor-driver, and power battery. Based on the mathematic model, a power control principle is designed, which uses full-states closed-loop feedback algorithm. To implement full-states feedback, a Luenberger state observer is designed to estimate open circuit voltage (OCV) of the battery, which make the control principle not sensitive to the battery SOC (state of charge) estimated error. Full-states feedback controller is then designed through analyzing step responding of the powertrain and test data. At last of the paper, the results of simulation and field test are illustrated. The results show that the power control strategy designed takes into account the performance and economy characteristics of components of the FCHV powertrain and achieves the control object excellently.     

Comparative analysis of battery electric,hydrogen fuel cell and hybrid vehicles in a future sustainable road transport system

This paper compares battery electric vehicles (BEV) to hydrogen fuel cell electric vehicles (FCEV) and hydrogen fuel cell plug-in hybrid vehicles (FCHEV). Qualitative comparisons of technologies and infrastructural requirements, and quantitative comparisons of the lifecycle cost of the powertrain over 100,000 mile are undertaken, accounting for capital and fuel costs. A common vehicle platform is assumed. The 2030 scenario is discussed and compared to a conventional gasoline-fuelled internal combustion engine (ICE) powertrain. A comprehensive sensitivity analysis shows that in 2030 FCEVs could achieve lifecycle cost parity with conventional gasoline vehicles. However, both the BEV and FCHEV have significantly lower lifecycle costs. In the 2030 scenario, powertrain lifecycle costs of FCEVs range from $7360 to $22,580, whereas those for BEVs range from $6460 to $11,420 and FCHEVs, from $4310 to $12,540. All vehicle platforms exhibit significant cost sensitivity to powertrain capital cost. The BEV and FCHEV are relatively insensitive to electricity costs but the FCHEV and FCV are sensitive to hydrogen cost. The BEV and FCHEV are reasonably similar in lifecycle cost and one may offer an advantage over the other depending on driving patterns. A key conclusion is that the best path for future development of FCEVs is the FCHEV.     

Prediction-based optimal power management in a fuel cell/battery plug-in hybrid vehicle

A prediction-based power management strategy is proposed for fuel cell/battery plug-in hybrid vehicles with the goal of improving overall system operating efficiency. The main feature of the proposed strategy is that, if the total amount of energy required to complete a particular drive cycle can be reliably predicted, then the energy stored in the onboard electrical storage system can be depleted in an optimal manner that permits the fuel cell to operate in its most efficient regime. The strategy has been implemented in a vehicle power-train simulator called LFM which was developed in MATLAB/SIMULINK software and its effectiveness was evaluated by comparing it with a conventional control strategy. The proposed strategy is shown to provide significant improvement in average fuel cell system efficiency while reducing hydrogen consumption. It has been demonstrated with the LFM simulation that the prediction-based power management strategy can maintain a stable power request to the fuel cell thereby improving fuel cell durability, and that the battery is depleted to the desired state-of-charge at the end of the drive cycle. A sensitivity analysis has also been conducted to study the effects of inaccurate predictions of the remaining portion of the drive cycle on hydrogen consumption and the final battery state-of-charge. Finally, the advantages of the proposed control strategy over the conventional strategy have been validated through implementation in the University of Delaware's fuel cell hybrid bus with operational data acquired from onboard sensors.     

Architecture design and performance analysis of a hybrid hydrogen fuel cell system for unmanned aerial vehicle

The flight endurance of UAV systems is an important issue that restricts the operational capabilities. Thus, different energy systems and alternative onboard energy generation systems have been tested for the UAVs. Within these systems, fuel cells provide high energy density that can increase flight endurance greatly. In this study, a PEM fuel cell – Li-Po battery hybrid system has been developed by evaluating three architecture models. In the guide of the experimental power demand data of a fixed-wing UAV, modeling and testing procedures were performed. Battery voltage and fuel cell current variations observed during the ground tests were evaluated. It has been observed that approximately 160–170 W of the 250 W power is met by the fuel cell since no preconditioning has been applied and the temperature values at which the fuel cell exhibits its optimum performance. In the case where the fuel cell could provide 7.8 An under conditions where the humidification effects were not included in the model, the required current was over 7.8 A between approximately 400-1200 s. The fuel cell and battery behavior in response to the sudden power changes and to the uncertainties corresponding to the changes in the motor power during the flight are also detailed.     

Health-aware frequency separation method for online energy management of fuel cell hybrid vehicle considering efficient urban utilization

Frequency separation methods (FSMs) are frequently used to implement energy management of fuel cell hybrid vehicle (FCHV), due to their flexible online implementation and resilience under diverse driving environments. However, predefined static rules of FSM generally result in inefficient operation of FCHV and rapid deterioration of sources. Additionally, allocated limits of storage devices are likely to be violated in the conventional FSM. With this inspiration, the paper proposes a novel health-aware FSM (HFSM) to appropriately distribute the traction power among energy sources of FCHV with efficient urban utilization. The power separation rules of HFSM are tuned in an instantaneous manner to concurrently realize the fuel economy, lifespan extension and allocated storage limits. Within HFSM, an online optimizer is formulated, which introduces the concept of soft/hard limitations and rationalized cost structure to adequately quantify the fuel consumption and health degradation of fuel cell. An adaptive droop adjustment is then integrated with HFSM to consistently realize the storage limitations. Compared to conventional FSM, considerable improvements in the fuel economy and fuel cell service life are observed over an extended iterative loop of standard urban driving cycles.     

An online prognostics-based health management strategy for fuel cell hybrid electric vehicles

As the energy transformation in the transportation sector is taking place driven by the development of fuel cell technologies, fuel cell hybrid electric vehicles become promising solutions owing to their long driving duration and zero emissions. However, the unsatisfied lifespan of fuel cells is an inevitable obstacle for their massive commercialization. This paper aims to propose an online adaptive prognostics-based health management strategy for fuel cell hybrid electric vehicles, which can improve the durability of the fuel cell thanks to online health monitoring. Here, particle filtering method is adapted for online fuel cell prognostics and the uncertainty of the predicted results is calculated based on the distribution of particles. A health management strategy is developed based on prognostics and a decision-making process is designed by considering the prognostics uncertainty through a decision fusion method. The obtained results show that the developed strategy has effectively improved the durability of the on-board fuel cell by up to 95.4%. Moreover, a sensitivity analysis of the prognostics occurrence frequency and probability calculation has also been conducted in this paper.