Viability study of a FC-battery-SC tramway controlled by equivalent consumption minimization strategy

This paper evaluates the option of using a new powertrain based on fuel cell (FC), battery and supercapacitor (SC) for the Urbos 3 tramway in Zaragoza, Spain. In the proposed powertrain configuration, a hydrogen Proton-Exchange-Membrane (PEM) FC acts as main energy source, and a Li-ion battery and a SC as energy support and storage systems. The battery supports the FC during the starting and accelerations, and furthermore, it absorbs the power generated during the regenerative braking. Otherwise, the SC, which presents the fastest dynamic response, acts mainly during power peaks, which are beyond the operating range of the FC and battery. The FC, battery and SC use a DC/DC converter to connect each energy source to the DC bus and to control the energy exchange. This configuration would allow the tramway to operate in an autonomous way without grid connection. The components of the hybrid tramway, selected from commercially available devices have been modeled in MATLAB-Simulink. The energy management system used for controlling the components of the new hybrid system allows optimizing the fuel consumption (hydrogen) by applying an equivalent consumption minimization strategy. This control system is evaluated by simulations for the real driving cycle of the tramway. The results show that the proposed control system is valid for its application to this hybrid system.     

Regenerative braking energy recovery strategy based on Pontryagin's minimum principle for fell cell/supercapacitor hybrid locomotive

A regenerative braking energy recovery strategy based on pontryagin's minimum principle (PMP) for Fuel Cell (FC)/Supercapacitor (SC) hybrid power locomotive was proposed in this paper. In the proposed strategy, the dynamic coefficient λ is used in PMP during the traction state of the locomotive, which makes system transient hydrogen consumption minimum. What's more, during locomotives brake state, according to the known parameters of SCs and operation indicators, an optimized braking speed curve can also be obtained which has maximum brake recovery rate. The results are obtained from RT-LAB platform testify that the proposed strategy is able to maximize SC absorption braking energy, and the energy recovery rate improves and maintains SC state of charge (SoC) in a reasonable and safe range, and decreases brake resistors energy consumption in the braking process.     

Parameter sizing and stability analysis of a highway fuel cell electric bus power system using a multi-objective optimization approach

Although FC based electric buses are currently popular on urban streets or in short transit routes within large facilities, the version that is designed to operate on a highway, which has much higher dynamic requirements, is yet to be well developed. This research proposes to adopt the NSGA-II based multi-objective optimization scheme to optimize a fuel cell-battery-supercapacitor (SC) based FC power system (FCPS) that is specifically for a FC electric bus operating on the highway fuel economy cycle (HWFET). The optimization objectives are to minimize the FC's fuel consumption, the required battery and SC size and the battery degradation rate. More importantly, the optimization scheme is based on a combined energy management strategy (EMS) software parameter and hardware component sizing approach which is important for guaranteeing dynamically stable responses. This characteristic is achieved by imposing constraints that limit the transient time responses the DC-Bus capacitor voltage electrical parameters upon a generic step change in load power. Results demonstrate that dynamic stability can be guaranteed with proper software parameter and hardware components combinations without any trade-off requirements with the optimizer objectives. Moreover, the system mass and the battery degradation objectives are in trade-off but don't have any dependence to hydrogen consumption.     

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.     

Enhancing the operation of fuel cell-photovoltaic-battery-supercapacitor renewable system through a hybrid energy management strategy

It is necessary to have an energy management system based on one or more control strategies to sense, monitor, and control the behavior of the hybrid energy sources. In renewable hybrid power systems containing fuel cells and batteries, the hydrogen consumption reduction and battery state of charge (SOC) utilizing are the main objectives. These parameters are essential to get the maximum befits of cost reduction as well as battery and hydrogen storage lifetime increasing. In this paper, a novel hybrid energy management system (HEMS) was designed to achieve these objectives. A renewable hybrid power system combines: PV, PEMFC, SC, and Battery was designed to supply a predetermined load with its needed power. This (REHPS) depends on the PV power as a master source during the daylight. It uses the FC to support as a secondary source in the night or shading time. The battery is helping the FC when the load power is high. The supercapacitor (SC) is working at the load transient or load fast change. The proposed energy management system uses fuzzy logic and frequency decoupling and state machine control strategies working together as a hybrid strategy where the switching over between both strategies done automatically based on predetermined values to obtain the minimum value of hydrogen consumption and the maximum value of SOC at the same time. The proposed HEMS achieves 19.6% Hydrogen consumption saving and 5.4% increase in SOC value compared to the results of the same two strategies when working as a stand-alone. The load is designed to show a surplus power when the PV power is at its maximum value. This surplus power is used to charge the battery. To validate the system, the results were compared with the results of each strategy if working separately. The comparison confirms the achievement of the hybrid energy management system goal.     

Double-layer fuzzy adaptive NMPC coordinated control method of energy management and trajectory tracking for hybrid electric fixed wing UAVs

The energy management and trajectory tracking control are crucial to realize long-endurance autonomous flight for hybrid electric UAVs. This study aims to comprehensively consider energy management and trajectory tracking for hybrid electric fixed wing UAVs with photovoltaic panel/fuel cell/battery. A double-layer fuzzy adaptive nonlinear model predictive control method (DFNMPC) is proposed. Separated by the surplus demand power, energy management and trajectory tracking problem are decoupled into the high-layer fuzzy adaptive nonlinear model predictive controll problem (H-FNMPC) and low-layer fuzzy adaptive nonlinear model predictive controll problem (L-FNMPC). H-FNMPC solves the trajectory tracking and navigation control probelm for the greatest benefit of solar energy. L-FNMPC solves the power allocation problem of hybrid energy system for minimum equivalent hydrogen consumption. A fuzzy adaptive prediction horizon adjustment method based on UAV maneuvering degree is proposed to effectively improve proposed method adaptability to different mission profiles. Analogously, a fuzzy adaptive equivalent hydrogen consumption factor adjustment method in L-FNMPC is proposed to ensure the flexible utilization of battery. In addition, an equivalent hydrogen flow rate calculation method based on the real-time current ratio is proposed for PV/FC/Battery hybrid energy system. Numerical simulation results including a spiral trajectory tracking and a quadrilateral trajectory tracking, demonstrate that DFNMPC can simultaneously handle energy management and trajectory tracking problem for hybrid electric UAVs. Compared to hierarchical fuzzy state machine strategy, DFNMPC can save 13.3% hydrogen for the spiral trajectory tracking, and 56.9% for the quadrilateral trajectory tracking. It indicates that the energy efficiency can be improved from both levels of energy management and flight motion. The proposed method prospected for exploring high-energy-efficiency autonomous flight of hybrid electric UAVs in the future.     

Experimental study of energy management of FC/SC hybrid system using the Passivity Based Control

Nowadays, the energy management of the hybrid system is becoming an interesting and the challenging topic for several researchers. The wise choice of the energy management strategy allows not only the best distribution of power between different sources but also reduce system consumption, increase the lifetime of the used sources and ensure the energy demand that involve the autonomy of the electrical vehicle. In this paper, the control and the energy management using the passivity control is adopted to the multiconverters multisources system, in particularly, Fuel Cell/SuperCapacitor (FC/SC) hybrid system. In the proposed system, the FC represents the main source and the SC is used for the transient of power where they can absorb or supply powers peaks. The proposed system is validated by experimental results. The obtained results prove the efficacy and the feasibility of the proposed approach for a real electrical vehicle.     

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.     

Automotive fuel cell stack and system efficiency and fuel consumption based on vehicle testing on a chassis dynamometer at minus 18 °C to positive 35 °C temperatures

This paper presents an in-depth laboratory technology assessment of a 2016 Toyota Mirai Fuel Cell (FC) vehicle based on chassis dynamometer testing. The 114.6 kW FC stack has a high dynamic response, which makes this powertrain a FC-dominant hybrid electric vehicle. The measured peak efficiency is 66.0% FC stack and 63.7% FC system with an idle hydrogen flow rate of 4.39 g/hr. The high FC system efficiencies at low loads match typical vehicle power spectrums, resulting in a high average vehicle efficiency of 62% compared to 45% and 23% for a hybrid electric vehicle and a conventional vehicle, respectively. An energy breakdown accounts for the FC stack losses, FC system losses, air compressor loads, and heater loads for different drive cycles and different thermal conditions. The cold-start North American city drive cycle (UDDS) energy consumption values are, respectively, 758, 581, 226, and 321 Wh/km at ambient conditions of −18 °C, −7 °C, 25 °C and 35 °C with 850 W/m² of solar loading. The FC system shutdown and startup processes at temperatures below the freezing point contribute to the increased hydrogen consumption. The raw test data files are available for download, thus providing the research community with a public reference data on a modern production automotive FC system.     

An energy matching method for battery electric vehicle and hydrogen fuel cell vehicle based on source energy consumption rate

The smart cities development requires reducing energy consumption and using as much renewable energy as possible, so the widespread use of new energy vehicles is a very important measure. In this work, for the energy system configuration and energy efficiency balance of new energy vehicles, we propose an energy matching method to study its energy efficiency from the view point for energy life cycle. Nowadays, new energy vehicles mainly include battery electric vehicles (BEV) and hydrogen fuel cell vehicles (HFCEV). Firstly, we proposed the Source to Range (STR) model. Then, based on STR model, we used energy efficiency analysis chart to visually represent the conversion, delivery and consumption of the vehicle energy life cycle. Furthermore, we proposed a Source Energy Consumption Rate (SECR), which is used to evaluate the vehicles energy efficiency. Finally, based on STR model, we obtained the dividing line of the same SECR for new energy vehicles and equivalent fuel vehicles, which provides constraints on the vehicle energy system design. The results show that STR model can provide an effective tool for energy matching and energy efficiency analysis of new energy vehicles, and has a reference for product development of new energy vehicles.     

Application of alternating direction method of multipliers algorithm in energy management of fuel cell vehicles

In this paper, a method based on alternating direction method of multipliers (ADMM) algorithm is proposed to solve the energy management problem for fuel cell vehicles (FCVs). Taking the minimum hydrogen consumption as the objective function, a new fuel cell dynamical system and a whole vehicle model that conform to the property of convex function are constructed, and an augmented Lagrangian equation with its scaled dual form is established, which satisfies the standard normal form of ADMM algorithm. For the first time, the ADMM algorithm is applied to energy management system of FCVs. In the meantime, a cyclic constraint inspection strategy is introduced to have a further precise control of the power of the fuel cell system so that it can meet the dynamic change constraints of the power. Compared with dynamic programming (DP) algorithm off-line calculation results, the simulation results of the proposed method indicate that the calculation of this algorithm is approximately 20–200 times faster than the former in 9 standard driving cycles. And under the two energy management modes by power maintenance and consumption, the deviation results in terms of the hydrogen consumption derived from the proposed method and DP were 2.37% and 1.06% respectively.     

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.     

宝钢合同能源管理的应用潜力与实践

宝钢作为大型高耗能国有企业,肩负着节能减排的重任。自建厂30多年来,通过技改、科研、维修工程等多种渠道在节能减排方面取得了显著成效。合同能源管理这一新的节能机制,作为公司现有节能项目实施渠道的扩展,为节能减排工作注入新的活力与潜力。文章在概述宝钢合同能源管理工作基础上,重点对宝钢开展过程中遇到的问题进行分析总结,并对其它钢铁企业合同能源管理工作提出建议。     

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.     

An energy management strategy based on dynamic power factor for fuel cell/battery hybrid locomotive

In order to efficiently absorb more regenerative braking energy which sustains much longer compared with the conventional vehicle, and guarantee the safety of the hybrid system under the actual driving cycle of locomotive, an energy management control based on dynamic factor strategy is proposed for a scale-down locomotive system which consists of proton exchange membrane fuel cell (PEMFC) and battery pack. The proposed strategy which has self-adaption function for different driving cycles aims to achieve the less consumption of hydrogen and higher efficiency of the hybrid system. The experimental results demonstrate that the proposed strategy is able to maintain the charge state of battery (SOC) better than Equivalent Consumption Minimization Strategy (ECMS), and the proposed strategy could keep the change trend of SOC, which the final SOC is closed to the target value regardless of the initial SOC of battery. Moreover, the hydrogen consumption has been reduced by 0.86g and the efficiency of overall system has been raised of 2% at least than ECMS under the actual driving cycle through the proposed strategy. Therefore, the proposed strategy could improve the efficiency of system by diminishing the conversion process of energy outputted by fuel cell.     

Hybrid energy management with respect to a hydrogen energy system and demand response

A Hybrid Energy System (HES) is a mechanism that combines multiple sources of energy connected together to achieve synchronised energy output. However, increased energy consumption, operator energy expenses, and the potential environmental impact of increased emissions from the exhaustion of non-renewable energy resources (fossil fuel) pose major challenges to HES. This research is to conduct energy management strategy based on a demand response (DR) program and a hydrogen storage system by designing a Program Logic Controller (PLC) unit. The hybrid system is evaluated by comparing different scenarios such as a hydrogen energy system and demand response. The purpose of this research is to reducing peak demand, minimise the cost of the system and also to extract surplus power generation out of the rate of the battery. This can be achieved by improving the system performances and by eliminating any degradation at the early stages. Organisations or companies must be sure their systems are working properly and that their investments will pay off.     

Fuel cell/supercapacitor passive configuration sizing approach for vehicular applications

Active configuration i.e., source coupling via a power converter, is the most common configuration for fuel cell/supercapacitor (FC/SC) vehicles. Passive connection of the FC with the SCs without any converters is an original and less expensive solution to distribute the power among the sources. This passive configuration does not require an energy management strategy. In fact, the power distribution only depends on the FC and SC impedance characteristics. Conventional methods to size the SC follow two criteria: storage capacity and maximum voltage. In this paper, a third criterion is added which is the FC operating current dynamics. This novel sizing methodology reduces the FC degradation and improves the global system efficiency. Experimental results provide validation to the proposed sizing approach. The SCs boost the FC to meet the requirements of the load with a guarantee of system stability reaching higher global performances and less stress to the FC.     

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 new method for dynamic performance improvement of a hybrid power system by coordination of converter's controller

In this paper, a new strategy for modeling and controlling a hybrid power generation system that contains a fuel cell (FC) and super capacitor (SC) system is proposed. The main drawback of FC systems is its slow dynamic because the FC current slope must be limited in order to prevent fuel starvation problems and to improve its efficiency and lifetime. To overcome this slow dynamic and to improve dynamic performance, a new control strategy is proposed to combine FC system with SC system. The proposed control strategy can be also used for cold starting and different types of FC systems with different dynamics. The control strategy is capable of determining the desired FC power to prolong FC system lifetime and keeps the AC and DC voltages around its nominal value in transient event by supplying propulsion power and recuperating FC energy. The minimum SC system is computed in new method and used to meet the load demand to constraint the DC bus voltage and enhances power regulation under various active and reactive load conditions. Two different case studies are used to obtain the simulation results using MATLAB/SIMULINK to verify the validity of the proposed control strategy.     

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.