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.     

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.     

Efficient model selection for real-time adaptive cold start strategy of a fuel cell system on vehicular applications

The PEMFC maximum power is greatly influenced by subfreezing temperature and degradation phenomena. Therefore, a dependable model is required to estimate the power with respect to the variation of the operating conditions and state of health. Semi-empirical models are potent tools in this regard. Nonetheless, there is not much information about their cold environment reliability. This paper comprehensively compares the performance of some models (already tested in normal ambient temperature) in subfreezing condition to introduce the most reliable one for PEMFC cold start-up application. Firstly, seven models are compared regarding voltage losses and precision. Subsequently, the three most dependable ones are selected and experimentally compared at sub-zero temperature in terms of polarization curve estimation for three PEMFCs with different degradation levels. The results of this study indicate that the model introduced by Amphlett et al. has a superior performance compared to other ones regarding the characteristic's estimation in below-zero temperature.     

Modeling and thermal management of proton exchange membrane fuel cell for fuel cell/battery hybrid automotive vehicle

The proton exchange membrane fuel cell (PEMFC) stack is a key component in the fuel cell/battery hybrid vehicle. Thermal management and optimized control of the PEMFC under real driving cycle remains a challenging issue. This paper presents a new hybrid vehicle model, including simulations of diver behavior, vehicle dynamic, vehicle control unit, energy control unit, PEMFC stack, cooling system, battery, DC/DC converter, and motor. The stack model had been validated against experimental results. The aim is to model and analyze the characteristics of the 30 kW PEMFC stack regulated by its cooling system under actual driving conditions. Under actual driving cycles (0–65 kW/h), 33%–50% of the total energy becomes stack heat; the heat dissipation requirements of the PEMFC stack are high and increase at high speed and acceleration. A PID control is proposed; the cooling water flow rate is adjusted; the control succeeded in stabilizing the stack temperature at 350 K at actual driving conditions. Constant and relative lower inlet cooling water temperature (340 K) improves the regulation ability of the PID control. The hybrid vehicle model can provide a theoretical basis for the thermal management of the PEMFC stack in complex vehicle driving conditions.     

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.     

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.     

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.     

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.     

Model-based decoupling control for the thermal management system of proton exchange membrane fuel cells

As one of the most promising sustainable energy technologies available today, proton exchange membrane fuel cell (PEMFC) engines are becoming more and more popular in various applications, especially in transportation vehicles. However, the complexity and the severity of the vehicle operating conditions present challenges to control the temperature distribution in single cells and stack, which is an important factor influencing the performance and durability of PEMFC engines. It has been found that regulating the input and output coolant water temperature can improve the temperature distribution. Therefore, the control objective in this paper is regulating the input and output temperature of coolant water at the same time. Firstly, a coupled model of the thermal management system is established based on the physical structure of PEMFC engines. Then, in order to realize the simultaneous control of the inlet and outlet cooling water temperature of the PEMFC stack, a decoupling controller is proposed and its closed-loop stability is proved. Finally, based on the actual PEMFC engine platform, the effectiveness, accuracy and reliability of the proposed decoupling controller are tested. The experimental results show that with the proposed decoupling controller, the inlet and outlet temperatures of the PEMFC stack cooling water can be accurately controlled on-line. The temperature error range is less than 0.2 °C even under the dynamic current load conditions.     

Adaptive energy management for fuel cell hybrid power system with power slope constraint and variable horizon speed prediction

An adaptive energy management strategy (EMS) is proposed to improve the economy and reliability of the fuel cell vehicle. Firstly, a variable horizon speed prediction method based on the principal component analysis and the K-means clustering is constructed. Then, an adaptive equivalent consumption minimization strategy (AECMS) with power slope constraints was designed to minimize the hydrogen consumption while ensuring reliability. Finally, a proportional-integral controller is used to track the air flow and pressure of the fuel cell engine (FCE) under energy distribution. Simulation results under West Virginia University Suburban (WVUSUB) show that the proposed strategy can improve the speed prediction accuracy by 2.80% and 25.57%, and reduce the hydrogen consumption by 2.79% and 2.66%, respectively, compared with the fixed 12 s and 15 s horizon. Moreover, the control error of oxygen excess ratio and the cathode pressure under energy distribution are 0.0102 (0.51%) and 189.4 Pa (0.0935%), respectively, indicating better reliability than the strategy without constraint.     

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.     

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.     

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.     

Thermal analysis and management of proton exchange membrane fuel cell stacks for automotive vehicle

The thermal management of a proton exchange membrane fuel cell (PEMFC) is crucial for fuel cell vehicles. This paper presents a new simulation model for the water-cooled PEMFC stacks for automotive vehicles and cooling systems. The cooling system model considers both the cooling of the stack and cooling of the compressed air through the intercooler. Theoretical analysis was carried out to calculate the heat dissipation requirements for the cooling system. The case study results show that more than 99.0% of heat dissipation requirement is for thermal management of the PEMFC stack; more than 98.5% of cooling water will be distributed to the stack cooling loop. It is also demonstrated that controlling cooling water flow rate and stack inlet cooling water temperature could effectively satisfy thermal management constraints. These thermal management constraints are differences in stack inlet and outlet cooling water temperature, stack temperature, fan power consumption, and pump power consumption.     

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.     

Regenerative energy control system for plug-in hydrogen fuel cell scooter

An intelligent control system was developed using simple control methodologies for an H₂-powered fuel cell scooter with the aid of a built-in microprocessor. This system increases the power input to drive a hydrogen fuel cell scooter, particularly during uphill conditions by running both the batteries and the fuel cell source in parallel. This system also improves the energy management of the scooter by recharging the battery using the fuel cell as well as automatic switching to the battery source when the hydrogen fuel cell is running low on hydrogen. This system was tested on a bench set simulating a 254 W hydrogen fuel cell stack equipped on a 200 W scooter. The test rig set-up depicts a practical scooter running on various load conditions. These results reflect the efficiencies of actual running conditions. The entire operation was embedded in a PICAXE-18 microcontroller for automatic switching between the batteries and the fuel cell source. An increase in the DC motor efficiency by 6 % has been shown. The uphill angle of the scooter has been increased by 19.3 %, which means the scooter would be able to travel on steeper hills. Copyright © 2007 John Wiley & Sons, Ltd.     

Comparison of MPC based advanced hybrid controllers for STATCOM in medium scale PEM fuel cell systems

In this article is about off grid medium scale Proton Exchange Membrane (PEM) Fuel Cell (FC) Renewable Energy System (FCRES). It is aimed to investigate the elimination of reactive power on the loads fed from the FCRES by using PI-Model Predictive Control (PI-MPC), Fractional PI (PI^λ)-Model Predictive Control (PI^λ−MPC) and Two-Degree of Freedom PI (2DOFPI)-Model Predictive Controller (2DOFPI-MPC). The effectiveness of these controllers were examined and compared both when controlling reactive power and using different control techniques. The reactive power was attempted to be eliminated using Static Synchronous Compensator (STATCOM). An advanced controller structure has been introduced by controlling capacitor voltages by switching 24 IGBTs in the 5-level inverter within its structure. In this way, the efficiency of the system was increased. The system was simulated in MATLAB/Simulink environment. System consisted of FC, STATCOM, loads, filter, coupling inductance, measurement units and advanced hybrid controllers. In the study, system behavior was also examined in the case of compensation in the system consisting of FCs working off grid.