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.     

Energy management of fuel cell/battery/ultracapacitor in electrical hybrid vehicle

This paper describes an energy management algorithm for an electrical hybrid vehicle. The proposed hybrid vehicle presents a fuel cell as the main energy source and the storage system, composed of a battery and a supercapacitor as the secondary energy source. The main source must produce the necessary energy to the electrical vehicle. The secondary energy source produces the lacking power in acceleration and absorbs excess power in braking operation. The addition of a supercapacitor and battery in fuel cell-based vehicles has a great potential because it allows a significant reduction of the hydrogen consumption and an improvement of the vehicle efficiency. Other the energy sources, the electrical vehicle composed of a traction motor drive, Inverter and power conditioning. The last is composed of three DC/DC converters: the first converter interfaces the fuel cell and the DC link. For the second and the third converter, two buck boost are used in order to interface respectively the ultracapacitor and the battery with the DC link. The energy management algorithm determines the currents of the converters in order to regulate accurately the power provided from the three electrical sources. This algorithm is simulated with MATLAB_Simulink and implemented experimentally with a real-time system controller based on dSPACE. In this paper, the proposed algorithm is evaluated for the New European Driving Cycle (NEDC). The experimental results validate the effectiveness of the proposed energy management algorithm.     

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.     

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.     

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.     

Proton exchange membrane fuel cell system diagnosis based on the multivariate statistical method

Although electric-powered vehicles have developed rapidly in recent years, with significant progress in the lithium power battery industry, the Fuel Cell Electric Vehicle (FCEV) is still a competitive choice for a clean transportation solution, because of its extended driving range, zero emissions, and fast fuel recharging capability. In particular the fuel cell hybrid bus used for city traffic is the FCEV type most likely to be commercialized. Demonstration programs for a fuel cell bus fleet have been operated for a few years in China. It is necessary to develop comprehensive diagnostic tools to increase the reliability of these systems, because fuel cell city buses serve large numbers of passengers using public transportation. This paper presents a diagnostic analysis and implementation study based on the Principal Component Analysis (PCA) method for the fuel cell system. This diagnostic system was successfully implemented for detecting a fuel cell stack sensor network failure in the fuel cell bus fleet at the Shanghai Expo in 2010.     

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.     

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.     

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.     

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.     

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.     

Active fault tolerance control system of fuel cell hybrid city bus

With ever growing concerns on energy crises and environmental issues, Proton Exchange Membrane Fuel Cell is favored in automotive applications because it is clean, efficient and low noise. A fuel cell hybrid powertrain is composed of a fuel cell system as the primary power source, a battery as the secondary power source and an electric motor. In order to improve the reliability, a distributed control system that can be preserved even under faulty cases is necessary. This paper presents an active fault tolerance control system (AFTCS) for a fuel cell/battery hybrid powertrain applied to a city bus. The AFTCS consists of a system for fault detection and diagnose and a reconfigurable controller. Algorithms to detect and isolated three kinds of important faults are introduced. The real-time applicable reconfigurable controller is exploited to recover the pre-fault system performance as much as possible. Experimental results show the effectiveness of the proposed system.     

Power management and nonlinear control of a fuel cell–supercapacitor hybrid automotive vehicle with working condition algorithm

This paper deals with the problem of controlling a multi-source system applied in hybrid electrical vehicles. The system consists of a proton exchange membrane fuel cell (PEMFC) and a super capacitor (SC). Fuel cell (FC) provides energy for load as a main power source, and SC helps the system in a load peak or in fast transients. The system is modeled as Port controlled Hamiltonian (PCH), and interconnection and damping assignment passivity based controller (IDA-PBC) is used for a typical hybrid vehicle. The aim is first to support the load power in all circumstances without interruption by combination of FC and SC production, and second to control the DC bus voltage. The purposed system analyzed under standard driving cycle consists of off-load, over-load, and charging conditions of SC. Simulations are accomplished in MATLAB/Simulink software for validation of control strategy and new represented algorithm. The results illustrate that both control method and algorithm can manage power among PEMFC, SC, and the load whereas the DC bus voltage remains near its reference.     

Experimental assessment of fuel cell/supercapacitor hybrid system for scooters

This paper presents an experimental assessment of fuel cell hybrid propulsion systems for scooters based on a modular 1.2 kW PEM fuel cell. The tests of the hybrid system are carried out using a programmable electronic load. Different configurations of the fuel cell/battery and the fuel cell/supercapacitor hybrid systems are explored. Both systems demonstrate their ability to deliver the requested load satisfactorily. The distributions of the fuel cell power delivery, although different between the two systems, are within the region where the fuel cell efficiency is approximately constant. As a result, the rates of fuel consumption show no discernable difference between the two systems for all three driving cycles considered. In addition to the fuel consumption, considerations including bus voltage, cost and packaging issues suggest that the supercapacitor has advantages over the battery for the use as secondary energy storage in fuel cell hybrid propulsion system for scooters.     

Implementation of self-adaptive Harris Hawks Optimization-based energy management scheme of fuel cell-based electric power system

This paper presents a hybrid Fuel Cell-based Power System (FCPS) consisting of fuel cell and hybrid Energy Storage Systems (ESSs), including a battery with high energy density and supercapacitor with high power density to overcome the sudden load demand change and improving the reliability of the delivered power. Any hybrid power system needs Energy Management Strategies (EMS) to balance the power between the different energy sources. In this paper, a comparative analysis of three energy management strategies, including the state machine control method, the classical PI control method and equivalent consumption minimization strategy (ECMS) is performed. The paper's main objective is enhancing the DC-bus voltage profile of a hybrid fuel cell/battery/supercapacitor power system equipped with the developed under-mentioned EMS by using a hybrid modified optimization technique that combines Harris Hawks optimization (HHO) and Sine Cosine Algorithm (SCA). The new hybrid HHO-SCA is employed to determine the optimal control parameters of the DC-bus voltage controller, which significantly assists in enhancing the DC-bus voltage profile as well as the performance of the applicable ESS in terms of improving efficiency and SoC. The effectiveness of the suggested control schemes is simulated using MATLAB/SIMULINK software. The simulation results confirmed that the proposed HHO-SCA is superior and efficient in improving the DC-bus voltage.     

Load sharing using fuzzy logic control in a fuel cell/ultracapacitor hybrid vehicle

Fuel cell (FC) and ultracapacitor (UC) based hybrid power systems appear to be very promising for satisfying high energy and high power requirements of vehicular applications. The improvement in control strategies enhances dynamic response of the FC/UC hybrid vehicular power system under various load conditions. In this study, FC system and UC bank supply power demand using a current-fed full bridge dc–dc converter and a bidirectional dc–dc converter, respectively. We focus on a novel fuzzy logic control algorithm integrated into the power conditioning unit (PCU) for the hybrid system. The control strategy is capable of determining the desired FC power and keeps the dc voltage around its nominal value by supplying propulsion power and recuperating braking energy. Simulation results obtained using MATLAB^® & Simulink^® and ADVISOR^® are presented to verify the effectiveness of the proposed control algorithm.     

Energy management of a fuel cell/ultracapacitor hybrid power system using an adaptive optimal-control method

Energy management of a fuel cell/ultracapacitor hybrid power system aims to optimize energy efficiency while satisfying the operational constraints. The current challenges include ensuring that the non-linear dynamics and energy management of a hybrid power system are consistent with state and input constraints imposed by operational limitations. This paper formulates the requirements for energy management of the hybrid power system as a constrained optimal-control problem, and then transforms the problem into an unconstrained form using the penalty-function method. Radial-basis-function networks are organized in an adaptive optimal-control algorithm to synthesize an optimal strategy for energy management. The obtained optimal strategy was verified in an electric vehicle powered by combining a fuel-cell system and an ultracapacitor bank. Driving-cycle tests were conducted to investigate the fuel consumption, fuel-cell peak power, and instantaneous rate of change in fuel-cell power. The results show that the energy efficiency of the electric vehicle is significantly improved relative to that without using the optimal strategy.     

Control of a fuel cell based on the SIRMs fuzzy inference model

A typical working mode for the fuel cell hybrid system is that the fuel cell produces constant power output while the auxiliary storage energy device such as ultracapacitor or battery provides the deviation between the desired power demand and the value of the actual operation. This paper concentrates on the control of the fuel cell flow system. The system which is like an under-actuated mechanical system needs to control two objects (the cathode pressure and the air flow) with one manipulated variable (the set voltage of the air mass flow controller). A SIRMs-based fuzzy inference model is successfully implemented in the system. Online random search optimization algorithm based on Simulink C-S functions is developed to adjust the parameters in the model. By alternative control of the two objects, experimental results demonstrate the realization of the control strategy with one adjustment means.     

Regenerative braking for fuel cell hybrid system with additional generator

The fuel cell hybrid system for automobiles consists of a fuel cell/battery or fuel cell/super-capacitor. The motor in the regenerative braking system revives electrical energy instead of dissipating heat during braking. In this study, an additional generator in a fuel cell/battery hybrid system is equipped and tested as an alternative to using a motor for regenerative braking. The fuel cell hybrid system uses the Nexa™ Power Module from Ballard Power Systems Inc. and a Ni-MH battery from Global Battery Co., Ltd. In the hybrid system, the battery's voltage undershoots, while the fuel cell's voltage does not undershoot. In this study, the fuel cell hybrid system is affected by the load share rates due to the SoC of the battery. Therefore, the SoC of the battery needs to be managed. Also, the dynamic performance of the fuel cell is more stable when comprising the hybrid system. In addition, the efficiency of regenerative braking by using the generator is 63.8%. This shows that the efficiency is significantly improved compared with the 24.2% efficiency of regenerative braking using the motor.