Power management strategy for vehicular-applied hybrid fuel cell/battery power system

In this paper, a control strategy for a hybrid PEM (proton exchange membrane) fuel cell/BES (battery energy system) vehicular power system is presented. The strategy, based on fuzzy logic control, incorporates the slow dynamics of fuel cells and the state of charge (SOC) of the BES. Fuel cell output power was determined according to the driving load requirement and the SOC, using fuzzy dynamic decision-making and fuzzy self-organizing concepts. An analysis of the simulation results was conducted using Matlab/Simulink/Stateflow software in order to verify the effectiveness of the proposed control strategy. It was confirmed that the control scheme can be used to improve the operational efficiency of the hybrid power system.     

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.     

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.     

The control strategy for a molten carbonate fuel cell hybrid system

Based on mathematical modelling and numerical simulations, the control strategy for a molten carbonate fuel cell hybrid system (MCFC-HS) is presented. Adequate maps of performances with three independent parameters are shown. The independent parameters are as follows: stack current, fuel mass flow and compressor outlet pressure. Those parameters can be controlled by external load, fuel valve and turbine–compressor shaft speed, 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.     

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.     

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.     

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.     

Energy management of a fuel cell hybrid ultra-energy efficient vehicle

This paper focuses on energy management in an ultra-energy efficient vehicle powered by a hydrogen fuel cell with rated power of 1 kW. The vehicle is especially developed for the student competition Shell Eco-marathon in the Urban Concept category. In order to minimize the driving energy consumption a simulation model of the vehicle and the electric propulsion is developed. The model is based on vehicle dynamics and real motor efficiency as constant DC/DC, motor controllers and transmission efficiency were considered. Based on that model five propulsion schemes and driving strategies were evaluated. The fuel cell output parameters were experimentally determined. Then, the driving energy demand and hydrogen consumption was estimated for each of the propulsion schemes. Finally, an experimental study on fuel cell output power and hydrogen consumption was conducted for two propulsion schemes in case of hybrid and non-hybrid power source. In the hybrid propulsion scheme, supercapacitors were used as energy storage as they were charged from the fuel cell with constant current of 10 A.     

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.     

Simulation research on a novel control strategy for fuel cell extended-range vehicles

Adapting to urban transportation and emission reduction in China, fuel cell extended-range commercial vehicles are advocated and studied, which have the advantages of no pollution and long continued driving mileage. According to the features of fuel cell extender and characteristics of the powertrain system of the electric commercial vehicle, the design principle of the extender control strategy is determined in this paper, in order to improve the power and economic performance. A simulation platform for fuel cell plus electric vehicles was established. By comparing and analyzing the characteristics of on-off control strategy, power following control strategy and fuzzy logic control strategy, an on-off power following control strategy is put forward and built which is used for extender controller, and a fuzzy algorithm of following control strategy is studied. By Simulating and analyzing on the platform, the results show that the power following fuzzy algorithm can improve the power performance with the 8.9s accelerating time (0–50 km/h) and better total mileage continued 286.7 km for the powertrain system of fuel cell extended-range commercial vehicles. The research in this paper provides a basis for the in-depth study of the energy management of electric vehicles.     

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.     

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.     

Research on ADHDP energy management strategy for fuel cell hybrid power system

Fuel cell hybrid power system is a prospective power source for electrical vehicles. To reduce hydrogen consumption and enhance dynamic performance of the system, Action Dependent Heuristic Dynamic Programming (ADHDP) energy management strategy for the fuel cell hybrid power system was proposed. Firstly, topology of the system was analyzed and mathematical model was established through mechanism analysis. Secondly, framework of the ADHDP algorithm was presented, and it was followed by training algorithm for evaluating network and executing network of ADHDP based on Back Propagation (BP) algorithm. Finally, hardware-in-the-loop (HIL) simulation of the fuel cell hybrid power system was carried out to demonstrate the proposed ADHDP algorithm under real operating conditions. The results show that evaluating network and executing network of ADHDP have good convergence performance under different operating conditions. Compared with the other algorithms, the proposed ADHDP energy management strategy has better fuel economy and dynamic performance.     

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.     

Fuzzy control based engine sizing optimization for a fuel cell/battery hybrid mini-bus

The fuel cell/battery hybrid vehicle has been focused for the alternative engine of the existing internal-combustion engine due to the following advantages of the fuel cell and the battery. Firstly, the fuel cell is highly efficient and eco-friendly. Secondly, the battery has the fast response for the changeable power demand. However, the competitive efficiency of the hybrid fuel cell vehicle is necessary to successfully alternate the conventional vehicles with the fuel cell hybrid vehicle. The most relevant factor which affects the overall efficiency of the hybrid fuel cell vehicle is the relative engine sizing between the fuel cell and the battery. Therefore the design method to optimize the engine sizing of the fuel cell hybrid vehicle has been proposed. The target system is the fuel cell/battery hybrid mini-bus and its power distribution is controlled based on the fuzzy logic. The optimal engine sizes are determined based on the simulator developed in this paper. The simulator includes the several models for the fuel cell, the battery, and the major balance of plants. After the engine sizing, the system efficiency and the stability of the power distribution are verified based on the well-known driving schedule. Consequently, the optimally designed mini-bus shows good performance.     

Fuel cell/battery passive hybrid power source for electric powertrains

The concept of passive hybrid, i.e. the direct electrical coupling between a fuel cell system and a battery without using a power converter, is presented as a feasible solution for powertrain applications. As there are no DC/DC converters, the passive hybrid is a cheap and simple solution and the power losses in the electronic hardware are eliminated. In such a powertrain topology where the two devices always have the same voltage, the active power sharing between the two energy sources can not be done in the conventional way. As an alternative, control of the fuel cell power by adjusting its operating pressure is elaborated. Only pure H₂/O₂ fuel cell systems are considered in this approach. Simulation and hardware in the loop (HIL) results for the powertrain show that this hybrid power source is able to satisfy the power demand of an electric vehicle while sustaining the battery state of charge.     

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.     

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.     

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.