Plug-in hybrid fuel cell vehicles market penetration scenarios

The main objective of this research is to analyze the impact of the market share increase of hydrogen based road vehicles in terms of energy consumption and CO₂, on today's Portuguese light-duty fleet. Actual yearly values of energy consumption and emissions were estimated using COPERT software: 167112 TJ of fossil fuel energy, 12213 kton of CO₂ emission and 141 kton of CO, 20 kton of HC, 46 kton of NO_x and 3 kton of PM. These values represent 20–40% of countries total emissions. Additionally to base fleet, three scenarios of introduction of 10–30% fuel cell vehicles including plug-in hybrids configurations were analysed. Considering the scenarios of increasing hydrogen based vehicles penetration, up to 10% life cycle energy consumption reduction can be obtained if hydrogen from centralized natural gas reforming is considered. Full life cycle CO₂ emissions can also be reduced up to 20% in these scenarios, while local pollutants reach up to 85% reductions. For the purpose of estimating road vehicle technologies energy consumption and CO₂ emissions in a full life cycle perspective, fuel cell, conventional full hybrids and hybrid plug-in technologies were considered with diesel, gasoline, hydrogen and biofuel blends. Energy consumption values were estimated in a real road driving cycle and with ADVISOR software. Materials cradle-to-grave life cycle was estimated using GREET database adapted to Europe electric mix. The main conclusions on CO₂ full life cycle analysis is that light-duty vehicles using fuel cell propulsion technology are highly dependent on hydrogen production pathway. The worst scenario for the current Portuguese and European electric mix is hydrogen produced from on-site electrolysis (in the refuelling stations). In this case full life cycle CO₂ is 270 g/km against 190 g/km for conventional Diesel vehicle, for a typical 150,000 km useful life.     

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.     

Power management and design optimization of fuel cell/battery hybrid vehicles

Power management strategy is as significant as component sizing in achieving optimal fuel economy of a fuel cell hybrid vehicle (FCHV). We have formulated a combined power management/design optimization problem for the performance optimization of FCHVs. This includes subsystem-scaling models to predict the characteristics of components of different sizes. In addition, we designed a parameterizable and near-optimal controller for power management optimization. This controller, which is inspired by our stochastic dynamic programming results, can be included as design variables in system optimization problems. Simulation results demonstrate that combined optimization can efficiently provide excellent fuel economy.     

Optimization of fuel cell system operating conditions for fuel cell vehicles

Proton exchange membrane fuel cell (PEMFC) technology for use in fuel cell vehicles and other applications has been intensively developed in recent decades. Besides the fuel cell stack, air and fuel control and thermal and water management are major challenges in the development of the fuel cell for vehicle applications. The air supply system can have a major impact on overall system efficiency. In this paper a fuel cell system model for optimizing system operating conditions was developed which includes the transient dynamics of the air system with varying back pressure. Compared to the conventional fixed back pressure operation, the optimal operation discussed in this paper can achieve higher system efficiency over the full load range. Finally, the model is applied as part of a dynamic forward-looking vehicle model of a load-following direct hydrogen fuel cell vehicle to explore the energy economy optimization potential of fuel cell vehicles.     

Dynamic competition between plug-in hybrid and hydrogen fuel cell vehicles for personal transportation

This article addresses the issue of the diffusion of hydrogen cars in the market, particularly the competition with electric cars for the replacement of conventional vehicles. Using the multi-technological competition model developed by Le Bas and Baron-Sylvester’s (Diffusion technologique non binaire et schéma épidémiologique. Une reconsidération. Economie Appliquée 1995; tome XLVIII(3):71–101), it is shown that the early deployment of plug-in hybrid vehicles—the only electric technology which can compete with fuel cell cars in the multipurpose vehicle field—risks closing the market for hydrogen in the future. Moreover, the advent of the hydrogen vehicle depends on the rapid advancements in fuel cell technologies, as well as on the existence of an infrastructure with a sufficient coverage.     

燃料电池在车辆中应用的技术难关

近年来,人们对能源匮乏和环境污染问题日益重视,使得燃料电池汽车的研究开发成为汽车行业的热点。阐述了燃料电池汽车的结构及质子交换膜燃料电池(PEMFC)是燃料电池汽车动力源的首选;对燃料电池汽车目前存在的技术难关及发展形势进行了综述。最后,预测随着燃料电池技术的进步,燃料电池最终将完全取代内燃机成为车辆动力装置。     

Optimal control in the power management of fuel cell hybrid vehicles

Several types of power management strategies have been developed to improve the fuel economy of fuel cell hybrid vehicles (FCHVs). Optimal control based on the Minimum Principle provides the necessary optimality conditions which minimize fuel consumption and optimize the power distribution between power sources while the vehicle is being driven. In the optimal control scheme, the costate is an equivalent parameter between fuel usage and electric usage. The optimal trajectory of the costate can be derived from one of the necessary conditions. In this paper, an optimal control scheme based on the Minimum Principle is proposed for cases without a state constraint and for those with a state constraint. The conditions in which a variable costate can be replaced with a constant costate are presented. The simulation results with constant costates are compared to those with variable costates in order to prove that variable costates can be replaced with constant costates when using the proposed optimal control scheme.     

Hybrid polymer electrolyte membrane fuel cell–lithium‐ion battery powertrain testing platform – hybrid fuel cell electric vehicle emulator

A testing and validation platform for hybrid fuel cell (FC)–lithium‐ion battery (LIB) powertrain systems is investigated. The hybrid FC electric vehicle emulator enables testing of hybrid system components and complete hybrid power modules up to 25 kW for application in electric light‐duty vehicles, light electric vehicles and so forth. A hybrid system comprising a 10‐kW_el low‐temperature polymer electrolyte membrane FC stack and an 11.5‐kWh LIB pack is installed. The system supplies power to a 20‐kW permanent magnet synchronous motor and a 25‐kW alternating current asynchronous, electrically programmable dynamometer is used to simulate the vehicle load during testing at dynamic drive cycle. The steady‐state performance tests of the direct current (DC) motor, DC/DC converter, low‐temperature polymer electrolyte membrane FC stack and LIB are performed as well as dynamic tests of the complete hybrid system. The Economic Commission for Europe driving cycle is selected as a reference cycle to validate the investigated hybrid FC–LIB powertrain. An efficiency of 83% and 95% is measured for electric motor and DC/DC converter, respectively. An average stack efficiency of 50% is achieved. An average hydrogen consumption of 3.9 g * km^?1 is reached during the Economic Commission for Europe driving cycle test. Copyright © 2017 John Wiley & Sons, Ltd.     

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 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.     

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.     

The effect of battery temperature on total fuel consumption of fuel cell hybrid vehicles

In order to consider the effect of battery temperature on the total fuel consumption when a Pontryagin's Minimum Principle (PMP)-based power management strategy is applied to a fuel cell hybrid vehicle (FCHV), this paper designates the battery temperature as a second-state variable other than the battery state of charge (SOC) and defines a new costate for the battery temperature in the control problem. The PMP-based power management strategy is implemented in a computer simulation and the relationship among the final values of the two state variables and the total fuel consumption is illustrated based on the simulation results. This relationship is defined as an optimal surface in this research. Using the optimal surface, it can be concluded that considering the battery temperature effect in the PMP-based power management strategy improves the fuel economy of the FCHV. Potential fuel economy gains attributed to consideration of the battery temperature effect are also determined based on the optimal surfaces.     

Minimum time current controller design for two-interleaved bidirectional converter: Application to hybrid fuel cell/supercapacitor vehicles

In fuel cell hybrid vehicles (FCHV) with a supercapacitor (SC) used as auxiliary source of hybridization, classical SC converters limit the current tracking performance due to the presence of a large input inductance. A hardware solution to this issue consists in using an interleaved converter topology that enables considerably reducing the input inductance. To optimize interleaved converters usage, an intelligent control scheme that maximizes closed-loop performance in current tracking must be designed. For that purpose, we design two controllers: a transient-state controller and a steady-state one. The first controller is a bang-bang one that guaranties a fast transition from the initial condition to some closed region around the reference. The second controller ensures the local asymptotic stabilization of a certain desired limit cycle and is based on switching surfaces design. The proposed control scheme performances and robustness are evaluated through simulations with piece-wise constant source and reference current. Performances are also evaluated on a battery-like supercapacitor vehicle using an urban driving cycle.     

Hydrogen fuel cell hybrid vehicles (HFCHV) for Birmingham campus

The design of a campus mail delivery vehicle powered by 350 bar hydrogen feeding a 1.2 kW PEM fuel cell to charge a lead acid battery pack is described. Five vehicles supplied to the campus at the University of Birmingham to measure the performance and to evaluate relevance to fleet operations are discussed. It is shown that the performance is better than that of a standard diesel van in two drive cycles, one following an academic circuit around the campus, the other doing multiple mail delivery stops. The acceleration and drive cycle compliance are found to be adequate on campus and the efficiency is significantly better than the diesel. The need for extension of range and increase in power and acceleration to meet standard urban drive cycles is clearly demonstrated.     

高温燃料电池/燃气轮机混合循环发电技术

高温燃料电池／燃气轮机混合循环系统以其效率高、排放低的特点,在未来分布式发电和集中式大规模发电中占有重要地位。本文首先简介了高温燃料电池和先进燃气轮机的结构特点及其分类,在此基础上阐述了高温燃料电池与先进燃气轮机混合系统的基本模式,然后对适用于分布式发电和集中式发电的几种典型混合循环系统的结构和相应的流程及特点进行了详细的描述,最后给出了高温燃料电池和燃气轮机混合循环发电系统中的一些主要代表性技术以及目前研究的进展、挑战和目标。     

Turbomachinery for the air management and energy recovery in fuel cell gas turbine hybrid systems

High temperature fuel cells (MCFCs and SOFCs) can operate at atmospheric or pressurised conditions. In both cases, system performance can be significantly improved when the fuel cells are integrated with proper devices, which are designed to provide the necessary air inlet conditions and to recover the exhaust gas energy.     

Co-optimization strategies for connected and automated fuel cell hybrid vehicles in dynamic curving scenarios

The performance of speed planning and energy management for connected and automated fuel cell hybrid vehicles (CAFCHVs) in the curve directly affects the curve passage, operating safety and energy economy. However, the uncertainty of complex traffic conditions (such as the dynamic state of the preceding and ego vehicle, road adhesion coefficient, and curve radius) and the lateral stability of CAFCHV lead to the difficulty of online speed planning and energy management. To address this problem, a co-optimization strategy is implemented in this study. First, according to the stability condition of CAFCHV in the curve, the critical safe speed is obtained by phase plane analysis. In addition, combing the timeliness, future information of driving conditions, and the current state of preceding and ego-vehicles, the gradient-based model prediction control (GRAMPC) leveraging the fast projection gradient method is adopted to calculate the safe and optimal speed sequence. Meanwhile, the energy management strategy (EMS) based on the power ratio adaptive equivalent consumption minimization strategy (PR-AECMS) is utilized for energy distribution. A multi-objective performance function is introduced to evaluate the total cost of hydrogen consumption and battery life extension. The simulation results reveal that the proposed strategy can obtain a safe and optimal speed sequence when CAFCHV operates on the curve road. And compared with the mode of tracking the speed of the preceding vehicle, the hydrogen consumption, SOC, battery degradation, and total cost are reduced by 1.4%, 1.9%, 9.9%, and 1.8% regulated by the planning mode, respectively.     

车载燃料电池诊断装置的研究

介绍了一种特殊的车载燃料电池诊断装置,该装置包括控制单元、燃料气体供给单元、电力调整单元、冷却单元和电力消耗单元.该装置可以精确地再现车辆行驶期间燃料电池堆的异常情况,便于在修理车间对其进行彻查,及时发现并排除故障.当燃料电池的冷却系统或燃料气体供给系统受到损坏时,利用该诊断装置的冷却单元和燃料气体供给单元也可以对燃料电池堆进行准确的诊断.     

A comparison of rule-based and model predictive controller-based power management strategies for fuel cell/battery hybrid vehicles considering degradation

Traditional power management systems for hybrid vehicles often focus on the optimization of one particular cost factor, such as fuel consumption, under specific driving scenarios. The cost factor is usually based on the beginning-of-life performance of system components. Typically, such strategies do not account for the degradation of the different components of the system over their lifetimes. This study incorporates the effect of fuel cell and battery degradation within their cost factors and investigates the impact of different power management strategies on fuel cell/battery loads and thus on the operating cost over the vehicle's lifetime. A simple rule-based power management system was compared with a model predictive controller (MPC) based system under a connected vehicle scenario (where the future vehicle speed is known a priori within a short time horizon). The combined cost factor consists of hydrogen consumption and the degradation of both the fuel cell stack and the battery. The results show that the rule-based power management system actually performs better and achieves lower lifetime cost compared to the MPC system even though the latter contains more information about the drive cycle. This result is explained by examining the changing dynamics of the three cost factors over the vehicle's lifetime. These findings reveal that a limited knowledge of traffic information might not be as useful for the power management of certain fuel cell/battery hybrid vehicles when degradation is taken into consideration, and a simple tuned rule-based controller is adequate to minimize the lifetime cost.     

A sizing-design methodology for hybrid fuel cell power systems and its application to an unmanned underwater vehicle

Hybridizing a fuel cell with an energy storage unit (battery or supercapacitor) combines the advantages of each device to deliver a system with high efficiency, low emissions, and extended operation compared to a purely fuel cell or battery/supercapacitor system. However, the benefits of such a system can only be realised if the system is properly designed and sized, based on the technologies available and the application involved. In this work we present a sizing-design methodology for hybridisation of a fuel cell with a battery or supercapacitor for applications with a cyclic load profile with two discrete power levels. As an example of the method's application, the design process for selecting the energy storage technology, sizing it for the application, and determining the fuel load/range limitations, is given for an unmanned underwater vehicle (UUV). A system level mass and energy balance shows that hydrogen and oxygen storage systems dominate the mass and volume of the energy system and consequently dictate the size and maximum mission duration of a UUV.