Development of new energy management strategy for a household fuel cell/battery hybrid system

This work aims to construct an efficient and robust fuel cell/battery hybrid operating system for a household application. The ability to dispatch the power demands, sustain the state of charge (SOC) of battery, optimize the power consumption, and more importantly, ensure the durability as well as extend the lifetime of a fuel cell system is the basic requirements of the hybrid operating system. New power management strategy based on fuzzy logical combined state machine control is developed, and its effectiveness is compared with various strategies such as dynamic programming (DP), state machine control, and fuzzy logical control with simulation. Experimental results are also presented, except for DP because of difficulties in achieving real‐time implementation and much faster response to load variation. The given current from the energy management system (EMS) as a reference of the fuel cell output current is determined by filtering out various harmful signals. The new power management strategy is applied to a 1‐kW stationary fuel cell/battery hybrid system. Results show that the fuel cell hybrid system can run much smoothly with prolonged lifetime.     

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.     

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.     

Optimized rule-based energy management for a polymer electrolyte membrane fuel cell/battery hybrid power system using a genetic algorithm

The polymer electrolyte membrane fuel cell (PEMFC) coupled with the battery is a promising hybrid power system for future energy supply application. Fuel cell durability, battery charge sustenance, and fuel consumption strongly rely on the energy management strategy (EMS). This paper puts forward an optimized rule-based EMS using genetic algorithm (GA) to optimally allocate the power between the fuel cell and the battery system. Control variables in real-time rule-based EMS are optimally adjusted with single objective of battery charge sustenance considering the fuel cell durability and efficiency. The proposed optimized rule-based EMS is simulated and experimentally verified via MATLAB/Simulink and LabVIEW-based experimental rig, respectively. The conventional rule-based EMS, fuzzy logic EMS, and dynamic programming (DP) EMS are also examined for comparison. The comparison results elucidate that the optimized rule-based EMS realizes a large performance improvement over the conventional rule-based and fuzzy logic EMSs. Near optimal performance is verified compared with DP EMS in terms of fuel economy, battery charge sustenance, fuel cell efficiency, and system durability. The combination of rule-based EMS and GA optimization algorithm has the advantage of having expert experience and global optimization properties, realizing optimal power allocation in real-time application with lower computation burden, which could be applied easily to other EMS system without loss of validity.     

Power management system for a fuel cell/battery hybrid vehicle incorporating fuel cell and battery degradation

Optimization of fuel cell/battery hybrid vehicle systems has primarily focused on reducing fuel consumption. However, it is also necessary to focus on fuel cell and battery durability as inadequate lifespan is still a major barrier to the commercialization of fuel cell vehicles. Here, we introduce a power management strategy which concurrently accounts for fuel consumption as well as fuel cell and battery degradation. Fuel cell degradation is quantified using a simplified electrochemical model which provides an analytical solution for the decay of the electrochemical surface area (ECSA) in the fuel cell by accounting for the performance loss due to transient power load, start/stop cycles, idling and high power load. The results show that the performance loss based on remaining ECSA matches well with test data in the literature. A validated empirical model is used to relate Lithium-ion battery capacity decay to C-rate. Simulations are then conducted using a typical bus drive cycle to optimize the fuel cell/battery hybrid system. We demonstrate that including these degradation models in the objective function can effectively extend the lifetime of the fuel cell at the expense of higher battery capacity decay resulting in a lower average running cost over the lifetime of the vehicle.     

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.     

Optimized power management based on adaptive-PMP algorithm for a stationary PEM fuel cell/battery hybrid system

This research develops an efficient and robust polymer electrolyte membrane (PEM) fuel cell/battery hybrid operating system. The entire system possesses its own rapid dynamic response benefited from hybrid connection and power split characteristics due to DC/DC buck-boost converter. An indispensable energy management system (EMS) plays a significant role in achieving optimal fuel economy and in a promising running stability. EMS as an indispensable part plays a significant role in achieving optimal fuel economy and promising operation stability. This study aims to develop an adaptive supervisory EMS that comprises computer-aided engineering tools to monitor, control, and optimize the performance of the hybrid power system. A stationary fuel cell/battery hybrid operating system is optimized using adaptive-Pontryagin's minimum principle (A-PMP). The proposed algorithm depends on the adaptation of the control parameter (i.e., fuel cell output power) from the state of charge (SOC) and load power feedback. The integrated model simulated in a Matlab/Simulink environment includes the fuel cell, battery, DC/DC converter, and power requirements models by analyzing the three different load profiles. Real-time experiments are performed to verify the effectiveness of EMS after analyzing the simulated operating principle and control scheme.     

Review on health-conscious energy management strategies for fuel cell hybrid electric vehicles: Degradation models and strategies

Considering the overwhelming pressure on worldwide demand of fossil fuels and the climate change caused by air pollution, hybrid electric vehicles have seen a promising future thanks to the development of renewable energy sources. Among various kinds of energy sources that have been used in hybrid electric vehicles, lithium-ion battery and proton exchange membrane (PEM) fuel cell exist to be the most favorable ones owing to their high energy density and power density. However, the degradation issues of the energy sources tend to be neglected when designing the energy management strategies for the hybrid electric vehicles. Concerning existing literature, degradation modelling methods of lithium-ion batteries and PEM fuel cells are reviewed and the possibility of integrating them into health-conscious energy management is discussed. Besides, a variety of energy management strategies that have taken the influence of degradations into consideration are reviewed and classified. The contribution of this paper is to investigate the possibility of developing a health-conscious energy management strategy based on accurate estimation of degradation to improve the durability of the system.     

Improved fuzzy logic control-based energy management strategy for hybrid power system of FC/PV/battery/SC on tourist ship

An improved fuzzy-based energy management strategy (EMS) is proposed for a tourist ship used hybrid power system with multiple power sources consisting of fuel cell(FC)/photovoltaic cell(PV)/battery(BAT)/super-capacitor(SC). The power demand from propeller and user terminal is afforded by the power sources connecting to power converters. To obtain more superior performance of the power system, the maximum power point tracking (MPPT) algorithm is employed to optimize the PV. Meanwhile, the improved fuzzy logic control based on dynamic programming (DP) associated with wavelet analysis and PI control are employed to achieve the output power optimal distribution and online control. In particular, the MPPT algorithm can improve the utilization of solar energy, and the SC can well absorb the high frequency power and reduce the fluctuation of the battery and FC that exhibits the potential of their lifetime extension. The FC outputs the high and stable power satisfying the ship's power demand even under the extreme work conditions. The developed model is able to illustrate well in the operation process of the hybrid power system governed by the proposed EMS. In addition, compared with the rule-based strategy, the improved fuzzy-based EMS can reduce 14.39% hydrogen consumption and keep the consistency of battery SOC.     

Fuzzy neural control of a hybrid fuel cell/battery distributed power generation system

An innovative control strategy is proposed of hybrid distributed generation (HDG) systems, including solid oxide fuel cell (SOFC) as the main energy source and battery energy storage as the auxiliary power source. The overall configuration of the HDG system is given, and dynamic models for the SOFC power plant, battery bank and its power electronic interfacing are briefly described, and controller design methodologies for the power conditioning units and fuel cell to control the power flow from the hybrid power plant to the utility grid are presented. To distribute the power between power sources, the fuzzy switching controller has been developed. Then, a Lyapunov based-neuro fuzzy algorithm is presented for designing the controllers of fuel cell power plant, DC/DC and DC/AC converters; to regulate the input fuel flow and meet a desirable output power demand. Simulation results are given to show the overall system performance including load-following and power management of the system.     

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.     

基于价值损耗的燃料电池混合动力能量管理策略评价

为了评价燃料电池混合动力系统能量管理策略的经济性,对基于状态机和模糊逻辑2种能量管理策略的燃料电池混合动力叉车的价值损耗进行分析。首先,通过分析燃料电池和锂电池的工作特性,分别构建依赖实际工况的燃料电池单体电压衰减率模型和锂电池容量衰减率模型;同时定义计及燃料电池氢耗量的燃料电池混合动力系统的综合价值损耗指标。其次,通过测试叉车极限工况,计算燃料电池功率和锂电池容量,并根据母线电压确定锂电池SOC范围。最后,设计基于状态机和模糊逻辑的2种燃料电池混合动力叉车能量管理策略,并通过仿真分析在叉车一次循环工况下2种能量管理的价值损耗。研究结果表明:相较于模糊逻辑策略,采用状态机策略造成燃料电池寿命损耗提高7.81%,氢耗量提高1.89倍,锂电池寿命损耗减小21.33%。     

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.     

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.     

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.     

Fuel cell system with sodium borohydride as hydrogen source for unmanned aerial vehicles

In this study, we design and fabricate a fuel cell system for application as a power source in unmanned aerial vehicles (UAVs). The fuel cell system consists of a fuel cell stack, hydrogen generator, and hybrid power management system. PEMFC stack with an output power of 100 W is prepared and tested to decide the efficient operating conditions; the stack must be operated in the dead-end mode with purge in order to ensure prolonged stack performance. A hydrogen generator is fabricated to supply gaseous hydrogen to the stack. Sodium borohydride (NaBH₄) is used as the hydrogen source in the present study. Co/Al₂O₃ catalyst is prepared for the hydrolysis of the alkaline NaBH₄ solution at room temperature. The fabricated Co catalyst is comparable to the Ru catalyst. The UAV consumes more power in the takeoff mode than in the cruising mode. A hybrid power management system using an auxiliary battery is developed and evaluated for efficient energy management. Hybrid power from both the fuel cell and battery powers takeoff and turning flight operations, while the fuel cell supplies steady power during the cruising flight. The capabilities of the fuel-cell UAVs for long endurance flights are validated by successful flight tests.     

Two-level energy management strategy for PV-Fuel cell-battery-based DC microgrid

With the fast development of DC Microgrid (MG) technology, its operating economy and reliability are getting more and more concern. The traditional distributed control method is aimed at power balance and system stability, and is difficult to meet the requirement of energy management system for multi-source hybrid DC MG. This paper provides a two-level energy management strategy for PV-fuel cell-battery-based DC MG, which is divided into device control level and system control level. At the device control level, the distributed control methods based on MPPT-droop dual-mode control and droop control are proposed to enhance system reliability; at the system control level, the equivalent consumption minimization strategy (ECMS) is used to distribute system net power between battery pack and fuel cell system. A lab-scale DC microgrid platform is developed to verify the proposed energy management strategy in this paper. Moreover, the analysis and compare of the results show that the proposed two-level energy management strategy can achieve lower equivalent hydrogen consumption than classical PI control and state machine control method.     

Hierarchical energy management for PV/hydrogen/battery island DC microgrid

This research presents an optimum design scheme and a hierarchical energy management strategy for an island PV/hydrogen/battery hybrid DC microgrid (MG). In order to efficiently utilize this DC MG, the optimum structure and sizing scheme are designed by HOMER pro (Hybrid Optimization of Multiple Energy Resources) software. The designed structure of hydrogen MG includes a PV generation, a battery as well as a hydrogen subsystem which composes a fuel cell (FC) system, an electrolyzer and hydrogen tank. To improve the robustness and economy of this DC MG, this study schedules a hierarchical energy management method, including the local control layer and the system control layer. In the local control layer, the subsystems in this DC MG are controlled based on their inherent operating characteristics. And the equivalent consumption minimization strategy (ECMS) is applied in the system control layer, the power flow between the battery and FC is allocated to minimum the fuel consumption. An island DC MG hardware-in-loop (HIL) Simulink platform is established by RT-LAB real-time simulator, and the simulation results are presented to validate the proposed energy management strategy.     

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.     

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.