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.     

The study on the power management system in a fuel cell hybrid vehicle

This paper presents a model of a hybrid electric vehicle, based on a primary proton exchange membrane fuel cell (PEMFC) and an auxiliary Li-ion battery, and its dynamics and overall performance. The power voltage from the fuel cell is regulated by a DC/DC converter before integrating with the Li-ion battery, which provides energy to the drive motor. The driving force for propelling the wheels comes from a permanent magnet synchronous motor (PMSM); where the power passes through the transmission, shaft, and the differential.     

Proton exchange membrane fuel cell degradation prediction based on Adaptive Neuro-Fuzzy Inference Systems

This paper studies the prediction of the output voltage reduction caused by degradation during nominal operating condition of a PEM fuel cell stack. It proposes a methodology based on Adaptive Neuro-Fuzzy Inference Systems (ANFIS) which use as input the measures of the fuel cell output voltage during operation. The paper presents the architecture of the ANFIS and studies the selection of its parameters. As the output voltage cannot be represented as a periodical signal, the paper proposes to predict its temporal variation which is then used to construct the prediction of the output voltage. The paper also proposes to split this signal in two components: normal operation and external perturbations. The second component cannot be predicted and then it is not used to train the ANFIS. The performance of the prediction is evaluated on the output voltage of two fuel cells during a long term operation (1000 h). Validation results suggest that the proposed technique is well adapted to predict degradation in fuel cell systems.     

Real-time implementation of a neural model-based self-tuning PID strategy for oxygen stoichiometry control in PEM fuel cell

This paper proposes a real-time implementable self-tuning PID control strategy to tackle oxygen excess ratio regulation challenge of a proton exchange membrane fuel cell. Controller parameters are updated on-line, at each sampling time, using a not iterative procedure based on an artificial neural network model. The proposed controller takes account of nonlinear behaviors of the process, while avoiding heavy computations.     

Numerical modeling of a non-flooding hybrid polymer electrolyte fuel cell

Experimental results were recently reported regarding a novel “non-flooding” hybrid fuel cell consisting of proton exchange membrane (PEM) and anion exchange membrane (AEM) half-cells on opposite sides of a water-filled, porous intermediate layer. Product water formed in the porous layer, where it could permeate to the exterior of the cell, rather than at the electrodes. Although electrode flooding was mitigated, the reported power output was low. To investigate the potential for increased power output, a physicochemical charge transport model of the porous electrolyte layer is reported here. Traditional electrochemical modeling was generalized in a novel way to consider both ion transport and reaction in the aqueous phase and electronic conduction in the graphitic scaffold using a unified Poisson–Nernst–Planck framework. Though the model used no arbitrary or fitting parameters, the ionic resistance calculated for the porous layer agreed well with the highly non-Ohmic experimental values previously reported for the entire fuel cell. Interestingly, electronic charge carriers in the scaffold were found to obviate the need for counterion presence in this unique electrolyte structure. Still, the thickness- and temperature-dependent model results offer limited prospects for improving the power output.     

Intelligent energy management strategy based on hierarchical approximate global optimization for plug-in fuel cell hybrid electric vehicles

The energy management strategy (EMS) is a key to reduce the equivalent hydrogen consumption and slow down fuel cell performance degradation of the plug-in fuel cell hybrid electric vehicles. Global optimal EMS based on the whole trip information can achieve the minimum hydrogen consumption, but it is difficult to apply in real driving. This paper tries to solve this problem with a novel hierarchical EMS proposed to realize the real-time application and approximate global optimization. The long-term average speed in each future trip segment is predicted by KNN, and the short-term speed series is predicted by a new model averaging method. The approximate global optimization is realized by introducing hierarchical reinforcement learning (HRL), and the strategy within the speed forecast window is optimized by introducing upper confidence tree search (UCTS). The vehicle speed prediction and the proposed EMS have been verified using the collected real driving cycles. The results show that the proposed strategy can adapt to driving style changes through self-learning. Compared with the widely used rule-based strategy, it can evidently reduce hydrogen consumption by 6.14% and fuel cell start-stop times by 21.7% on average to suppress the aging of fuel cell. Moreover, its computation time is less than 0.447 s at each step, and combined with rolling optimization, it can be used for real-time application.     

Experimental investigation on water and heat management in a PEM fuel cell using response surface methodology

Water and heat management are the most critical issues for the performance of proton exchange membrane (PEM) fuel cells. They can be provided by keeping hydrogen flow rate, oxygen flow rate, cell temperature and humidification temperature under control. In this study, the effects of these parameters on the power density of proton exchange membrane (PEM) fuel cell which has 25 cm² active area have been examined experimentally using hydrogen on the anode side and oxygen on the cathode side. Response Surface Methodology (RSM) has been applied to optimize these operation parameters of proton exchange membrane (PEM) fuel cell. The test responses are the maximum output power density. ANOVA (analysis of variance) analyses are used to compute the effects and the contributions of the various factors to the fuel cell maximal power density. The use of this design shows also how it is possible to reduce the number of experiments. Hydrogen flow rate, oxygen flow rate, humidification temperature and cell temperature were the main parameters to have been varied between 2.5–5 L/min, 3–5 L/min, 40–70 °C and 40–80 °C in the analyses. The maximum power density was found as 241.977 mW/cm².     

Control of a heat-integrated proton exchange membrane fuel cell system with methanol reforming

This work presents a novel heat-integrated fuel cell stack system with methanol reforming. Its configuration is composed of fuel processing units (FPUs), proton exchange membrane (PEM) fuel cell stack, and heat exchangers (HEXs). Well mixed methanol and oxygen flows in contact with countercurrent flowing water dominates the production of hydrogen at the exit of FPUs and influences the stack temperature. The heat exchange connections can enhance the utilization of energy of FPUs. To ensure the stable steady-state operation, the model-free fuzzy incremental control scheme within the multi-loop feedback control framework is developed. Finally, the proposed system integration and control configuration are verified by closed-loop simulations.     

Dynamic modeling of a photovoltaic hydrogen fuel cell hybrid system

The objective of this paper is to mathematically model a stand-alone renewable power system, referred to as “Photovoltaic–Fuel Cell (PVFC) hybrid system”, which maximizes the use of a renewable energy source. It comprises a photovoltaic generator (PV), a water electrolyzer, a hydrogen tank, and a proton exchange membrane (PEM) fuel cell generator. A multi-domain simulation platform Simplorer is employed to model the PVFC hybrid systems. Electrical power from the PV generator meets the user loads when there is sufficient solar radiation. The excess power from the PV generator is then used for water electrolysis to produce hydrogen. The fuel cell generator works as a backup generator to supplement the load demands when the PV energy is deficient during a period of low solar radiation, which keeps the system's reliability at the same level as for the conventional system. Case studies using the present model have shown that the present hybrid system has successfully tracked the daily power consumption in a typical family. It also verifies the effectiveness of the proposed management approach for operation of a stand-alone hybrid system, which is essential for determining a control strategy to ensure efficient and reliable operation of each part of the hybrid system. The present model scheme can be helpful in the design and performance analysis of a complex hybrid-power system prior to practical realization.     

Development of a proton exchange membrane fuel cell cogeneration system

A proton exchange membrane fuel cell (PEMFC) cogeneration system that provides high-quality electricity and hot water has been developed. A specially designed thermal management system together with a microcontroller embedded with appropriate control algorithm is integrated into a PEM fuel cell system. The thermal management system does not only control the fuel cell operation temperature but also recover the heat dissipated by FC stack. The dynamic behaviors of thermal and electrical characteristics are presented to verify the stability of the fuel cell cogeneration system. In addition, the reliability of the fuel cell cogeneration system is proved by one-day demonstration that deals with the daily power demand in a typical family. Finally, the effects of external loads on the efficiencies of the fuel cell cogeneration system are examined. Results reveal that the maximum system efficiency was as high as 81% when combining heat and power.     

Multivariable robust control of a proton exchange membrane fuel cell system

This paper applies multivariable robust control strategies to a proton exchange membrane fuel cell (PEMFC) system. From the system point of view, a PEMFC can be modeled as a two-input-two-output system, where the inputs are air and hydrogen flow rates and the outputs are cell voltage and current. By fixing the output resistance, we aimed to control the cell voltage output by regulating the air and hydrogen flow rates. Due to the nonlinear characteristics of this system, multivariable robust controllers were designed to provide robust performance and to reduce the hydrogen consumption of this system. The study was carried out in three parts. Firstly, the PEMFC system was modeled as multivariable transfer function matrices using identification techniques, with the un-modeled dynamics treated as system uncertainties and disturbances. Secondly, robust control algorithms were utilized to design multivariable H_∞ controllers to deal with system uncertainty and performance requirements. Finally, the designed robust controllers were implemented to control the air and hydrogen flow rates. From the experimental results, multivariable robust control is shown to provide steady output responses and significantly reduce hydrogen consumption.     

Performance of a proton exchange membrane fuel cell stack with thermally conductive pyrolytic graphite sheets for thermal management

This work experimentally investigates the effects of the pyrolytic graphite sheets (PGS) on the performance and thermal management of a proton exchange membrane fuel cell (PEMFC) stack. These PGS with the features of light weight and high thermal conductivity serve as heat spreaders in the fuel cell stack for the first time to reduce the volume and weight of cooling systems, and homogenizes the temperature in the reaction areas. A PEMFC stack with an active area of 100 cm² and 10 cells in series is constructed and used in this research. Five PGS of thickness 0.1 mm are cut into the shape of flow channels and bound to the central five cathode gas channel plates. Four thermocouples are embedded on the cathode gas channel plates to estimate the temperature variation in the stack. It is shown that the maximum power of the stack increase more than 15% with PGS attached. PGS improve the stack performance and alleviate the flooding problem at low cathode flow rates significantly. Results of this study demonstrate the feasibility of application of PGS to the thermal management of a small-to-medium-sized fuel cell stack.     

Design of proton exchange membrane fuel cell grid-connected system based on resonant current controller

Because of its high efficiency, low pollution and good stability, proton exchange membrane fuel cell (PEMFC) is considered as one of the most promising technologies for a wide range of applications, such as distributed power generation, transportation, portable power source and automobile. In a PEMFC grid-connected system, the proportion integration (PI) regulator can achieve zero error for the dc components in the rotating frame, but cannot achieve zero error for the ac components in the rotating frame. Hence, the PEMFC grid-connected system will produce a large number of harmonics. In order to overcome this shortcoming, a proportion integration resonant (PIR) controller is utilized to realize zero magnitude error and selective disturbance rejection. Instead of the PIR controller, a vector proportion integration (VPI) controller is designed to quickly and accurately regulate current which achieve zero both amplitude and phase frequency response at the resonant frequency simultaneously. In this paper, the PEMFC grid-connected system based on PIR and VPI controllers are developed according to the operating characteristics of a PEMFC generation system, then analyze and compare the performance of compensation harmonics between them. The total harmonic distortion (THD) of grid-connected voltage and current are measured by means of the criterion of IEEE Std1547-2003. This proposed grid-connected method will provide a novel approach for the design of advanced PEMFC grid-connected control system.     

PEM fuel cell supported distribution static compensator for power quality enhancement in three-phase four-wire distribution system

One of the major problems in electrical power system is the lack of quality of power due to the rapid growth of nonlinear load and unbalanced load utilization in three-phase four-wire distribution system. In this paper, PEM (Proton Exchange Membrane) fuel cell supported four-leg Distribution Static Compensator (DSTATCOM) is modelled to mitigate harmonics, neutral current and load balancing under nonlinear load and unbalanced load conditions in three-phase four-wire distribution system. The instantaneous reactive power (IRP) theory control algorithm is proposed for four-leg DSTATCOM. The Real coded Genetic Algorithm (RGA) optimized Proportional Integral (PI) controller and Adaptive Neuro Fuzzy Inference System (ANFIS) controller are used for regulating the DC link voltage of DSTATCOM. This paper also investigates the performance of ANFIS based DSTATCOM with conventional method. The proposed system is modelled and its performance is analyzed in MATLAB/SIMULINK.     

Brazilian hybrid electric-hydrogen fuel cell bus: Improved on-board energy management system

The present paper unveils the technology developed for a series hybrid battery-dominant electric-hydrogen fuel cell plug-in city bus. It possesses a homemade power train with three electric energy sources, which are the grid-charged energy, the one produced by the fuel cell that works at constant power and acts as a range extender and that resultant from the regeneration of kinetic energy. Emphasis was given to the design of the hybridization energy engineering that has predominance of power in batteries and predominance of energy with hydrogen. The remarkable amount of 46.6% of the total energy input reaches the motor axle for effective motion and a fuel economy of 6.7 kg H₂/100 km was achieved. A total owner cost analysis has shown that computation of capital, operational and fueling costs makes the present bus 133% more expensive than a conventional diesel powered one. Commercialization prospects, and also social and environmental impacts are analyzed.     

Parameter identification for proton exchange membrane fuel cell model using particle swarm optimization

The accurate mathematical model is an extremely useful tool for simulation and design analysis of fuel cell power systems. Particle swarm optimization (PSO) is a recently invented high-performance algorithm. In this work, a PSO-based parameter identification technique of proton exchange membrane (PEM) fuel cell models was proposed in terms of the voltage–current characteristics. Using the simulated and experimental voltage–current data, the validity of the proposed method has been confirmed. The results indicate that the PSO is an effective technique for identifying the parameters of PEM fuel cell models even in the presence of measuring noise. Moreover, the proposed method does not particularly necessitate initial guesses as close as possible to the solutions, required only is a broad range specified for each of the parameters. Therefore, the PSO method outperforms the GA and traditional optimization methods.     

Dynamic fuel cell stack model for real-time simulation based on system identification

The authors have been developing an empirical mathematical model to predict the dynamic behaviour of a polymer electrolyte membrane fuel cell (PEMFC) stack. Today there is a great number of models, describing steady-state behaviour of fuel cells by estimating the equilibrium voltage for a certain set of operating parameters, but models capable of predicting the transient process between two steady-state points are rare. However, in automotive applications round about 80% of operating situations are dynamic. To improve the reliability of fuel cell systems by model-based control for real-time simulation dynamic fuel cell stack model is needed. Physical motivated models, described by differential equations, usually are complex and need a lot of computing time. To meet the real-time capability the focus is set on empirical models. Fuel cells are highly nonlinear systems, so often used auto-regressive (AR), output-error (OE) or Box-Jenkins (BJ) models do not accomplish satisfying accuracy. Best results are achieved by splitting the behaviour into a nonlinear static and a linear dynamic subsystem, a so-called Uryson-Model. For system identification and model validation load steps with different amplitudes are applied to the fuel cell stack at various operation points and the voltage response is recorded. The presented model is implemented in MATLAB environment and has a computing time of less than 1 ms per step on a standard desktop computer with a 2.8 MHz CPU and 504 MB RAM. Lab tests are carried out at DaimlerChrysler R&D Centre with DaimlerChrysler PEMFC hardware and a good agreement is found between model simulations and lab tests.     

An overview: Current progress on hydrogen fuel cell vehicles

The harmful consequences of pollutants emitted by conventional fuel cars have prompted vehicle manufacturers to shift towards alternative energy sources. Currently, fuel cells (FCs) are commonly regarded as highly efficient and non-polluting power sources capable of delivering far greater energy densities and energy efficiency than conventional technologies. Proton exchange membrane fuel cells (PEMFC) are viewed as promising in transportation sectors because of their ability to start at cold temperatures and minimal emissions. PEMFC is an electrochemical device that converts hydrogen and oxidants into electricity, water, and heat at various temperatures. The pros and cons of the technology are discussed in this article. Various fuel cell types and their applications in the portable, automobile, and stationary sectors are discussed. Additionally, recent issues associated with existing fuel cell technology in the automobile sector are reviewed.     

Multiple model predictive control for a hybrid proton exchange membrane fuel cell system

This paper presents a hierarchical predictive control strategy to optimize both power utilization and oxygen control simultaneously for a hybrid proton exchange membrane fuel cell/ultracapacitor system. The control employs fuzzy clustering-based modeling, constrained model predictive control, and adaptive switching among multiple models. The strategy has three major advantages. First, by employing multiple piecewise linear models of the nonlinear system, we are able to use linear models in the model predictive control, which significantly simplifies implementation and can handle multiple constraints. Second, the control algorithm is able to perform global optimization for both the power allocation and oxygen control. As a result, we can achieve the optimization from the entire system viewpoint, and a good tradeoff between transient performance of the fuel cell and the ultracapacitor can be obtained. Third, models of the hybrid system are identified using real-world data from the hybrid fuel cell system, and models are updated online. Therefore, the modeling mismatch is minimized and high control accuracy is achieved. Study results demonstrate that the control strategy is able to appropriately split power between fuel cell and ultracapacitor, avoid oxygen starvation, and so enhance the transient performance and extend the operating life of the hybrid system.     

Parallel serpentine-baffle flow field design for water management in a proton exchange membrane fuel cell

In a proton exchange membrane fuel cell (PEMFC) water management is one of the critical issues to be addressed. Although the membrane requires humidification for high proton conductivity, water in excess decreases the cell performance by flooding. In this paper an improved strategy for water management in a fuel cell operating with low water content is proposed using a parallel serpentine-baffle flow field plate (PSBFFP) design compared to the parallel serpentine flow field plate (PSFFP). The water management in a fuel cell is closely connected to the temperature control in the fuel cell and gases humidifier. The PSBFFP and the PSFFP were evaluated comparatively under three different humidity conditions and their influence on the PEMFC prototype performance was monitored by determining the current density–voltage and current density–power curves. Under low humidification conditions the PEMFC prototype presented better performance when fitted with the PSBFFP since it retains water in the flow field channels.