Fuel economy evaluation of fuel cell hybrid vehicles based on equivalent fuel consumption

Fuel cell hybrid vehicles (FCHVs) have become a major topic of interest in the automotive industry owing to recent energy supply and environmental problems. Consequently, fuel economy evaluation methods of FCHVs have a popular research topic. The initial state of charge (SOC) and the final SOC of the battery have to be identical in an evaluation of the fuel economy of an FCHV. In an actual driving situation or during a forward simulation, however, the final SOC depends on the power management strategy, which is usually different from the initial SOC. To consider the effect of the difference between the initial and final SOC on fuel economy evaluation, the concept of equivalent fuel consumption, based on the optimal control, is introduced in this paper. A rule-based power management strategy is applied to an FCHV, and its fuel economy is evaluated in terms of the equivalent fuel consumption and compared to the optimal control result.     

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.     

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.     

A hierarchical energy management strategy for fuel cell/battery/supercapacitor hybrid electric vehicles

In this paper, a hierarchical energy management strategy (EMS) based on low-pass filter and equivalent consumption minimization strategy (ECMS) is proposed in order to lift energy sources lifespan, power performance and fuel economy for hybrid electrical vehicles equipped with fuel cell, battery and supercapacitor. As for the considered powertrain configuration, fuel cell serves as main energy source, and battery and supercapacitor are regarded as energy support and storage system. Supercapacitor with high power density and dynamic response acts during great power fluctuations, which relives stress on fuel cell and battery. Meanwhile, battery is used to lift the economy of hydrogen fuel. In higher layer strategy of the proposed EMS, supercapacitor is employed to supply peak power and recycle braking energy by using the adaptive low-pass filter method. Meantime, an ECMS is designed to allocate power of fuel cell and battery such that fuel cell can work in a high efficient range to minimize hydrogen consumption in lower layer. The proposed EMS for hybrid electrical vehicles is modeled and verified by advisor-simulink and experiment bench. Simulation and experiment results are given to confirm effectiveness of the proposed EMS of this paper.     

Adaptive supervisory control strategy of a fuel cell/battery-powered city bus

This paper presents an adaptive supervisory control strategy for a fuel cell/battery-powered city bus to fulfill the complex road conditions in Beijing bus routes. An equivalent consumption minimization strategy (ECMS) is firstly proposed to optimize the fuel economy. The adaptive supervisory control strategy is exploited based on this, incorporating an estimating algorithm for the vehicle accessorial power, an algorithm for the battery charge-sustaining and a Recursive Least Squares (RLS) algorithm for fuel cell performance identification. Finally, an adaptive supervisory controller (ASC) considering the fuel consumption minimization, the battery charge-sustaining and the fuel cell durability has been implemented within the hybrid city buses. Results in the “China city bus typical cycle” testing and the demonstrational program of Beijing bus routes are presented, demonstrating that this approach provides an improvement of fuel economy along with robustness and ease of implementation. However, the fuel cell system does not leave much room for the optimal strategy to promote the fuel economy. Benefits may also result in a prolongation of the fuel cell working life, which needs to be verified in future.     

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.     

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.     

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.     

On the sizing and energy management of an hybrid multistack fuel cell – Battery system for automotive applications

This study aims at highlighting the impact of the sizing of a hybrid multi-stack fuel cell – battery system on its behavior. Using a rule based energy management strategy, the influence of the hybridization rating is studied. The system's fuel consumption, operation duration, start/stop cycles, ampere-hour throughput and state of charge deviation are evaluated using an Energetic Macroscopic Representation (EMR) based dynamic model. The simulation results predict and expected superiority in both fuel economy and system degradation for systems with a high hybridization ratings whereas the use of systems with low hybridization ratings results in a reduced battery depth of discharge.     

Optimal sizing of a fuel processor for auxiliary power applications of a fuel cell-powered passenger car

The present study considers the optimal sizing of a three-way hybrid powertrain consisting of a compact reformer, a compact battery and a low temperature PEM fuel cell stack serving as the main power unit. A simulation model consisting of the relevant characteristic parameters of the three power sources has been developed and has been used to study the fuel utilization features of the hybrid powertrain while going through the NEDC driving cycle with a given auxiliary power requirement. The optimality is based on minimizing fuel cost while having an assured range of 500 km under practical driving conditions and a further 100 km under reduced auxiliary power usage. It is shown that for performance characteristics of Toyota Mirai and for average auxiliary power consumption of 5 kW, a smaller NiMH battery size of 1.3 kWh together with a fuel processor of 5.6 kW constant output would be optimal with a further requirement of 25% more hydrogen and 33 kg of ethanol to be carried on-board. Substantial reductions in vehicle mass and fuel load can be achieved for more modest performance characteristics and auxiliary power consumption.     

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.     

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.     

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.     

Optimal control in the power management of fuel cell hybrid vehicles

Several types of power management strategies have been developed to improve the fuel economy of fuel cell hybrid vehicles (FCHVs). Optimal control based on the Minimum Principle provides the necessary optimality conditions which minimize fuel consumption and optimize the power distribution between power sources while the vehicle is being driven. In the optimal control scheme, the costate is an equivalent parameter between fuel usage and electric usage. The optimal trajectory of the costate can be derived from one of the necessary conditions. In this paper, an optimal control scheme based on the Minimum Principle is proposed for cases without a state constraint and for those with a state constraint. The conditions in which a variable costate can be replaced with a constant costate are presented. The simulation results with constant costates are compared to those with variable costates in order to prove that variable costates can be replaced with constant costates when using the proposed optimal control scheme.     

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.     

Optimization of powerplant component size on board a fuel cell/battery hybrid bus for fuel economy and system durability

The size of the individual powerplant components on board a fuel cell/battery hybrid vehicle affects the power management strategy which determines both the fuel economy and the durability of the fuel cell and the battery, and thus the average lifetime cost of the vehicle. Cost is one of the major barriers to the commercialization of fuel cell vehicles, therefore it is important to study how the sizing configuration affects overall vehicle cost. In this paper, degradation models for the fuel cell and the battery on board a fuel cell/battery hybrid bus are incorporated into the power management system to extend their lifetimes. Different sizing configurations were studied and the results reveal that the optimal size with highest lifetime and lowest average cost is highly dependent on the drive cycle. The vehicle equipped with a small fuel cell stack serving as a range extender will fail earlier and consume more fuel under drive cycles with high average power demand resulting in higher overall cost. However, the same configuration gives optimal results under a standard bus cycle with lower average power demand. At the other end of the spectrum, a fuel cell-dominant bus does not guarantee longer lifetime since the fuel cell operates mostly under low-load conditions which correspond to higher potentials reducing lifetime. Such a configuration also incurs a higher initial capital cost of the fuel cell stack resulting in a high average cost. The best configuration is a battery-dominated system with moderately-sized fuel cell stack which achieves the longest lifetime combined with the lowest average running cost throughout the lifetime of the vehicle.     

Comparison among various energy management strategies for reducing hydrogen consumption in a hybrid fuel cell/supercapacitor/battery system

The aim of this study is to introduce a comprehensive comparison of various energy management strategies of fuel cell/supercapacitor/battery storage systems. These strategies are utilized to manage the energy demand response of hybrid systems, in an optimal way, under highly fluctuating load condition. Two novel strategies based on salp swarm algorithm (SSA) and mine-blast optimization are proposed. The outcomes of these strategies are compared with commonly used strategies like fuzzy logic control, classical proportional integral control, the state machine, equivalent fuel consumption minimization, maximization, external energy maximization, and equivalent consumption minimization. Hydrogen fuel economy and overall efficiency are used for the comparison of these different strategies. Results demonstrate that the proposed SSA management strategy performed best compared with all other used strategies in terms of hydrogen fuel economy and overall efficiency. The minimum consumed hydrogen and maximum efficiency are found 19.4 gm and 85.61%, respectively.     

A real-time PMP energy management strategy for fuel cell hybrid buses based on driving segment feature recognition

Establishing a reasonable energy management strategy (EMS) is the key to improve the service durability, power performance and fuel economy of the fuel cell hybrid electric vehicle (FCHEV). This paper obtains energy distribution optimal solution for the fuel cell hybrid bus (FCHB) based on Pontryagin's minimum principle (PMP) algorithm, and the problems of inaccurate estimation of motor power and difficult real-time application are solved. Firstly, the driving feature recognition is completed by collecting the motor output power directly when the FCHB stops at the station. On the basis of it, the sub-optimal co-state value is chosen. Secondly, the sub-optimal co-state is used to complete the real-time application of PMP algorithm in the driving segment. The results are acquired through the simulation and the actual comparison experiment, compared with rule-based simulation and rule-based actual experiment, the hydrogen consumption of the proposed strategy decreases by 20.3% and 28.9% on average. Moreover, the online computation time per step of the proposed strategy is 3.64 ms averagely, less than sampling time interval 1s. It is shown that the proposed method has lower hydrogen consumption rate and excellent real-time performance.     

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.     

Experimental investigation on the online fuzzy energy management of hybrid fuel cell/battery power system for UAVs

The hybrid powerplant combining a fuel cell and a battery has become one of the most promising alternative power systems for electric unmanned aerial vehicles (UAVs). To enhance the fuel efficiency and battery service life, highly effective and robust online energy management strategies are needed in real applications.In this work, an energy management system is designed to control the hybrid fuel cell and battery power system for electric UAVs. To reduce the weight, only one programmable direct-current to direct-current (dcdc) converter is used as the critical power split component to implement the power management strategy. The output voltage and current of the dcdc is controlled by an independent energy management controller. An executable process of online fuzzy energy management strategy is proposed and established. According to the demand power and battery state of charge, the online fuzzy energy management strategy produces the current command for the dcdc to directly control the output current of the fuel cell and to indirectly control the charge/discharge current of the battery based on the power balance principle.Another two online strategies, the passive control strategy and the state machine strategy, are also employed to compare with the proposed online fuzzy strategy in terms of the battery management and fuel efficiency. To evaluate and compare the feasibility of the online energy management strategies in application, experiments with three types of missions are carried out using the hybrid power system test-bench, which consists of a commercial fuel cell EOS600, a Lipo battery, a programmable dcdc converter, an energy management controller, and an electric load. The experimental investigation shows that the proposed online fuzzy strategy prefers to use the most power from the battery and consumes the least amount of hydrogen fuel compared with the other two online energy management strategies.