battery electric vehicles

i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 3 8 ( 2 0 1 3 ) 3 4 3 3 e3 4 4 6

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Lifecycle performance assessment of fuel cell/battery electric vehicles Jenn-Jiang Hwang a,*, Jenn-Kun Kuo a, Wei Wu b, Wei-Ru Chang c, Chih-Hong Lin a, Song-En Wang a a

Department of Greenergy, National University of Tainan, Tainan, Taiwan Department of Chemical Engineering, National Cheng Kung University, Tainan, Taiwan c Department of Landscape Architecture, Fu Jen Catholic University, Taipei, Taiwan b

article info

abstract

Article history:

This paper has performed an assessment of lifecycle (as known as well-to-wheels, WTW)

Received 17 November 2012

greenhouse gas (GHG) emissions and energy consumption of a fuel cell vehicle (FCV). The

Received in revised form

simulation tool MATLAB/Simulink is employed to examine the real-time behaviors of an

28 December 2012

FCV, which are used to determine the energy efficiency and the fuel economy of the FCV.

Accepted 29 December 2012

Then, the GREET (Greenhouse gases, Regulated Emissions, and Energy use in Trans-

Available online 1 February 2013

portation) model is used to analyze the fuel-cycle energy consumption and GHG emissions for hydrogen fuels. Three potential pathways of hydrogen production for FCV application

Keywords:

are examined, namely, steam reforming of natural gas, water electrolysis using grid elec-

Lifecycle analysis

tricity, and water electrolysis using photovoltaic (PV) electricity, respectively. Results show

Fuel economy

that the FCV has the maximum system efficiency of 60%, which occurs at about 25% of the

Fuel cell vehicle

maximum net system power. In addition, the FCVs fueled with PV electrolysis hydrogen

Greenhouse gas emissions

could reduce about 99.2% energy consumption and 46.6% GHG emissions as compared to the conventional gasoline vehicles (GVs). However, the lifecycle energy consumption and GHG emissions of the FCVs fueled with grid-electrolysis hydrogen are 35% and 52.8% respectively higher than those of the conventional GVs. As compared to the grid-based battery electric vehicles (BEVs), the FCVs fueled with reforming hydrogen from natural gas are about 79.0% and 66.4% in the lifecycle energy consumption and GHG emissions, respectively. Copyright ª 2013, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.

1.

Introduction

With zero tailpipe emissions, high fuel efficiency, and less dependence on crude oil the fuel cell vehicles (FCVs) have been regarded as the potential candidate to replace the conventional gasoline vehicles (GVs) [1e6], which could improve greatly urban air quality, climate change, and energy security. In the past decade, major automakers around the world have disclosed their fuel cell vehicles and devoted themselves

to introduce their fuel cell vehicles into the market as quickly as possible, such as F-Cell by Mercedes-Benz [7], FCX Clarity by Honda [8], FCV-R by Toyota [9], and Equinox Fuel Cell by General Motors [10]. The feasibility of driving these candidate vehicles in real-world conditions has been demonstrated by several governmental-supported programs such as the “Controlled Hydrogen Fleet and Infrastructure Demonstration and Validation Project” [11] by US Department of Energy (DOE), and the “Japan Hydrogen Fuel Cell Project (JHFC)” by Japan [12].

* Corresponding author. Tel./fax: þ886 62602205. E-mail addresses: [email protected], [email protected] (J.-J. Hwang). 0360-3199/$ e see front matter Copyright ª 2013, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ijhydene.2012.12.148

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Most of the automakers are planning to enter the fuel cell vehicle market around 2015. Before entering the market, it is important to understand the energy, environmental, and economic impacts of the hydrogen-based transportation system will be as compared with the conventional petroleum-based system. The objective of the present paper is therefore to study the lifecycle performance of an FCV using simulation tools. Focuses are placed on the effects of replacing conventional GVs by hydrogenfueled FCVs on the greenhouse gas (GHG) emission and the total energy consumption. The battery electric vehicles (BEVs) that might be competitive against the FCVs in terms of the benefits in energy conservation and emission reduction are also included for comparison. There have been many simulation/modeling researches on the transient power characteristics of an FCV [13e21]. Fuel-cycle analysis of GHG emission and energy consumption of transportation fuels has also been studied extensively [22e27]. However, few studies evaluate the lifecycle performance of an FCV by simultaneously analyzing the fuel economy of an FCV and the fuelcycle emission and energy consumption of various hydrogen pathways. In the present work, first, the multi-domain dynamic simulation platform Matlab/Simulink is employed to analyze the transient behaviors of the FCV powertrain. Mathematical formulations are derived and proper empirical correlations are developed to model the major components of the FCV, such as a proton exchange membrane (PEM) fuel cell stack, a lithium-ion battery, an electrical motor, and a vehicle chassis system. Then, linking these components on the Matlab/Simulink platform creates an accurate simulation model that predicts the real-time behaviors of the system. Moreover, scoping and analyzing the system dynamic behaviors using the power trace technique against the typical drive cycle of NEDC (New European Drive Cycle) to determine the system efficiency and fuel economy of the FCV. In general, building prototypes is very time consuming and cost ineffective in early stages of the development of an FCV. The present work using dynamic simulation techniques to determine the fuel economy of an FCV would be beneficial in cost and time saving in realizing an FCV. Subsequently, a comprehensive analysis of fuel-cycle energy consumption and GHG emissions for various hydrogen pathways is conducted using the GREET (Greenhouse gases, Regulated Emissions, and Energy use in Transportation) code. Three potential hydrogen pathways for FCV applications, namely, steam reforming of natural gas, water electrolysis using grid electricity, and water electrolysis using photovoltaics (PVs), are discussed and compared thereafter. In conjunction with the fuel economy and fuelcycle analysis, the lifecycle performance in energy consumption and GHG emission of the hydrogen-based transportation system could be obtained, which would enrich the information available for the public, industry and government to make well-informed decisions.

2.

series type [2,13]. Note that the assumptions of the fuel cell/ battery models (e.g. heat transfer effects, water removal, variation of humidity, internal resistances, self-discharge, the capacity, discharge and charge characteristics etc.) have been described in detail authors’ previous work [21] and are not repeated again in the following discussion.

2.1.

Fuel cell model

The equivalent circuit of the PEM fuel cell is shown in Fig. 1, which uses hydrogen and air as its fuel sources. The rates of conversion (utilizations) of hydrogen ðUfH2 Þ and oxygen ðUfO2 Þ are determined in Block A as follows: UfH2 ¼

UfO2 ¼

nrH2 nin H2 nrO2 nin O2

¼

60; 000RTNifc zFPfuel VlpmðfuelÞ x%

(1)

¼

60; 000RTNifc 2zFPair VlpmðairÞ y%

(2)

where Pfuel is absolute supply pressure of fuel (atm), Pair the absolute supply pressure of air (atm), Vlpm(fuel) the fuel flow rate (l min1), Vlpm(air) the air flow rate (l min1), x the percentage of hydrogen in the fuel (%), y percentage of oxygen in the oxidant (%), N the number of cells, and z the number of moving electrons. The partial pressures of the hydrogen ðPH2 Þ, oxygen ðPO2 Þ, and product water vapor defined by the parameters applied to Block B, are shown in the following equations [14,15]:
 PH2 ¼ 1  UfH2 x%Pfuel

(3)


PH2 O ¼ w þ 2y%UfO2 Pair

(4)


PO2 ¼ 1  UfO2 y%Pair

(5)

Model of fuel cell vehicle

The present models to simulate the subsystems of an FCV include the PEM fuel cell, the lithium-ion battery, the electrical motor, and the vehicle chassis. The FCV powertrain is of the

Fig. 1 e Equivalent circuit of the PEM fuel cell.

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where PH2 O is the partial pressure of water vapor inside the stack (bar) and w is the percentage of water vapor in the oxidant (%). Then, from the partial pressures of gases and the Nernst voltage (En), the values of the open circuit voltage (Eoc) and the exchange current (i0) can be calculated as the following equations: Eoc ¼ Kc En      DG zFk PH2 þ PO2 e RT i0 ¼ Rh

(6)

(7)

where Kc is the voltage constant at nominal operation conditions, k the Boltzmann’s constant [J K1], and h the Planck’s constant [J s]. As for the Tafel slope b of the output of Block C, it could be calculated by the following equation: b¼

RT zaF

(8)

The parameters a, DG and Kc are calculated based on the polarization curve at nominal operation conditions along with some additional parameters, such as the stack efficiency, composition of fuel and air, supply pressures and temperatures. The nominal rates of conversion of gases are therefore calculated as follows. UfH2 ¼

UfO2 ¼

hnom Dh0ðH2 OÞg N zFVnom 60; 000RTnom NInom 2zFPairnom VlpmðairÞnom $0:21

(9)

(10)

where hnom is the nominal efficiency (%), Dh0ðH2 OÞg the enthalpy of water vapor (J mol1), Vnom the nominal voltage (V), Inom the nominal current (A), Vlpm(air)nom the nominal air flow rate (l min1), Pairnom the nominal absolute pressure of air (Pa), and Tnom the nominal operating temperature (K). From these rates of conversion, the nominal partial pressures of gases and the Nernst voltage can be derived. With Eoc, i0 and b known and assuming that the stack operates at constant rates of conversion or utilizations at the nominal conditions, a, DG and Kc can be determined. If there is no fuel or air at the stack input, it is assumed that the stack is operating at a fixed rate of conversion of gases (nominal rate of conversion), that is, the supply of gases is adjusted according to the current so that they are always supplied with just a bit more than needed by the stack at any load. Actually, the maximum current that the stack can deliver is limited by the maximum flow rates of fuel and air that can be reached [28,29]. Beyond that maximum current, the voltage output by the stack decreases abruptly as more current is drawn [16e18]. In the present work, the fuel cell stack of the maximum power of 100 kW has 400 cells in series. Other parameters of the fuel cell stack are summarized in Table 1.

2.2.

Table 1 e Parameters of the PEM fuel cell stack. Parameters

Value

Stack power

Nominal Maximum

Fuel cell resistance Nernst voltage

Unit

85.5 100

kW kW

0.17572 1.1729

U V/cell

Nominal utilization

Hydrogen Oxidant

95.24 50.03

% %

Nominal consumption

Fuel Air

794.4 1891

lpm lpm

0.024152 1.1912 99.95 21

A

Exchange current Exchange coefficient Fuel composition Oxidant composition

e % %

Fuel flow rate at nominal hydrogen utilization

Nominal Maximum

374.8 456.7

lpm lpm

Air flow rate at nominal oxidant utilization

Nominal Maximum

1698 2069

lpm lpm

368 3 3

K bar bar

System temperature Fuel supply pressure Air supply pressure

charging, the electron flows from the outside of the charger to the carbon material of the cathode; while the lithium ion of anode material leaves the anode, and passes through the electrolyte solution before entering the cathode. Conversely, when the lithium-ion battery discharges, the electron and the lithium-ion take the opposite directions. The equivalent circuit of the lithium-ion battery is shown in Fig. 2. In this figure, Ebatt is the nominal voltage (V), E0 is the standard voltage (V), Exp(s) is the exponential dynamic voltage, and Sel(s) is the battery mode (when discharging, Sel(s) ¼ 0; when charging, Sel(s) ¼ 1). The charge/discharge model for the lithium-ion battery [19,20] can be described as follows. Discharge model (i* > 0): Echarge ¼ E0  Eb $

Q Q $i  Kb $ $it þ A$eðB$itÞ Q  it Q  it

Charge model (i* < 0) Edischarge ¼ E0  Kb $

Q Q $i  Kb $ $it þ A$eðB$itÞ 0:1$Q þ it Q  it

Lithium-ion battery model

In the present work, a 13.9 Ah, 288 VDC, and 25 kW lithium-ion battery serves as not only the secondary power source to propel the FCV but also an energy buffer to store the energy from the regenerative brake. When the lithium-ion battery is

(11)

Fig. 2 e Equivalent circuit of the lithium ion battery.

(12)

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where Kb is the polarization constant (V (Ah)1) or polarization resistance (U), i* is the current of low-frequency dynamic (A), it is the available capacity (Ah), Q is the maximum battery capacity (Ah), A is the exponential voltage (V), and B is the exponential capacity ((Ah)1). The battery state-of-charge (SOC) can be formulated as the following equation. 0 1 Zt 1 SOC ¼ 100@1  iðtÞdtA (13) Q 0

The battery management system maintains the SOC between 35% and 80%. Also, it prevents against voltage collapse by controlling the power required from the battery.

2.3.

Electrical motor model

In the present model, a 288 VDC/100 kW interior permanent magnet synchronous motor (PMSM) is employed to propel the fuel cell vehicle. It has eight pole and the magnets are buried (salient rotor’s type). A flux weakening vector control is used to achieve a maximum motor speed of 12,500 rpm. The dynamics of PMSM are simulated with sinusoidal or trapezoidal back-EMF, which has been described in detail elsewhere [21], and is not elaborated on here due to space limitation. Note that the PMSM can be operated as either a generator or a motor. The mechanic torque determines the operating modes. If the torque is positive, it is the motor mode; if the torque is negative, it is the generator mode.

2.4.

Vehicle dynamic model

The vehicle dynamics of the FCV represent the motion influence on the overall vehicular system, which are simulated based on Newton’s second law of motion. The forces of vehicle driving include air drag, rolling resistance, accelerating force and climbing force. The driving force of the FCV is the sum of these forces and the power needed in vehicle driving can be written as   1 (14) Pd ¼ ma þ CR mg þ mg sin q þ ra CD AF v2 v 2 where Pd is the power needed by vehicle, m the total mass of vehicle, a the acceleration of vehicle, CR the coefficient of rolling resistance, g the gravity constant, q the angle of gradient, r the density of air, CD the drag coefficient, AF the formal area of vehicle. On vehicle driving, there are power losses in many vehicle components. The required power from the PEM fuel cell and the lithium battery should consider the above losses as well as the auxiliary power for cooling, air supply, fuel supply, headlights etc.

3.

Hydrogen production model

Hydrogen should be produced using other primary energy sources. Globally, about 49% hydrogen is produced from natural gas, primarily via steam methane reforming (SMR), 29% is produced from oil, most of which is consumed in petroleum refineries, 18% is produced from coal, primarily for the manufacture of ammonia, and the remaining 4% is via water electrolysis [30]. That is more than 95% hydrogen is still

derived from fossil fuels in the world. Consequently, in a hydrogen-based FCV, the majority of GHG emissions and energy consumption would occur before the hydrogen reaches the fuel cells. Therefore, it should carefully consider the lifecycle performance of hydrogen supply system in the FCV application. One of the objectives of this paper is to perform an assessment of lifecycle (also as known as well-to-wheels, WTW) GHG emissions and energy consumption for the FCV [22e27]. In the present work, the GREET (Greenhouse gases, Regulated Emissions, and Energy use in Transportation) model developed by Argonne National Laboratory is employed to examine the lifecycle energy and emission of FCVs along with hydrogen fuels. The probability-based distribution functions are developed to describe energy consumption and GHG emissions for individual operations in fuel production and transportation processes, as well as vehicle operations. Three kinds of GHGs are combined together with their global warming potentials (GWPs) to calculate CO2-equivalent GHG emissions, i.e., 1 for CO2, 23 for CH4, and 296 for N2O, which are recommended by the Intergovernmental Panel on Climate Change (IPCC) for the 100-year time horizon [31]. Other details of the GREET model could be find elsewhere [22e24] and is not elaborated on here. As shown in Fig. 3, both renewable and non-renewable hydrogen production options are explored in the present work, including steam reforming of natural gas, water electrolysis using grid generation, and water electrolysis using PV. In addition, the fuel pathways of petroleum-to-gasoline for GV applications and fuel mix-to-electricity for BEV applications are included for comparison. Table 2 lists fuel pathways for various fuel/vehicle systems investigated in this study. As further depicted in Fig. 3, the WTW analysis could be divided into three typical stages, i.e., the feedstock stage, the fuel stage, and the vehicle operation stage. The feedstock together with fuel stages is called as the well-to-pump (WTP) stage, while the vehicle operation is called as the pump-towheels (PTW) stage. Consequently, combining the WTP stage and the PTW stage becomes the lifecycle energy and emission for FCVs. Note that the present study considers the operationrelated energy and emissions only. That is, the energy and emissions related to operational activities for fuel processes and vehicles are included. On the other hand, those of infrastructure-related energy consumption and GHG emissions, such as energy and emissions associated with building roads, plants, and plant equipment, are not included for any of the pathways evaluated.

3.1.

Hydrogen from natural gas

Fig. 4 shows a process diagram of the hydrogen production via natural gas reforming. First, the feedstock is desulfurized by hydro-desulfurization to reduce the sulfur levels to protect catalysts used in the downstream reforming process. Then, SMR reaction is carried out to convert methane and steam to a hydrogen rich reformate stream within a compact furnace at 800e900  C temperature and 25e35 bar pressure in the presence of a nickel catalyst according to the following reactions: CH4 þ H2O / CO þ 3H2

(15)

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Fig. 3 e Stages of vehicle/fuel systems covered in the present model.

The above reaction is an endothermic process and the thermal energy required is obtained by the combustion of fuel gas and purged off gas from the pressure swing adsorption (PSA) unit [32,33]. Following the reforming step the syngas is cooled and fed into the water-gas shift reactor (WGS) to produce additional hydrogen. CO þ H2O / CO2 þ H2

Then, the hydrogen is purified by means of the PSA unit consisting of several vessels filled with selected adsorbents. It reaches hydrogen purities higher than 99.99% by volume and CO impurities of less than 1 ppm fulfilling the fuel

(16)

Table 2 e Fuel pathways associated with the vehicle technologies. Feedstocks Petroleum Fuel complexity Natural gas Fuel complexity Solar

Fuel processes

Fuels

Vehicles

Refinery Combustion, nuclear reaction. Reforming Electrolysis Electrolysis

Gasoline Electricity

GV BEV

H2 H2 H2

FCV FCV FCV

Fig. 4 e Simplified block flow diagram for hydrogen from natural gas.

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specifications for FCV applications. Table 3 shows the matrices of energy efficiency of hydrogen production via natural gas reforming at the distributed station, including natural gas recovery, natural gas processing, natural gas reforming, and hydrogen compression. It is interesting to note that a large-scale SMR unit could offer a higher efficiency than a small one. For moderate levels of hydrogen demand with a low geographical concentration, distributed production from natural gas could be cheaper than the large-scale centralized production, because it does not require an extensive transportation and distribution infrastructure. Therefore, distributed natural gas reforming is likely to be a proper option to supply hydrogen during the early market introduction phase. Actually, some small-scale reformers are currently being used in demonstration refueling stations for automotive applications [34e36].

3.2.

Hydrogen from electrolysis

Electrolysis is a mature process that splits water into hydrogen and oxygen using electricity. It opens the door to hydrogen production from any primary energy source that can be used for electricity generation. Since the transmission and distribution system is already extensive, electricity can readily be transmitted to refueling stations. Thus, this pathway helps avoid long-distance transportation and storage of hydrogen. In particular, if hydrogen will eventually become an FCV’s fuel, this pathway could help overcome inadequate hydrogen distribution infrastructure in the early stage of FCV introduction and in areas outside of major metropolitan areas. Currently, three kinds of electrolytic technologies have been widely considered for hydrogen production, namely the alkaline electrolyzer that uses potassium hydroxide (KOH) solutions [37,38], the solid polymer electrolyte electrolyzers [39,40], and the ceramic oxide electrolyzer [41,42]. Among them, alkaline electrolysis is the most established industrial technology. The maximum electrolysis efficiency is about 85% theoretically, but current electrolysis is in general less efficient. Some sources suggest an efficiency of 63.5% (LHV) for the decentralized electrolyzer of capacity 20 kgH2 h1 (or 120 cars a day), including auxiliary loads other than compression [43,44]. Table 4 gives the assumption of the electrolysis efficiency in the current model. This difference in electrolytic efficiency between the present model (71.5%) and the previous works (63.5%) may be explained by differences in working and test conditions. The fuel sources for electricity generation symbolize the key factor for determining energy use and GHG emissions of electrolysis hydrogen. Two kinds of electricity for electrolysis hydrogen are discussed in the present work, i.e., grid

Table 3 e Energy efficiencies for natural gas to hydrogen pathways. Stages NG recovery NG processing GH2 compression: electric compressor GH2 production (reforming)

Efficiency 97.5% 97.5% 94.0% 70.5%

Table 4 e Energy efficiencies from electrolysis to hydrogen pathways. Stages

Efficiency

H2 production at refueling stations (electrolysis) H2 compression (for GH2): electric compressor H2 compression (for GH2): NG compressor

71.5% 94.0% 86.0%

electricity and solar electricity. Tables 5 and 6 respectively show the typical fuel share of and the generation efficiency of the various fuels in Taiwan [45]. According to the data shown in the tables, the model calculates the carbon intensity (gCO2e kWh1) of the grid electricity for hydrogen production by water electrolysis. The transmission loss of grid electricity is assumed loss to be 5% for electricity delivered to refueling stations. If the solar electricity is used without grid backup, the electrolysis presents non-carbon-emitting hydrogen production. In the present calculations, the conversion efficiency from renewable energy sources to electricity is assumed to be 100% because, for renewable sources, resource consumption is not a concern, and there are not any process fuel combustion emissions. However, in terms of practical implementation, since solar panels have around 15% conversion efficiency, it seems that space limitations would restrict the number of FCVs that could ultimately be fueled via PV electrolysis.

4.

Results and discussion

4.1.

Simulated drive cycle

Drive cycle simulations of vehicle models is an important tool for design in driveline and control strategies. The main purpose in powertrain design is to minimize fuel consumption and component costs, while maximizing drivability. Another aim to use drive cycle simulations in the design is emission legislations. Since it is very time consuming and cost ineffective to build prototypes of FCVs in early stages of the development, drive cycle simulation has become a necessary tool in powertrain design and control system development. A drive cycle is a speed profile where speed is defined as a function of time. Several drive cycles have been developed by governments around the world as a tool for vehicle certification. Examples of such cycles are the NEDC (New European Drive Cycle) in Europe [46] and the FTP (Federal Test Procedure)-75 in USA. In the present work, NEDC is employed to

Table 5 e Fuel mix for average electric generation in Taiwan. Sources Coal Oil Natural Gas Nuclear Power Biomass Other Total

Amount

Share

123,969 GWh 13,367 GWh 48,364 GWh 40,827 GWh 589 GWh 3437 GWh 238,326 GWh

49.9% 3.8% 24.6% 16.9% 1.5% 3.4% 100%

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Table 6 e Energy efficiencies for fuel mix-to-electricity in Taiwan. Items Residual oil utility boiler efficiency NG utility boiler efficiency NG simple cycle turbine efficiency NG combined cycle turbine efficiency Coal utility boiler efficiency Electricity transmission and distribution Loss

Efficiency 34.8% 34.8% 33.1% 55.0% 34.1% 8.0%

speed has well met the speed required by the EUDC cycle. In general, the energy management subsystem (EMS) determines the reference signals for the electric motor drives, the fuel cell system and the DC/DC converter in order to distribute accurately the power from the two electrical sources. These signals are calculated using mainly the position of the accelerator, which is between 100% and 100%, and the measured FCV speed. Note that a negative accelerator position represents a positive brake position.

4.2. evaluate the fuel economy and the GHG emission of the FCVs. As shown in Fig. 5(a), the NEDC is composed of four ECE-15 segments followed by a EUDC (Extra-Urban Drive Cycle) segment. Each ECE-15 cycle is 195 s and 1.013 km long and is intended to resemble urban driving as it typically is in metropolitan areas in Europe such as Rome or Paris. The EUDC cycle is 400 s and 6.955 km long and is intended to resemble extra urban driving. Fig. 5(b) shows the corresponding pedal positions of the FCV to the EUDC cycle, in which actual vehicle

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Model validation

Before the FCV simulation, the BEV-version simulation results are provided for validating the performance of the simulation model. The model parameters of the BEV based on the 2011version Nissan Leaf are listed in Table 7. Fig. 6 shows the simulation results of the dynamics of the battery power and SOC of the BEV over the NEDC driving cycle. It is seen from this figure that the battery power is highly fluctuated during the NEDC cycle. The power is delivered from/to the battery when

Fig. 5 e Input of the dynamic simulation (a) New European Drive Cycle (NEDC) and (b) pedal positions (accelerator) for the EUDC cycle.

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Table 7 e Parameters of BEV (Nissan Leaf) for model validation.

Table 8 e Comparison of modeling simulations and manufacture data of BEV (Nissan Leaf).

Components

Parameters

Sources

Li-ion battery

Nominal voltage Rated capacity Maximum capacity

288 VDC 84 Ah 84 Ah

Motor

Rated voltage Rated power Maximum speed Number of pole

288 VDC 100 kW 12,500 rpm 8

Values

Present model

the BEV is accelerated/decelerated. As for the SOC distribution, it has a general trend of decrease over the NEDC cycle. Detailed inspection of this figure reveals that the SOC increases slightly as the BEV is decelerated due to power regeneration by braking. Table 8 further shows the simulated results of fuel efficiencies of the BEV under NEDC driving cycle. The data from the manufacture are also listed for comparison. It is seen from this table that the simulated fuel efficiencies for ECE-15 and EUDC cycles are 0.210 kWh km1 and 0.200 kWh km1, respectively. Consequently, the simulated fuel efficiency over the NEDC cycle is 0.204 kWh km1. On the other hand, based on the data reported by US Environmental Protection Agency (EPA), the fuel efficiencies of Nissan Leaf are 0.196 kWh km1 and 0.225 kWh km1 for city and highway modes, respectively. Accordingly, the fuel efficiency of the combined mode is 0.211 kWh km1. Therefore, the discrepancy of fuel efficiency between the simulation results and the manufacture data is less than 3.4%. The good agreement in the above comparison has verified the availability and reliability of the present simulation model.

4.3.

Vehicle dynamics

The simulation results of the FCV applying the EUDC drive cycle are shown in Figs. 7e9. Fig. 7(a)e(c) shows the power dynamics of the FCV over the EUDC cycle. The power management system controls the reference power of the electrical motor by splitting the power demand as a function of the

81

30

Battery 25

79

20

78

15

77

10

76

5

75

0

74

-5

73

-10

72

-15

71

-20

70

-25

69 0

100

200

300

400

500

600

700

800

900

1000

1100

-30 1200

Time (s)

Fig. 6 e Power dynamics and SOC of the BEV under the NEDC cycle.

Power (kW)

SOC (%)

SOC 80

Cycle/mode ECE-15 cycle

NEDC cycle

Range Average speed Top speed Consumption power Fuel efficiency Range Average speed Top speed Consumption power Fuel efficiency Fuel efficiency

City mode Highway mode Combined mode

Fuel efficiency Fuel efficiency Fuel efficiency

EUDC cycle

US EPA data

Parameters

Values 1.013 km 18.42 km h1 49.95 km h1 0.213 kWh 0.210 kWh km1 6.995 km 62.97 km h1 120.01 km h1 1.4 kWh 0.200 kWh km1 0.204 kWh km1 0.196 kWh km1 0.225 kWh km1 0.211 kWh km1

available power of the lithium-ion battery and the PEM fuel cell. As shown in Fig. 7(a), the power dynamics have demonstrated different operating modes of the FCV over the drive cycle, e.g., accelerating, cruising, and recharging the battery while decelerating or braking. In general, the hybrid power output from the PEM fuel cell and the lithium-ion battery are closely matched with the power required by the electrical motor. During the acceleration period, the power discharged from the lithium-ion battery compensates for the transient inability of the fuel cell to provide sufficient power for the system. In contrast, during the periods of low power and/or slowdown, the lithium-ion battery stores the extra power provided by the PEM fuel cell. Fig. 7(b) puts the attention in the period of the startup of the FCV. In the first 20 s, the FCV keeps idle with zero pedal position and the lithium-ion battery is steadily charged by the PEM fuel cell. At t ¼ 20 s, the accelerator pedal starts being pushed and the FCV begins moving. Due to the nature of slow dynamics, the fuel cells could not catch up the dynamics of the motor when the power increases sharply. Therefore, the lithium-ion battery takes charge of the dynamics of the motor power between 20 and 22 s. At t ¼ 25 s, the accelerator pedal is released to zero and thus the motor power decreases abruptly. Similarly, the fuel cell could not decrease its power promptly over the response time. The excess power from the fuel cell is therefore charged into the lithium-ion battery (between 25 and 27 s) in order to maintain the required torque of the FCV. Focus is then turning to the dynamics of brake regeneration shown in Fig. 7(c). It is seen that the FCV brakes in the period of 110e120 s, with the pedal position of 9% (Fig. 7(a)). In this circumstance, the lithium-ion battery power becomes negative and its SOC increases. Actually, the motor acts as a generator driven by the vehicle’s wheels. The kinetic energy of the FCV is therefore transformed in electrical energy that is stored in the lithium-ion battery. Meanwhile, it also receives some power from the PEM fuel cell. At this moment, the required torque cannot be met anymore. Fig. 8 shows the dynamics of the two kinds of energy storages in an FCV under the EUDC cycle, i.e., hydrogen

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Fig. 7 e (a) Power dynamics of the FCV under the EUDC cycle, (b) acceleration in the first 50 s, (c) regenerative braking between 110 s and 120 s.

consumption rate and battery state of charge. It is clearly seen that the hydrogen consumption rate is closely related to the power delivery by the fuel cell shown in Fig. 7(a). It is interesting to note that there are a series of sharp dips of the

dynamics of hydrogen consumption rates between 20 and 50 s, which are in response of the variation of accelerator pedal positions shown in Fig. 5. As for the dynamics of the state of charge of the lithium-ion battery, it decreases as the

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Fig. 8 e Dynamics of hydrogen consumption rate and state of charge of the lithium ion battery for the EUDC cycle.

FCV either increases its speed or is cruising. Conversely, the lithium-ion battery increases its SOC when the FCV brakes to decrease its speed.

4.4.

Stack and vehicle efficiencies

Fig. 9 shows the real-time efficiencies of the fuel cell stack under the EUDC cycle. The efficiency of the fuel cell stack is defined as the ratio of the stack gross power to the power consumption by the input hydrogen:

3 stack

¼

Pstack IV ¼ _ H2  Dh _ H2  Dh m m

(17)

where V (V) and I (A) are the voltage across the fuel cell stack _ H2 and the current passing through the stack, respectively. m (mol s1) is the hydrogen consumption rate. The dashed blue curve and the green curve represent the results based on the lower heating value (LHV) and the higher heating value (HHV), respectively. During the driving cycle, both the LHV and HHV efficiencies are strongly dependent on the power delivered by the fuel cell stack (Fig. 7(a)), rather than the FCV speed.

Fig. 9 e Dynamics of stack efficiency during the EUDC cycle.

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Fig. 10 e Effect of net system power on the fuel cell system efficiency.

In general, the efficiency of the fuel cell stack is fluctuated when the stack power changes. A fuel cell system consists of many balance-of-plants (BOPs) such as cooling fans, water pumps, and air blowers. Part of the power from the fuel cell stack should be used to

Table 9 e Comparison of fuel economies of various types of vehicles. Type Brand Fuel economy

Fuel cell vehicle

Gasoline vehicle

Battery electric vehicle

Present work 65 MPGE

Toyota Camry 26 MPGE

Nissan Leaf 99 MPGE

drive these BOPs. In addition, the power conversion using DC/DC converters to regulate the voltage of the fuel cell when the output current changes results in some power losses. That is the net power to propel the electric motor of the FCV should subtract these auxiliaries from the gross stack power. Therefore, the system efficiency of an FCV is defined as the ratio of the DC input power to the electrical motor Pm to the hydrogen enthalpy flow rate. 3 system

¼

Pm _  Dh m

(18)

Fig. 10 shows the relationship between the system efficiency and the relative net system power. It is seen from this figure that the system efficiency is very low at the low relative net system power conditions, because of a large portion of

Fig. 11 e Comparison of energy consumption and GHG emissions for various hydrogen production ways (per kilogram hydrogen).

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Fig. 12 e Well-to-pump energy efficiencies of various fuel pathways for vehicular applications.

gross power used for the operation of BOPs. In contrast, the system efficiency is high at the medium relative net system power. The optimal system efficiency is about 60%, which occurs at about 25% full net system power. At the high net system power conditions, the system efficiency is again low because the efficiency of the fuel cell stack decreases as the fuel cell current increases. The fuel economy is a key parameter in determining the lifecycle efficiencies of an FCV. In the present study, the fuel economy of the FCV is determined by the ratio of the driving

distance to the amount of the hydrogen consumption during the NEDC cycle, resulting a fuel economy of the present FCV is 65 MPGE. As shown in Table 9, the fuel economies of the commercial GV (Toyota Camry A-S6) and BEV (Nissan Leaf) are 28 MPGE and 99 MPGE, respectively. The significantly higher fuel economy of the BEV than that of the GV is because the energy efficiency of the electrical motor is higher than that of the gasoline engine. The lower fuel economy of FCVs as compared to that of BEVs is attributed to the electrification loss caused by the electrochemical reaction in the fuel cell system.

Fig. 13 e Relative changes of lifecycle energy consumption and GHG emissions to the GVs for BEVs and FCVs.

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4.5.

WTP efficiency

Fig. 11 shows a comparison of energy consumption (MJ kgH1 2 ) ) among the three hydrogen and GHG emission (kgGHG kgH1 2 pathways, i.e., reforming hydrogen from natural gas, electrolysis hydrogen using grid electricity, and electrolysis hydrogen using solar electricity, respectively. It is seen from this figure that the hydrogen production from the water electrolysis using grid electricity consumes the most energy and simultaneously produces the largest GHG emissions among the three hydrogen pathways. In contrast, the hydrogen production from PV electrolysis has the least total energy consumption as well as GHG emissions. Fig. 12 further compares the WTP efficiency among the three hydrogen pathways. The results for the conventional gasoline and the grid electricity are also displayed for comparison. It is seen from this figure that the fuel pathway of gasoline from petroleum refinery for GV applications has the highest WTP efficiency, typically as high as 80%. The WTP efficiency of grid electricity for charging the BEVs is 36.4%, which is significantly less than that of gasoline from petroleum refinery. Among the three hydrogen pathways, the PV electrolysis has the highest WTP efficiency (63.3%), while the water electrolysis using grid electricity has the lowest WTP efficiency (25%). The latter case is subject to two significant efficiency losses of electricity generation and water electrolysis.

4.6.

Change in total energy and GHG emissions

Fig. 13 shows the relative changes in the lifecycle energy consumption and GHG emissions of FCVs as compared to the baseline case of GVs. The results of BEVs are also included in this figure for comparison. It is clearly seen from this figure that the FCVs fueled with electrolysis hydrogen using grid electricity suffer for the increases in the lifecycle energy consumption by 30.7% and the increase in the lifecycle GHG emissions by 48%. In contrast, the FCVs fueled with hydrogen from PV electrolysis have the most promise in the energy conservation and the GHG emission reduction. It reduces the total energy consumption and the GHG emissions up to 47.1% and 91.6%, respectively. It is interesting to note that the FCVs fueled with reforming hydrogen from natural gas, which would be the primary hydrogen production method in the initial stage of the hydrogen economy [47], perform better than the BEVs. Their lifecycle energy consumption and GHG emissions are only 79.0% and 66.4% of those of the BEVs, respectively.

5.

Conclusions

Combining the fuel-economy analysis of an FCV using the MATLAB/Simulink tool and the fuel-cycle analysis of hydrogen production using the GREET code has successfully assessed the lifecycle performance of energy consumption and GHG emissions of a hydrogen-fueled FCV. Three potential hydrogen production options for FCV applications are discussed, namely steam reforming of natural gas, water electrolysis using grid generation, and water electrolysis using photovoltaics. Main findings from the simulation results are concluded below.

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1. The hybrid-power dynamics of the FCV have clearly revealed that the power from the lithium-ion battery could compensate for the transient inability of the fuel cell to provide sufficient power for the system during the acceleration period. Conversely, it could store the extra power from the fuel cell during the slowdown periods. 2. The maximum system efficiency of the FCV is about 60%, which occurs at about 25% of the maximum net system power. 3. The simulated fuel economies of FCVs and BEVs are about 65 MPG and 99 MPG, respectively. 4. The FCVs fueled with solar electrolysis hydrogen show the greatest capability for minimizing negative energy and environmental impacts, which reduce about 46.6% total energy consumption and 99.2% GHG emissions as compared to the conventional GVs. Conversely, the lifecycle energy consumption and GHG emissions of the FCVs fueled with grid electrolysis hydrogen are 35% and 52.8% respectively higher than those of the conventional GVs. 5. The FCVs fueled with reforming hydrogen from natural gas, which is likely to be the major hydrogen production scheme for initial introduction of FCVs, perform better than the BEVs in the lifecycle energy consumption and GHG emissions, typically only about 79.0% and 66.4%, respectively.

Acknowledgments The author Professor Jenn Jiang Hwang would like to thank the National Science Council of Taiwan, for financially supporting this research under contract no. NSC 98-2221-E-024-015-MY2.

Appendix A. Supplementary material Supplementary material associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j. ijhydene.2012.12.148.

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