TY - JOUR
AU1 - Gupta,, Pralhad
AU2 - Zhuge,, Weilin
AU3 - Luo,, Sijie
AU4 - Ma,, Fanhua
AB - Abstract Increasing urban air pollution, greenhouse gases, and declining fossil energy sources are the three major problems of transportation sector which drive the use of alternative vehicular fuels to prevent energy shortage, reduce oil dependency and decrease tailpipe emissions including air pollutants and greenhouse gas emissions. This research work focused on life cycle analysis of HCNG® heavy-duty vehicle in which 20% gaseous hydrogen blended with compressed natural gas has been investigated in terms of net energy ratio, GHG value, and cost-effectiveness over a scale of ‘per MJ energy output’ in two fuel options, i.e. 0%HCNG and 20%HCNG for an entire well-to-wheel cycle. An engineering economic approach has been used to evaluate cost-effectiveness ratio of CNG and 20%HCNG pathways derived from fuel economy improvement. It has been shown that at pump-to-wheel stage, 7% reduction in fuel consumption can be achieved together with 11% reduction in GHGs, 7% reduction in energy consumption at operation and 7% reduction in total costs (RMB/MJ) for 20%HCNG compared with CNG. Rank (1 means ‘best and 10 means ‘worst’) showed that renewable-based hydrogen pathways such as solar, wind and biomass showed dual benefits of lower energy consumption and lower GHG emissions whereas grid electricity-to-hydrogen displayed the worst case in both scenarios. Usually, biomass-based HCNG pathways may have higher net energy ratio, but the sources are cleaner, and renewable in nature. The energy efficiency of fossil-based pathways such as natural gas, coal, etc., is higher than biomass gasification pathway. 1 INTRODUCTION 1.1 Research motivation The concept of using hydrogen enhanced compressed natural gas fuel for HDVs has been well understood by many researchers and scientists. Although HCNG® is comparatively more advantageous than conventional CNG in terms of performance and cleaner emissions, the large scale application has not been achieved till now. One of the major barriers to large scale marketization of HCNG is immaturity of ‘hydrogen economy’ which encompasses hydrogen production, storage, T&D and HCNG fuelling infrastructure. Therefore, to get the deep insight to HCNG technology, life cycle analysis is an effective choice. The well-to-wheel study of HCNG established on a typical life cycle approach provides an extension to the demonstration projects and test bench experiments of Hythane® ⁠. 1.2 Previous works Lyu et al. [1] stated that automotive energy is one of the core areas of environmental and climatic issues and is still challenging in overall spectrum which includes technology, economics and sustainability at present. Ou et al. [2] claimed that compared to conventional diesel buses, AFVs offer dual benefits of energy saving and GHG reduction. Hydrogen is a zero-emitter fuel with a very fast laminar burning speed and extended limit of flammability which ensures possibilities to enrich CNG with hydrogen [3]. The transient performance and emission analysis of 20% HCNG by the author [4] demonstrated that 20% hydrogen enrichment decreases NOx ⁠, CO, NMHC, CH4 and BSFC by 51%, 36%, 59%, 47% and 7%, respectively, whereas the maximum power remains the same. [5] showed that 5% HCNG enhanced the energy consumption by 4%, 25% HCNG produced the largest reduction of CO2 by 25% compared with baseline CNG (HDB) operation. The levels of HC emission were constant and could not meet the EEV limits. NOx reduced by 47% with 5% HCNG ([5]). A chassis dynamometer test bench experiment performed on a passenger car with L4-SI (PFI) engine equipped with a TWC as per NEDC cycle procedure showed that CO and CO2 was reduced by 19% and 3% respectively whereas HC was almost unchanged. The amount of NOx increased by 70%. No significant changes occurred in fuel consumption on both basis; mass and energy. Significant benefits from HCNG can only be achieved with an appropriate hydrogen ratio and spark timing [6]. Hydrogen fraction, spark timing, and excess air ratio and are the important operating parameters of HCNG. Development of sufficient control system for the HCNG engine is necessary to maintain a balance between maximum performance and minimum exhausts. Development of an infrastructure supporting HCNG use is another big challenge [7]. Kilgus [8] has claimed that no significant changes in the performance trends of modified HCNG 15% and HCNG 30% occurred compared with baseline CNG vehicle. However, the corresponding fleet of hydrogen vehicles would have the best environmental performance. Nelsson et al. [9] revealed that if the objective is only CO2 reduction, HCNG may not be an effective choice as it contributes to only 10% reduction in CO2 emissions which is easy to achieve just by switching CNG to biogas in various parts of the world. Lastly, this study also proved that HCNG is best suited for the primary goal of air pollution reduction rather than CO2 reduction [9]. Huang and Zhang [10] disclosed that NG-based hydrogen pathway showed the highest WTW energy efficiency and the electricity-to-hydrogen-based pathways showed the poorest characteristics. The well-to-wheel energy efficiency of naphtha-to-hydrogen and coal-to-hydrogen lies between WTW energy efficiency of natural gas SMR and electricity-to-hydrogen pathways. According to Ewan and Allen [11], solar PV seems to have tremendous prospects as a large scale electricity producer which can provide a platform for hydrogen economy but costs of PV technology is a major concern. He also added that biomass is a low carbon intensive technology, direct water splitting solar photo catalysis pathway can be more effective if continuous researches can increase its conversion efficiency to a larger extent. Bartels et al. [12] claimed that with the pace of technological advancement of alternative technologies, fossil fuels are becoming costlier than alternative sources which clearly is expected to dominate the energy mix in future [12]. Turner [13] proposed that building a larger number of hydrogen production stations with lower capacity is more cost-effective than building a small number of large scale central production sites. Higher volume or mass production supports costs savings. The author also suggested not to spend a tremendous amount of money for CO2 sequestration rather he emphasized to limit the use of coal in both gasification and electricity generation and leave them unused which is very challenging because of its availability and economic competitiveness [13]. Life cycle cost analysis is defined as the methodical estimation of costs of a system or a product for its full life duration Márquez, Márquez [14]. DeCicco and Ross [15] proposed an engineering-economic analysis for a fleet of cars with a set of technological improvements focused on fuel economy optimization. In his work, he also mentioned about estimation of fuel saving payback and cost of conserved energy average. The author also explained the significance of fuel economy improvement in vehicle stock turnover in the form of gasoline consumption reduction. 2 METHODOLOGY To describe the method of this research work, a basic understanding of components of life cycle analysis is important. This work is based on an integrated two-stage well-to-wheel (WTW) analysis of 20%HCNG fuel for its heavy-duty applications (e.g. passenger city buses). According to ISO 14040, there are basically four components of LCA: definition of goal & scope, LCI or life cycle inventory, LCIA or life cycle impact analysis, and improvement analysis. For any typical LCA, the first and most important thing is the selection of a ‘system boundary’. This system boundary is basically a framework of study which provides a detailed layout of investigation including ‘level of details’, ‘affordability’, ‘constraints’, and ‘scope’ of the work. 2.1 System boundary Figure 1 shows the system boundary or study framework for our research. This approach combines the experimental test results of the engine with the data obtained from literature reviews for the upstream well-to-pump. The idea is to calculate the amount of energy required and GHG emitted to produce 1 MJ of energy from the engine including the entire fuel cycle (WTW). Similarly, emission data was available from the test performance data published in [4]. However, as the data of CO2 emission was missing, ‘carbon balance method’ was used to calculate specific CO2 emission by using mathematical computations supported by given brake specific fuel consumption and emissions data (⁠ CH4 ⁠, CO, NMHC). Later, all the greenhouse gases were combined to form gCO2e/MJ ⁠. The fuel-use system in this framework is an operating engine under a transient test ETC cycle aimed at meeting Euro IV emission requirements with 20% HCNG and suitable operating conditions of equivalence ratio, spark timing, etc (Table 1). Two parameters have been investigated ‘net energy ratio (MJ/MJ)’ and ‘GHG emission (⁠ gCO2e/MJ)’ (Table 2). Both of these findings were made on two scenarios of natural gas i.e. conventional natural gas (CNG) and shale gas (SG) (Table 3). For each of these scenarios, baseline i.e. CNG and later SG were compared with 20%HCNG pathways (EU4 ETC) taking in account of various hydrogen pathways. Results differentiate the WTW total energy and WTW GHG emissions between 0% HCNG (CNG) and 20%HCNG including the impacts of each hydrogen and natural gas pair pathways. Figure 1. Open in new tabDownload slide System boundary. Figure 1. Open in new tabDownload slide System boundary. Table 1 Unmodified experimental data of PTW stage (20%HCNG-ETC Euro IV) Fuel CO2 (g/kWh) NOx (g/kWh) CO (g/kWh) NMHC (g/kWh) CH4 (g/kWh) BSFC (g/kWh) Peak power (kW) CNG/0%HCNG (EU3) 686.647 4.76 2.45 0.52 4 254 154 20% HCNG (EU4) 644.389 2.31 1.54 0.21 2.1 236 154 Fuel CO2 (g/kWh) NOx (g/kWh) CO (g/kWh) NMHC (g/kWh) CH4 (g/kWh) BSFC (g/kWh) Peak power (kW) CNG/0%HCNG (EU3) 686.647 4.76 2.45 0.52 4 254 154 20% HCNG (EU4) 644.389 2.31 1.54 0.21 2.1 236 154 Open in new tab Table 1 Unmodified experimental data of PTW stage (20%HCNG-ETC Euro IV) Fuel CO2 (g/kWh) NOx (g/kWh) CO (g/kWh) NMHC (g/kWh) CH4 (g/kWh) BSFC (g/kWh) Peak power (kW) CNG/0%HCNG (EU3) 686.647 4.76 2.45 0.52 4 254 154 20% HCNG (EU4) 644.389 2.31 1.54 0.21 2.1 236 154 Fuel CO2 (g/kWh) NOx (g/kWh) CO (g/kWh) NMHC (g/kWh) CH4 (g/kWh) BSFC (g/kWh) Peak power (kW) CNG/0%HCNG (EU3) 686.647 4.76 2.45 0.52 4 254 154 20% HCNG (EU4) 644.389 2.31 1.54 0.21 2.1 236 154 Open in new tab Table 2. Adjusted pump-to-wheel data (dimensionality modifications) Fuel PTWTotalEnergy(MJMJ) PTWGHGs(gCO2eMJ) CNG 3.528 217.037 20%HCNG (EU4) 3.278 192.224 Fuel PTWTotalEnergy(MJMJ) PTWGHGs(gCO2eMJ) CNG 3.528 217.037 20%HCNG (EU4) 3.278 192.224 Open in new tab Table 2. Adjusted pump-to-wheel data (dimensionality modifications) Fuel PTWTotalEnergy(MJMJ) PTWGHGs(gCO2eMJ) CNG 3.528 217.037 20%HCNG (EU4) 3.278 192.224 Fuel PTWTotalEnergy(MJMJ) PTWGHGs(gCO2eMJ) CNG 3.528 217.037 20%HCNG (EU4) 3.278 192.224 Open in new tab Table 3. Well-to-pump data of compressed natural gas Code Study Energy source Process WTPEnergy(MJMJf) WTPGHGs(gCO2eMJ) Code Con.NG [17] (Coal + Raw NG+ Petroleum) Conventional Raw NG to NG 1.15 137.81 NGConv·[CNG] SG-CNG [18] Shale Gas EU Shale Gas to CNG 0.10 8 CNGShaleGas·[SG] Code Study Energy source Process WTPEnergy(MJMJf) WTPGHGs(gCO2eMJ) Code Con.NG [17] (Coal + Raw NG+ Petroleum) Conventional Raw NG to NG 1.15 137.81 NGConv·[CNG] SG-CNG [18] Shale Gas EU Shale Gas to CNG 0.10 8 CNGShaleGas·[SG] Open in new tab Table 3. Well-to-pump data of compressed natural gas Code Study Energy source Process WTPEnergy(MJMJf) WTPGHGs(gCO2eMJ) Code Con.NG [17] (Coal + Raw NG+ Petroleum) Conventional Raw NG to NG 1.15 137.81 NGConv·[CNG] SG-CNG [18] Shale Gas EU Shale Gas to CNG 0.10 8 CNGShaleGas·[SG] Code Study Energy source Process WTPEnergy(MJMJf) WTPGHGs(gCO2eMJ) Code Con.NG [17] (Coal + Raw NG+ Petroleum) Conventional Raw NG to NG 1.15 137.81 NGConv·[CNG] SG-CNG [18] Shale Gas EU Shale Gas to CNG 0.10 8 CNGShaleGas·[SG] Open in new tab This flow diagram shows the step-by-step procedure for this research methodology. The upstream well-to-pump data were chosen from literature reviews which include various hydrogen pathway mixes. The well-to-pump inventory data were integrated by a statistical weighted average principle on LHV basis to find the integrated WTP energy use and GHG for 20%HCNG pathways. Available experimental pump-to-wheel (PTW) data was transformed by appropriate mathematical ways to implement integration of WTP and PTW required by the integration method of GREET which was also used by other previous works [16]. CO2 was calculated for all these operations by using carbon balance method. CO2,andCH4 was combined to calculate total GHGs based on their global warming potential values for 100-years’ time horizon as suggested by ‘Intergovernmental Panel on Climate Change’. The GWP of CO2,CH4 and N2O is assumed as 1, 25 and 298 gCO2,e according to the IPCC report. Then, the well-to-pump inventory was combined with pump-to-wheel inventory for analysis. Cost-effectiveness analysis has been performed using a typical engineering economics approach which has not been shown in Figure 1 for the simplicity. 2.2 Functional unit The functional unit is an effective way to evaluate comparison between two different fuel options and also an important component in an LCA study. The functional unit has been defined ‘per MJ of energy’ from the engine. Functional unit WTP PTW WTW Total energy MJInputMJfinishedfuel MJInput(fromfinishedfuel)MJ1MJoutputfromengine MJMJengine GHGs gCO2eMJfinishedfuel gCO2eMJ1MJoutputfromengine gCO2eMJengine Functional unit WTP PTW WTW Total energy MJInputMJfinishedfuel MJInput(fromfinishedfuel)MJ1MJoutputfromengine MJMJengine GHGs gCO2eMJfinishedfuel gCO2eMJ1MJoutputfromengine gCO2eMJengine Open in new tab Functional unit WTP PTW WTW Total energy MJInputMJfinishedfuel MJInput(fromfinishedfuel)MJ1MJoutputfromengine MJMJengine GHGs gCO2eMJfinishedfuel gCO2eMJ1MJoutputfromengine gCO2eMJengine Functional unit WTP PTW WTW Total energy MJInputMJfinishedfuel MJInput(fromfinishedfuel)MJ1MJoutputfromengine MJMJengine GHGs gCO2eMJfinishedfuel gCO2eMJ1MJoutputfromengine gCO2eMJengine Open in new tab Note: In other well-to-wheel analyses, it is quite common to find the WTW results in terms of per km basis and per passenger-km for passenger heavy-duty buses (number of passengers*VKT). For example, MJkm for energy consumption and gCO2,equivalentkm for GHG emissions. 2.3 Data Pump-to-Wheel Data Well-to-Pump Data Table 4 shows the well-to-pump total energy consumption and GHG emission for various pathways or hydrogen chosen for this study. Additionally, pathway’s code has been assigned for the simplicity and a brief description of the pathway details has been provided. Table 4. Well-to-pump data of gaseous hydrogen Code Study Energy source Process WTPEnergy(MJMJf) WTPGHGs(gCO2eMJf) Pathway NG SMR [19] NG SMR 1.527 101.049 GH2_NG_SMR Coal Gasification (w/o CCS) [20] Coal/No CCS Gasification 0.710 238.000 GH2_CO_Gas_wo_CSeq. Coal Gasification (w/ CCS) [20] Coal/ CCS Gasification 0.560 53.917 GH2_CO_Gas_w_CSeq. WTH [21] Wind Power Electrolysis 0.082 8.083 GH2_ElectWindPower STH [21] Solar Power Electrolysis 0.279 20.101 GH2_ElectSolarPower Bio-fermentation [20] Wheat Straw (WS) Bio-fermentation 1.080 46.667 GH2_BioWS_Ferm. Bio-Gasification [20] Waste Wood On-site gasification 0.82 11.167 GH2_BioWW_Ferm. Biomethane [22] Biomethane SMR 0.45 52 GH2_BioCH4_SMR Bio-Electrolysis [22] Straw (herbaceous biomass) Electrolysis 0.5 90 GH2_ElectBio_straw Distr. Grid Elect. [23] Distributed Grid Electricity Electrolysis 3.79 361.88 GH2_ElectGrid Code Study Energy source Process WTPEnergy(MJMJf) WTPGHGs(gCO2eMJf) Pathway NG SMR [19] NG SMR 1.527 101.049 GH2_NG_SMR Coal Gasification (w/o CCS) [20] Coal/No CCS Gasification 0.710 238.000 GH2_CO_Gas_wo_CSeq. Coal Gasification (w/ CCS) [20] Coal/ CCS Gasification 0.560 53.917 GH2_CO_Gas_w_CSeq. WTH [21] Wind Power Electrolysis 0.082 8.083 GH2_ElectWindPower STH [21] Solar Power Electrolysis 0.279 20.101 GH2_ElectSolarPower Bio-fermentation [20] Wheat Straw (WS) Bio-fermentation 1.080 46.667 GH2_BioWS_Ferm. Bio-Gasification [20] Waste Wood On-site gasification 0.82 11.167 GH2_BioWW_Ferm. Biomethane [22] Biomethane SMR 0.45 52 GH2_BioCH4_SMR Bio-Electrolysis [22] Straw (herbaceous biomass) Electrolysis 0.5 90 GH2_ElectBio_straw Distr. Grid Elect. [23] Distributed Grid Electricity Electrolysis 3.79 361.88 GH2_ElectGrid Open in new tab Table 4. Well-to-pump data of gaseous hydrogen Code Study Energy source Process WTPEnergy(MJMJf) WTPGHGs(gCO2eMJf) Pathway NG SMR [19] NG SMR 1.527 101.049 GH2_NG_SMR Coal Gasification (w/o CCS) [20] Coal/No CCS Gasification 0.710 238.000 GH2_CO_Gas_wo_CSeq. Coal Gasification (w/ CCS) [20] Coal/ CCS Gasification 0.560 53.917 GH2_CO_Gas_w_CSeq. WTH [21] Wind Power Electrolysis 0.082 8.083 GH2_ElectWindPower STH [21] Solar Power Electrolysis 0.279 20.101 GH2_ElectSolarPower Bio-fermentation [20] Wheat Straw (WS) Bio-fermentation 1.080 46.667 GH2_BioWS_Ferm. Bio-Gasification [20] Waste Wood On-site gasification 0.82 11.167 GH2_BioWW_Ferm. Biomethane [22] Biomethane SMR 0.45 52 GH2_BioCH4_SMR Bio-Electrolysis [22] Straw (herbaceous biomass) Electrolysis 0.5 90 GH2_ElectBio_straw Distr. Grid Elect. [23] Distributed Grid Electricity Electrolysis 3.79 361.88 GH2_ElectGrid Code Study Energy source Process WTPEnergy(MJMJf) WTPGHGs(gCO2eMJf) Pathway NG SMR [19] NG SMR 1.527 101.049 GH2_NG_SMR Coal Gasification (w/o CCS) [20] Coal/No CCS Gasification 0.710 238.000 GH2_CO_Gas_wo_CSeq. Coal Gasification (w/ CCS) [20] Coal/ CCS Gasification 0.560 53.917 GH2_CO_Gas_w_CSeq. WTH [21] Wind Power Electrolysis 0.082 8.083 GH2_ElectWindPower STH [21] Solar Power Electrolysis 0.279 20.101 GH2_ElectSolarPower Bio-fermentation [20] Wheat Straw (WS) Bio-fermentation 1.080 46.667 GH2_BioWS_Ferm. Bio-Gasification [20] Waste Wood On-site gasification 0.82 11.167 GH2_BioWW_Ferm. Biomethane [22] Biomethane SMR 0.45 52 GH2_BioCH4_SMR Bio-Electrolysis [22] Straw (herbaceous biomass) Electrolysis 0.5 90 GH2_ElectBio_straw Distr. Grid Elect. [23] Distributed Grid Electricity Electrolysis 3.79 361.88 GH2_ElectGrid Open in new tab For example, GH2_CO_Gas_wo_CSeq.→ gaseous hydrogen production from coal gas gasification with CO2 sequestration; Governing Equations Pump-to-wheel total energy Equation (1)Pump-to-wheel total energy (MJ/MJ) calculation PTWTotalEnergy(MJMJ)=(BSFC(gkWh)3.6)⁎LHVf(MJkg)1000 (1) Where: - LHVf=lowerheatingvalueoffuel - BSFC(gkWh)=brakespecificfuelconsumption - PTWTotalEnergy(MJMJ)=totalenergyrequiredtoproduce1MJofenergyfromtheengine(downstream) Pump-to-wheel GHGs Carbon balance approach: Equation (2)Carbon balance method for CO2 calculation MC,CH4MCH4⁎BSFCfuel=MC,CO2MCO2⁎[CO2]+MC,COMCO⁎[CO]+MC,NMHCMNMHC⁎[NMHC]+MC,CH4MCH4⁎[CH4] (2) Where: - Mi=Molarmassofi−componentinexhausttailpipe - MC,i=molecularmassofcarbonini−component - i=CH4,CO,CO2,NMHC - [i]=exhaustflowrate(g)perkWh - [N2O]=0gkWh(assumption;therewasnosuchtailpipeemissionsmeasured.) Equation (3) Pump-to-wheel GWP (gCO2e/MJ) PTWGHGs=(CO2⁎1+CH4⁎25+N2O⁎298)(g3.6MJf) (3) Where: - (CO2/CH4/N2O)=measuredingkWh(1kWh=3.6MJ) Integration of well-to-pump total energy & GHGs of Hydrogen and CNG to form 20%HCNG For 20%HCNG operation, the integration of WTP inventory of gaseous hydrogen and compressed natural gas was achieved by using following expression of ‘weighted average’ on LHV basis. Equation (4)Integration method for WTP HCNG LCI k20%HCNG=0.2⁎kH2⁎LHVH2+0.8⁎kCNG⁎kCNG0.2⁎LHVH2+0.8⁎LHVCNG (4) - k=WTPTotalEnergy,WTPGHGs,LHV The GHG was calculated by the following expression for WTP stage combining all greenhouse gases inventory together to form gCO2,equivalent emission. Equation (5)WTP GHG (gCO2eMJf)calculation WTPGHGs=(CO2⁎1+CH4⁎25+N2O⁎298)(gCO2e3.6MJf) (5) WTW net energy ratio & total GHG emissions Equation (6)Net energy ratio (MJ/MJ) WTWTotalEnergy(MJMJ)=PTWTotalEnergy(MJfMJ)∗(1+WTPEnergy)(MJMJf) (6) Equation(7)WTW GHG WTWGHGs(gCO2eqMJ)=PTWGHGs(gCO2eqMJ)+PTWTotalEnergy(MJfMJ)⁎WTPGHGs(gCO2eqMJf) (7) Where: - PTWTotalEnergy(MJfMJ)=amountofenergyneededfromfueltoproduce1MJenergyfromengine - WTPEnergy(MJMJf)=amountofenergyneededtoproducefuelthatcontains1MJofenergyinit - PTWGHGs(gCO2eqMJ)=GHGemittedperMJofengineoutput - WTPGHGs(gCO2eqMJf)=GHGemittedperMJofenergyinthefinalfuelproduced Adapted from: JRC, HASS [16] In Section 2.2, these parameters have been explained under the heading of ‘functional unit’. 3 RESULTS Figure 2 shows the comparison of well-to-wheel net energy ratio and GHG emissions of 20%HCNG (EU4) pathways with a baseline 0%HCNG(EU3) (conventional natural gas scenario). Figure 3 shows the similar comparison but only difference is that baseline is CNG from shale gas. The net energy ratio and GHGs of 20%HCNG (with 80% conventional natural gas) pathways have been assigned rank here where ‘1’ means the best and ‘10’ means the worst scenario for both these NER and GHG. The purpose of ranking is to highlight the effects of hydrogen pathway on 20%HCNG pathways which has been shown in Figure 5. In this case, 80% CNG in 20%HCNG comes from conventional natural gas. Figure 4 highlights the pump-to-wheel advantages of 20%HCNG (EU4) over 0%HCNG (EU3) in terms of energy saving (MJ/MJ), fuel saving (g/kWh), GHGs reduction gCO2e/MJ ⁠, CO2 reduction (gCO2e/MJ) ⁠, and cost saving (CNY/MJ). These advantages are shown in terms of % where baseline is ‘0%HCNG (conventional natural gas)’ and the target option is ‘20%HCNG (with 80% conventional CNG)’. Figure 2. Open in new tabDownload slide (a) WTW net energy ratio (MJ/MJ) (b) WTW total GHGs (gCO2e/MJ) [conventional NG] Figure 2. Open in new tabDownload slide (a) WTW net energy ratio (MJ/MJ) (b) WTW total GHGs (gCO2e/MJ) [conventional NG] Figure 3. Open in new tabDownload slide (a) WTW net energy ratio (MJ/MJ) (b) WTW total GHGs (gCO2e/MJ) [Shale gas] Figure 3. Open in new tabDownload slide (a) WTW net energy ratio (MJ/MJ) (b) WTW total GHGs (gCO2e/MJ) [Shale gas] Figure 4. Open in new tabDownload slide Miscellaneous benefits of 20%HCNG over 0%HCNG Figure 4. Open in new tabDownload slide Miscellaneous benefits of 20%HCNG over 0%HCNG Comparing Figures 2 and 5, renewable hydrogen pathways such as solar, wind and biomass displayed the ‘best’ scenarios in both energy use and GHG emission perspectives. Grid-electricity-based hydrogen is found to be the worst case as majority of the electricity in China as well as in USA is generated from coal and other fossil sources. Furthermore, On-site gasification of waste wood showed 2nd rank in lower GHG emissions but 7th rank in net energy ratio scenario. It means that although the energy consumption is higher in this case, the feedstock used i.e. waste wood is a cleaner waste-product. Quantitatively, it might be higher, but qualitatively it is clean and sustainable. However, for the near-term future, hydrogen from natural gas steam reforming and coal gasification equipped with CCS facility can be a good choice to cut down emission of conventional NG- and coal-based pathways. They are preferable as the feedstock is cheaper, and the technology is quite efficient comparing to other renewable hydrogen options. However, CO2 sequestration is costlier which is another barrier. Moreover, booming of renewable electricity specially from solar and wind energy with reasonable cost is expected to flourish hydrogen use not just in transportation, but also for residential and industrial applications. Figure 5. Open in new tabDownload slide Ranking of net energy ratio & GHG emissions for 20%HCNG pathways (conventional NG) Figure 5. Open in new tabDownload slide Ranking of net energy ratio & GHG emissions for 20%HCNG pathways (conventional NG) At present, natural gas steam reforming is the most mature and widely used technology of hydrogen production. If we compare 0%HCNG with 20%HCNG (w/o hydrogen from NG SMR), energy consumption is slightly increased but there is subsequent reduction in GHG emissions. As 20%HCNG contains 80%CNG, the feedstock of CNG has a significant impact on an entire fuel cycle. Therefore, shale gas can be a futuristic choice of CNG pathway which significantly cuts down GHG emissions and energy use to make 20%HCNG pathways even more efficient and cleaner. This difference has been demonstrated in Figure 3. Figure 4 shows that using 20%HCNG, reduces energy consumption by 7%, CO2 emission by 6.2%, GHGs by 11% as well as total costs of fuel usage (RMB/MJ, derived from fuel economy) by 7%. In Figure 4, the cost saving was derived from ‘fuel economy benefits’ which is associated with the decrease in brake specific fuel consumption (g/kWh) at pump-to-wheel operation. Hao et al. [24] mentioned the price of H2 in China recently as 3.6CNY/m3 ⁠. Considering this amount, the % of cost saved is almost equal to the % of fuel saved which shows the clear benefits of improved fuel economy in terms of cost. Figure 5 shows that wind-to-hydrogen pathway of 20%HCNG demonstrated the best case in both energy and GHG scenarios and grid electricity-hydrogen demonstrated the worst. Bio-based pathways although seem to have higher energy consumption, they contribute to lower emissions as the feedstock is qualitatively cleaner and by-products or wastes. With the integration of CCS (carbon-capture and storage), coal-to-hydrogen pathway can reduce both energy consumption and GHG emissions. Interestingly, coal-hydrogen with CCS seems a better choice than NG SMR in terms of both energy consumption and GHG emissions. However, there are other drawbacks of using CCS and one of them is incremental cost of the technology. 4 CONCLUSION AND FUTURE OUTLOOK There is a great necessity of life cycle analysis of HCNG® in order to achieve marketization and promote large scale commercial use. It is evident that 20%hydrogen enhancement optimizes the performance and emission of a conventional CNG vehicle. But, it is very important to consider the downsides of adding 20% hydrogen and get an insight to ‘hydrogen economy’. The pathways of hydrogen have a tremendous impact on environment as well as economics. So, it is recommended to perform life cycle analysis and life cycle cost analysis to obtain a detailed and comprehensive cluster of remarks. However, in this research, using data from literature, a simplified but an effective approach has been chosen to perform well-to-wheel analysis of HCNG HDV. The general conclusion can be made as renewable hydrogen pathways can be the best choice of HCNG pathways. It is evident that if in future, these renewable hydrogen pathways get cheaper, technically advanced and energy efficient, 20%HCNG vehicles can get a booming market in a near-term future especially for heavy duty applications such as passenger buses. One of the greatest challenges associated with HCNG vehicular technology is lack of sufficient infrastructure e.g. refuelling stations, high costs of hydrogen generation, and choice of highly carbon-intensive fossil-based hydrogen pathways because of comparatively better maturity and energy efficiency. Additionally, in future, shale gas can be one of the best replacements of conventional compressed natural gas even compared with 20%HCNG pathways. ACKNOWLEDGEMENTS The author of this manuscript whole-heartedly thanks to Professor Wu Ye and Mrs. He Xiaoyi, School of Environment, Tsinghua University for their support and guidelines whenever required. The special thanks goes to my supervisor Prof. Zhuge Weilin and Prof. Ma Fanhua. Without their continuous help, suggestions, and emotional support, this work could not have taken the same form. Thanks to my beloved parents who are the source of my inspiration. REFERENCES 1 Lyu C , Ou X , Zhang X . China automotive energy consumption and greenhouse gas emissions outlook to 2050 . Mitig Adapt Strateg Glob Chang 2015 ; 20 : 627 – 50 . Google Scholar Crossref Search ADS WorldCat 2 Ou X , Zhang X , Chang S . Alternative fuel buses currently in use in China: life-cycle fossil energy use, GHG emissions and policy recommendations . Energy Policy 2010 ; 38 : 406 – 18 . Google Scholar Crossref Search ADS WorldCat 3 Mehra RK , Duan H , Juknelevičius R , et al. Progress in hydrogen enriched compressed natural gas (HCNG) internal combustion engines—a comprehensive review . Renew Sust Energ Rev 2017 ; 80 : 1458 – 98 . Google Scholar Crossref Search ADS WorldCat 4 Ma F , Wang Y , Ding S , et al. Twenty percent hydrogen-enriched natural gas transient performance research . Int J Hydrogen Energy 2009 ; 34 : 6523 – 31 . Google Scholar Crossref Search ADS WorldCat 5 Genovese A , Contrisciani N , Ortenzi F , et al. On road experimental tests of hydrogen/natural gas blends on transit buses . Int J Hydrogen Energy 2011 ; 36 : 1775 – 83 . Google Scholar Crossref Search ADS WorldCat 6 Unich A , Morrone B , Mariani A , et al. The impact of natural gas-hydrogen blends on internal combustion engines performance and emissions. SAE International. 2009 . 7 Ma F , Naeve N , Wang M , et al. Hydrogen-enriched compressed natural gas as a fuel for engines. Natural Gas: InTech; 2010 . 8 Kilgus D. Life Cycle Assessment of a Demonstration Project [Graduate Thesis]. Göteborg, Sweden, 2005 : Chalmers University of Technology; 2005 . 9 Nelsson C , Hulteberg C , Saint-Just J , et al. , editors. HCNG—a dead end or a bridge to the future? 18th World Hydrogen Energy Conference Essen; 2010 2010 ; Essen. Institute of Energy Research—Fuel Cells (IEF-3) Forschungszentrum Jülich GmbH, Zentralbibliothek, Verlag: Schriften des Forschungszentrums Jülich / Energy & Environment; 2010 . 10 Huang Z , Zhang X . Well-to-wheels analysis of hydrogen based fuel-cell vehicle pathways in Shanghai . Energy 2006 ; 31 : 471 – 89 . Google Scholar Crossref Search ADS WorldCat 11 Ewan B , Allen R . A figure of merit assessment of the routes to hydrogen . Int J Hydrogen Energy 2005 ; 30 : 809 – 19 . Google Scholar Crossref Search ADS WorldCat 12 Bartels JR , Pate MB , Olson NK . An economic survey of hydrogen production from conventional and alternative energy sources . Int J Hydrogen Energy 2010 ; 35 : 8371 – 84 . Google Scholar Crossref Search ADS WorldCat 13 Turner JA . Sustainable hydrogen production . Science 2004 ; 305 : 972 – 4 . Google Scholar Crossref Search ADS WorldCat 14 Márquez AC , Márquez CP , Fernández JG , et al. Asset Management . Springer Netherlands , 2012 . Google Preview WorldCat COPAC 15 DeCicco J , Ross M . Recent advances in automotive technology and the cost-effectiveness of fuel economy improvement . Transp Res D Transp Environ 1996 ; 1 : 79 – 96 . Google Scholar Crossref Search ADS WorldCat 16 JRC RE , Hass H , Larivé J-F , et al. WELL-TO-WHEELS Report Version 4. a JEC WELL-TO-WHEELS ANALYSIS. Luxembourg: Publications Office of the European Union, 2014: Institute for Energy and Transport, Joint Research Centre, Institute for Energy and Transport JRC; 2014 2014 . Report No.: EUR 26236 EN. 17 Ou X , Zhang X , Zhang X , et al. Life cycle GHG of NG-based fuel and electric vehicle in China . Energies 2013 ; 6 : 2644 – 62 . Google Scholar Crossref Search ADS WorldCat 18 Concawe E. WELL-TO-TANK Report Version 4.0 JEC WELL-WHEEL ANALYSIS. Summary Report. Joint Research Centre: Institute for Energy and Transport, Institute for Energy and Transport JRC; 2006 2013. Contract No.: Report EUR 26028. 19 Spath PL , Mann MK. Life cycle assessment of hydrogen production via natural gas steam reforming. National Renewable Energy Lab., Golden, CO (US); 2000 . 20 Djomo SN , Blumberga D . Comparative life cycle assessment of three biohydrogen pathways . Bioresour Technol 2011 ; 102 : 2684 – 94 . Google Scholar Crossref Search ADS WorldCat 21 Cetinkaya E , Dincer I , Naterer G . Life cycle assessment of various hydrogen production methods . Int J Hydrogen Energy 2012 ; 37 : 2071 – 80 . Google Scholar Crossref Search ADS WorldCat 22 Wulf C , Kaltschmitt M . Life cycle assessment of biohydrogen production as a transportation fuel in Germany . Bioresour Technol 2013 ; 150 : 466 – 75 . Google Scholar Crossref Search ADS WorldCat 23 Ruth M , Laffen M , Timbario TA. Hydrogen pathways: cost, well-to-wheels energy use, and emissions for the current technology status of seven hydrogen production, delivery, and distribution scenarios. National Renewable Energy Lab.(NREL), Golden, CO (United States); 2009 . 24 Hao H , Liu Z , Zhao F , et al. Natural gas as vehicle fuel in China: a review . Renew Sust Energ Rev 2016 ; 62 : 521 – 33 . Google Scholar Crossref Search ADS WorldCat Author notes Present address: Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou, P.R. China © The Author(s) 2019. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
TI - The well-to-wheel analysis of hydrogen enriched compressed natural gas for heavy-duty vehicles using life cycle approach to a fuel cycle
JF - International Journal of Low-Carbon Technologies
DO - 10.1093/ijlct/ctz020
DA - 2019-08-31
UR - https://www.deepdyve.com/lp/oxford-university-press/the-well-to-wheel-analysis-of-hydrogen-enriched-compressed-natural-gas-D003fxFcZ0
SP - 432
VL - 14
IS - 3
DP - DeepDyve
ER -