Conventional Gasoline Vehicles Research Articles

Battery-powered bargains? Assessing electric vehicle resale value in the United States

The resale market will play a critical role in expanding plug-in electric vehicle (PEV) adoption to middle- and lower-income households. Understanding PEV depreciation trends in comparison to those of conventional gasoline vehicles (CVs) is critical for assessing PEV affordability and informing relevant policy, such as subsidies for used PEVs. We deliver comprehensive, high-resolution estimates of value retention rates at the make-model level in the United States for battery electric vehicles (BEVs), plug-in hybrid electric vehicles (PHEVs), hybrid electric vehicles (HEVs), and CVs using a database of 9 million used cars listed online between 2016 and 2022. While BEVs and PHEVs have depreciated at faster rates than CVs, this trend is changing, with newer model year BEVs and those with larger ranges have significantly higher retention rates than older model years with smaller ranges. Tesla BEVs are a notable exception following the opposite trend, with earlier model years holding their value better than newer model years. Subsidized BEVs in the new market are associated with lower prices for the same model in the resale market, with the $7500 federal subsidy translating to a 3% lower resale price on average. Finally, disruptions from the COVID-19 pandemic have affected affordability across all vehicles, with mean listing prices rising 37% and 39% for CVs and BEVs, respectively, from January 2020 to March 2022 in inflation-adjusted 2019 dollars.

Global Warming Potential from the Life Cycle of Fischer-Tropsch Fuel from Carbon Capture for Passenger Cars in Jakarta

Measures to mitigate the impacts of climate change in the form of carbon capture and storage have not been implemented in Indonesia, especially because there are carbon transportation and storage processes that do not provide added value. The Fischer-Tropsch process is a technology that can produce products that can be processed from CO2 and processed to become value-added commodities in the form of motor vehicle fuel. Using the openLCA application, a life cycle analysis was carried out of the synthesis of FT fuel for use in conventional gasoline vehicles in Jakarta from the capture of CO2 sourced from a coal fired power plant’s exhaust gas, which was compared with the life cycle of electrical energy for battery electric vehicles and conventional fuel for internal combustion engine vehicles. The life cycle impact method used is global warming potential which is expressed in kg CO2-eq. An alternative scenario is created as a comparison for analysis regarding life cycle impacts and parameter sensitivity. It is gathered that FT Fuel synthesis from carbon capture has a higher GWP impact than electrical energy for BEVs and conventional gasoline for ICEVs, due to the high electrical energy requirements from FT Fuel synthesis and the energy mix of the Java-Bali electric power system which is still dominated by fossil-fueled power plants. With alternative scenarios, the GWP impact from FT Fuel synthesis could be equivalent to or lower than electrical energy for BEVs and conventional gasoline for ICEVs.

Experimental investigation of air-conditioning electrical compressor using binary TiO2–SiO2 polyol-ester nanolubricants

As electric vehicles (EVs) continue to replace conventional gasoline vehicles, maintaining thermal comfort within the car requires additional energy to operate the automotive air-conditioning (AAC) system. This work aims to optimise the electrically driven compressor (EDC) system utilising polyol-ester (POE)-based binary nanolubricants to enhance performance and minimise the size of the EV battery and AAC components. A binary nanolubricant was formulated using a two-step method of formulation. The TiO2–SiO2/POE binary nanolubricant was prepared at different volume concentrations ranging from 0.01 to 0.1 %. The experiment was conducted for the 1200–3840 rpm compressor speed with different initial refrigerant charges between 120 and 160 g. The heat absorption rose by up to 44.2 % while utilising the binary nanolubricant at a volume concentration of 0.03 %. The coefficient of performance (COP) reached its maximum value of 2.43 at a refrigerant charge of 160 g and compressor speed of 1860 rpm. Furthermore, the binary nanolubricant significantly reduced the expansion valve discharge temperature, exhibiting a substantial decrease of up to 51.6 %. The highest COP increment, up to 23.4 %, was achieved at a volume concentration of 0.03 %. Hence, it is recommended to utilise 0.03 % TiO2–SiO2/POE binary nanolubricant to achieve optimal performance in the AAC-EDC system.

Emission Characteristics of Particle Number from Conventional Gasoline and Hybrid Vehicles

Vehicular particle number (PN) emissions have garnered increasing attention. In this study, nine light-duty vehicles, involving conventional internal combustion engine gasoline vehicles (ICEVs) and hybrid electric vehicles (HEVs), underwent testing on a chassis dynamometer to elucidate key factors influencing PN emissions. We found that with more stringent emission standards Gasoline Direct Injection (GDI) vehicles exhibited a reduction in PN emission factors. Higher PN emissions for GDI vehicles than vehicles with Multi-Port Fuel Injection (PFI) engines were observed; meanwhile, HEV showed lower PN emissions than ICEVs. PN emissions for cold start consistently exceeded warm start across vehicles with different standards and technologies. Notably, China VI HEV exhibited a substantial 19.2-fold increase in PN emissions for cold start compared to warm start. Analysis on a second-by-second basis revealed that cold-start emissions concentrated in low speed, while warm-start emissions were prominent in extra-high speed. Concerning vehicle specific power (VSP), the lowest mean PN emission rate occurred during idle conditions. PN emissions for China IV-VI ICEVs with GDI engines would increase with the increasing VSP, whereas China VI ICEVs with PFI engines and HEV with GDI engines showed varied patterns of PN emissions, especially under cold start. Our study would further facilitate formulating effective strategies for vehicular PN emissions.

Energy Management Strategy for P1 + P3 Plug-In Hybrid Electric Vehicles

In order to simultaneously improve the fuel economy and overall performance of plug-in hybrid electric vehicles (PHEVs), this study selected the P1 + P3 configuration as its research object. Through a configuration analysis of hybrid vehicles, it confirmed the feasibility of P1 + P3 configuration-PHEV operating modes. Based on this, a rule-based control strategy was developed, and simulation models for the entire vehicle and control strategy were constructed in both Cruise and MATLAB/Simulink software. The study conducted simulation analysis by combining three sets of Worldwide Harmonized Light vehicles Test Cycle (WLTC) driving cycles to assess the fuel-saving potential of the dual-motor P1 + P3 configuration. The simulation results showed that the vehicle model was reasonably constructed and the proposed control strategy had good control effects on the entire vehicle. Compared to conventional gasoline vehicles, the P1 + P3 configuration PHEV achieved a 67.4% fuel economy improvement, demonstrating a significant enhancement in fuel efficiency with the introduction of electric motors.

Real-world emissions of nanoparticles, particulate mass and black carbon from a plug-in hybrid vehicle compared to conventional gasoline vehicles

Among various Hybrid Electric Vehicles (HEVs), Plug-in Hybrid Electric Vehicles (PHEVs) charged from the grid are seen as the most advanced ones, as they can drive dozens of kilometers using only the electric engine and thus producing less tailpipe greenhouse gas emissions than vehicles with internal combustion engines or other HEVs. The proportion of PHEVs among all vehicles is still relatively low but increasing rapidly in many countries. However, the real-world emissions from these novel hybrid technologies are not straightforward to estimate. This study investigates multiple properties of the particle emissions of a PHEV, with gasoline direct injection, GDI, compared to two conventional gasoline vehicles, one with port fuel injection, PFI and one with GDI. Distance-based emission factors (EFs) for each vehicle in various driving modes, including battery-hold and battery-charge driving modes for the PHEV, were analyzed. The results showed that the PHEV produced smaller particles in size, resulting that particle mass (PM) and black carbon (BC) were lower by factor of ten in comparison to EFs from the vehicles with PFI and GDI engines. The PHEV consistently emitted lower distance-based EFs than the PFI and GDI vehicles in all driving modes, though EF for particle number (PN) in battery-charge mode was close to the EFs from the other two vehicles. The study also found that the vehicle cold start effect was present in the case of the shorter driving route but not as significant in the longer one. Overall, the study demonstrated that PHEVs could produce lower particle and BC emissions compared to traditional gasoline-powered vehicles. The vehicle cold start and systematic combustion engine restart effects still can have significant impacts on particle emissions, especially in shorter trips.

Predictive cruise control for hybrid electric vehicles based on hierarchical convex optimization

Predictive Cruise Control (PCC) achieves automated driving with improved vehicle economy by incorporating road gradient information from high-precision maps. However, existing research on PCC has primarily centered around conventional gasoline vehicles. Hybrid Electric Vehicle (HEV) offer superior fuel efficiency compared to their traditional counterparts. To further enhance vehicle economy by integrating the PCC system with HEVs, this paper proposes a hierarchical control architecture for HEV-PCC based on convex optimization. Firstly, considering spatial slope information from the road ahead, this paper introduces a Time-Space Integration Model Predictive Control (TSI-MPC) based on the principles of kinetic energy. Secondly, the paper combines a current regression model with the Pontryagin Minimum Principle (PMP) to present a Regression Analysis - Pontryagin Minimum Principle (RA-PMP) approach for the direct analytical solution of the energy management problem. Finally, the strategy is validated through simulation analysis and Hardware-in-Loop (HIL) testing on various real road scenarios. The result indicate that, in comparison to the Equivalent Consumption Minimization Strategy (ECMS) with constant speed cruising (CC), this method achieves an average fuel economy improvement of 9.45%.

Research on the CO2 Emission Characteristics of a Light-Vehicle Real Driving Emission Experiment Based on Vehicle-Specific Power Distribution

China implemented the China VI emission standard in 2020. The China VI emission standard has added requirements for the RDE (real-world driving emission) test. To evaluate vehicle CO2 emission for different vehicles, 10 conventional gasoline vehicles were tested under the RDE procedure using the PEMS (portable emission testing system) method. All vehicles tested meet the sixth emission regulation with a displacement of 1.4 L~2.0 L. Among the vehicles tested, the highest CO2 emission factor was 281 g/km and the lowest was 189 g/km, while the acceleration of RDE gets a wider distribution, varying from −2.5 m/s2 to 2.5 m/s2. The instantaneous mass emission rate could reach around 16 g/s. The amounts of total CO2 emission in the positive region and the negative region make up 82~89% and 11~18% of the overall CO2 emission during the entire RDE driving period, respectively. The same vehicle has a wide range of CO2 emission factors at different VSP (vehicle specific power) intervals. Different RDE test conditions can lead to large differences in CO2 emissions.

Exhaust aftertreatment device-derived ammonia emissions from conventional and hybrid light-duty gasoline vehicles over different driving cycles

Ammonia emissions from motor vehicles have great effect on air pollution and human health in urban areas. Recently, many countries have focus on ammonia emission measurement and control technologies for light-duty gasoline vehicles (LDGVs). To analyze ammonia emission characteristics, three conventional LDGVs and one hybrid electric light-duty vehicle (HEV) were evaluated over different driving cycles. The average ammonia emission factor at 23℃ was 4.5 ± 1.6 mg/km over Worldwide harmonized light vehicles test cycle (WLTC). Most ammonia emissions mainly concentrated in low and medium speed sections at cold-start stage, which were related to rich burn conditions. The increasing ambient temperatures led to the decrease of ammonia emissions, but high load caused by extremely elevated ambient temperature led to obvious ammonia emissions. The ammonia formation is also related to three-way catalytic converter (TWC) temperatures, and underfloor TWC catalyst could eliminate ammonia partly. The ammonia emission from HEV, which are significant less than LDGV, corresponded to the engine working state. The large temperature difference in the catalysts caused by power source shifts were the main reason. Exploring the effects of various factors on the ammonia emission is beneficial for revealing the instinct formation conditions, providing theoretical support for the future regulations.

Production and decarbonisation of conventional gasoline vehicle automakers under subsidy and regulation policies

ABSTRACT Decarbonising the transportation sector through electrification is critical to reducing greenhouse gas emissions. To explore the decarbonisation of conventional gasoline vehicle (GV) automakers in GV and/or electric vehicle (EV) production, we develop analytical models under pure subsidy, pure regulation, and hybrid subsidy-regulation policies. We first examine the monopoly GV automaker’s EV market entry intention in each policy and find that it always increases with consumer range sensitivity and subsidy/regulation parameters, but decreases with product substitutability between the two vehicles. Then, we analyse the implication of policy changes on equilibrium outcomes. Our results show that although introducing regulations and removing subsidies have opposite effects on optimal production and driving range decisions, both might stimulate or restrict the GV automaker to decarbonise, depending on the unit carbon emissions of GVs and EVs. Finally, we extend the monopoly model to the duopoly case, showing that while entering the EV market hurts the EV automaker, it allows the GV automaker’s carbon emissions to always decrease with GV credit coefficients. Together with sensitivity analyses on key parameters, our findings provide insights not only for GV automakers to respond to policy changes but also for governments to design incentives for EV diffusion.

Cradle-to-Grave Lifecycle Analysis of Greenhouse Gas Emissions of Light-Duty Passenger Vehicles in China: Towards a Carbon-Neutral Future

Vehicle electrification is considered a pathway for on-road transportation decarbonization in China. Different from the conventional gasoline vehicles whose emissions are mainly released from vehicle tailpipes, emissions of battery electric vehicles (BEVs) are from the upstream processes of electricity generation and vehicle manufacturing, thus a comprehensive lifecycle analysis and comparison of BEVs with gasoline vehicles is required to quantify the emission mitigation benefit of vehicle electrification and determine the path to a carbon-neutral future. In the study, we compare the cradle-to-grave (C2G) lifecycle greenhouse gas emissions of gasoline and electric vehicles in China and analyze the greenhouse gas emission reduction of vehicle electrification in different provinces. Results show that under the current technologies, the national average C2G GHG emissions for battery electric vehicles (BEVs) of 100 miles (i.e., 160 km) and 300 miles (i.e., 480 km) all-electric range (AER) are 231 and 279 g CO2eq/km, respectively, 22% and 5% lower than those for gasoline internal combustion engine vehicles (ICEVs). Improving vehicle fuel efficiency by hybridizing gasoline ICEVs can effectively reduce C2G emissions to 212 g CO2eq/km. At the provincial level, C2G GHG emissions of BEVs vary according to the provincial electricity mix. In eight provinces, C2G GHG emissions of BEVs with 300 miles AER (BEV300s) are higher than those of gasoline ICEVs due to the GHG-intensive coal-based electricity mix. In the future scenario, with low carbon fuels (such as high-level bioethanol blending gasoline) and electricity decarbonization, the national average C2G emissions of hybrid electric vehicles (HEVs) and BEV300s can be reduced to 55 and 73 g CO2eq/km, respectively. Further decrease of C2G GHG emissions relies on reducing vehicle-cycle emissions from material processing and vehicle component manufacturing.

A preliminary feasibility study of potential market applications for non-commercial technology magnets

Climate change has spurred increased electrification through means of transportation, hydropower, and wind turbines which has caused an increased demand for permanent magnet materials. Current commercial magnet technologies rely heavily on several critical materials such as neodymium, praseodymium, dysprosium, samarium, and cobalt which are primarily sourced and refined outside of the United States (U.S.). To combat these problems, the Critical Materials Institute (CMI) has begun research into alternative magnet compositions to reduce critical material content. Additionally, these alternative magnets can fulfill a gap in the market between high performance neodymium-iron-boron (Nd-Fe-B) and samarium cobalt (Sm–Co) magnets and low performance ferrite or bonded Nd-Fe-B magnets, earning the term gap magnets. This research seeks to compile a simple strategy for identifying an application for these alternative magnets and assessing preliminary market impacts through substitution for two example magnets. The first magnet was identified to be applicable for ancillary motors and sensors in conventional gasoline vehicles with a maximum substitution of 4,825 metric tonnes (mt) per year by the year 2050. The second magnet was identified to be applicable for magnetic couplings in energy and industrial sectors with a maximum substitution of 978 mt per year by the year 2050.

Characterizing and assessing the fuel economy, particle number and gaseous emissions performance of hybrid electric and conventional vehicles under different driving modes

Hybrid electric vehicle (HEV) is considered to be more energy efficient and less emissions than conventional gasoline vehicle (CV). A growing number of studies suggested that real-road emissions may be much higher than laboratory measurements. To compare the fuel economy and emissions performance of HEV and CV on real roads, this study conducted real driving emission (RDE) tests on HEV and CV of the same configuration under real driving conditions with aggressive and normal driving styles. The results indicated that the HEV had lower fuel consumption (L/100 km), nitrogen oxides (NOX) and carbon dioxide (CO2) emissions compared to the CV. Fuel saving of HEV compared to CV was up to 41.14% under urban conditions. Compared to emissions of other trip conditions, The CO2 and NOX reductions of HEV under urban conditions were the largest, which were more than 33.65% and 61.98% respectively. However, the particle number (PN) and carbon monoxide (CO) emissions of HEV were larger than those of CV. This was due to the frequent stopping and restarting of the internal combustion engine of the HEV. In order to comprehensively compare the fuel economy and emissions performance of HEV and CV, the evaluation index of real conformity factor was proposed. The analysis showed that to reduce the high PN and CO emissions of HEV, a combination of fuel economy-focused and emission control-focused strategies should be used.

Optimal site selection and sizing of solar EV charge stations

Driving a conventional gasoline vehicle is an important polluting factor that causes environmental degradation. In order to reduce dependence on gasoline and its related environmental effects, electric vehicles are emerging as an alternative to conventional gasoline vehicles. Electric vehicles are both economical and environmentally friendly vehicles that draw their energy from rechargeable batteries in the car, which have many benefits such as reduced carbon emissions or pollution, cost-effectiveness, and less noise. The most important drawback of these vehicles is the problems with recharging. One way to deal with this problem is to build charge stations for electric vehicles. A suitable charge station for electric vehicles should also be located in a very precise place to get the most out of electric vehicles. Therefore, in this paper, an MCDA approach based on GIS for optimal site selection of charge stations has been conducted. A simple Hierarchical Analysis Process (AHP) is used to select the optimal locations for electric vehicle charge stations. The proposed method is applied in Kish (an island in the south of Iran) as the study area. The genetic optimization algorithm is applied to solve the optimization problem and is simulated in Matlab software. Using integrated modeling and mathematical optimization in a GIS operating system, the results showed that the proposed model can select suitable locations for charge stations for electric vehicles that can meet the demand for charging electric vehicles within a certain access distance. Also, the method of locating and determining the simultaneous capacity of solar sources and charge stations of electric vehicles and managing the charging process of vehicles in the network has been presented and suitable places for constructing charge stations of electric vehicles have been proposed.

Willingness to Pay for the Public Electric Bus in Nepal: A Contingent Valuation Method Approach

The rapid increase in conventional diesel and gasoline vehicles in developing countries draws attention to clean energy vehicles, including electric buses. From socioeconomic and environmental perspectives, the benefits of electric buses are well described; however, there is a lack of studies to analyze the willingness to pay (WTP). This study aims to estimate 500 residents’ WTP in Pokhara Metropolitan City in Nepal, based on a contingent valuation method (CVM). The survey results show that 78% of respondents are willing to pay a special monthly tax for introducing electric buses in the city primarily due to the fact that electric buses are likely to be helpful to the environment (82.3%). Using the logistic regression analysis, it is estimated that the mean WTP is 758.6 NPR per person, with the most influencing factors of ‘willingness to ride electric buses for free’ and ‘the average usage of the main transportation per week’. The variables that show a positive relationship with the WTP are ‘the average usage of the main transportation per week’, ‘willingness to ride electric buses for free’, and ‘age’. The variable that negatively correlates with the WTP is ‘age’. The study’s findings provide references for developing funding options and budgeting plans for local policymakers.

Hidden Markov Models-Based Anomaly Correlations for the Cyber-Physical Security of EV Charging Stations

Over recent years, we have seen a significant rise in electric mobility to overcome the anthropogenic emissions by conventional gasoline vehicles. However, the prerequisite of smoothening of peaks and imbalances through bidirectional charging is concatenated with the undesired impacts on reliability and security of power system operation when there are probable intrusions in the eXtreme Fast Charging (XFC) station, hence destabilizing the charging networks. So this paper applies STRIDE based threat modeling to analyze and identify multiple potential threats endured by the cyber-physical system (CPS) of XFC station by using weighted attack defense tree. Potential mitigation strategies are then suggested for the identified severe threats. In addition, this paper also develops a stochastic probabilistic tool, the Hidden Markov Model (HMM) for modeling the security attacks for a given range of identified attack vectors and hence employing an appropriate defense strategy against the malicious hacker. Also, a weighted attack defense tree has been developed to generate various attack scenarios. In the end, the results of the proposed work are substantiated and validated if it is able to considerably improve overall charging efficiency and cyber-physical security of the charging station network.

The impacts from cold start and road grade on real-world emissions and fuel consumption of gasoline, diesel and hybrid-electric light-duty passenger vehicles

Progressively stringent regulations regarding vehicle emissions and fuel economy have spurred technology diversification in light-duty passenger vehicles (LDPVs). To assess the real-world emissions and fuel economy performances of hybrid electric vehicles (HEVs) compared to conventional internal combustion engine (ICE) vehicles, on-road measurements of ten gasoline, four diesel and six full hybrid LDPVs were performed using portable emissions measurement systems (PEMS) in Macao, China. The hot-running emission results indicate that the high emission risks of gasoline vehicles are associated with high mileage and old model years. Diesel vehicles are found to be the highest pollutant emitters in this study due to the intentional removal of aftertreatment systems. Under hot-running conditions, HEVs, as expected, could achieve carbon-reduction benefits of approximately 30 % (i.e., lower CO2 emissions and fuel consumption) compared to their conventional gasoline counterparts, while no measurable reduction in pollutant emissions was observed except in NOX (~70 % reduction). In contrast, the cold-start extra emissions (CSEEs) of CO2 reached 120–364 g/start for these HEVs, even exceeding the maximum values of conventional gasoline vehicles. However, the higher CO2 CSEEs of HEVs can be far offset by their hot-running emission reduction benefits. For tailpipe pollutants, the CSEEs of the HEVs were reduced by 21 %–68 % on average in comparison to those of conventional gasoline vehicles. Furthermore, strong correlations (R2 values of 0.69–0.89) between the road grades and relative emissions were observed. These results can provide necessary information regarding the improvement of future LDPV emission models and inventories.

Charging-Related State Prediction for Electric Vehicles Using the Deep Learning Model

Electric vehicles (EVs) are becoming the potential contender for the conventional gasoline vehicles in view of the environment-friendly and energy-efficient characteristics. The prediction of EV charging-related states (defined in this study as home charge, outside charge, home stop, outside stop, low-battery travel, and high-battery travel) could help to identify the future charging demand (power consumption) of EV individuals. Specifically, it could guide the operation and management of charging facilities and also provide tailored charger availability information based on users’ real-time locations. This study aims to predict charging-related states of individual EVs using a deep learning approach. We first propose a tangible approach to convert EV trajectory data into state sequences and then develop a bidirectional gated recurrent unit model with attention mechanism (Bi-GRU-Attention) to forecast EV states. A sensitivity analysis is conducted to tune and/or calibrate parameters in the model based on plug-in hybrid EV trajectories dataset collected in Shanghai, China. Experiment results show that (i) the proposed method could achieve an average accuracy of 77.15% with a 1-hour prediction length and it outperforms the baseline models for all tested prediction lengths; (ii) it is also revealed that the prediction accuracy varies dramatically with different states and time periods. Among all states, the proposed model has a higher prediction accuracy on “home stop” (89.0%). As for time periods, the EV states around 08:00 am and 04:00 pm are hard to predict, and a comparatively low prediction accuracy (close to 60%) is obtained; and (iii) the stability and robustness analysis implies that the proposed model is stable and insensitive to SOC noise or season.

ADAPTIVE EQUIVALENT FUEL CONSUMPTION MINIMIZATION STRATEGIES FOR ENERGY MANAGEMENT OF ELECTRIC VEHICLE

Growing concerns about global warming have been sparked by the rise in the emission rate of greenhouse gases, which is mostly brought on by the transportation industry. Additionally, cost of fuels based on gasoline and diesel is rising daily. Due to this, the transportation sector must adopt hybrid and plug-in hybrid vehicles. The vehicle battery needs to be charged frequently because its range is not very great. The charging method that must be used to recharge the batteries in electric vehicles must be effective, quick, and inexpensive. Electric vehicles require between 60 and 80 minutes to recharge their battery compared to conventional gasoline vehicles, which can be refuelled in 2 to 5 minutes or less. A hybrid electric vehicle consists of two or more energy sources with energy management and power management for the better fuel economy. The main scheme of the energy management strategy (EMS) is to split energy between the sources to reduce fuel consumption.

Life cycle assessment on energy efficiency of hydrogen fuel cell vehicle in China

Based on the analysis and research achievements of domestic and foreign scholars regarding the vehicle life cycle assessment, in combination with the research data of relevant enterprises, the research has objectively assessed well to wheel energy efficiency of hydrogen fuel cell vehicle, and performed comparative analysis with conventional gasoline vehicle, conventional diesel vehicle, natural gas vehicle, methanol vehicle and battery electric vehicle. The research results show that the range of well to wheel energy efficiency of hydrogen fuel cell vehicle is between 6.8% and 29.2%, which depend much on the difference of source and transport mode of hydrogen, and the range covers several technical routes of vehicles other than battery electric vehicle which uses green electricity. The well to wheel energy efficiency under the scenario of hydrogen production from water electrolysis using renewable power such as photovoltaic and wind power is relatively high (21.9%–29.2%). The scenarios of hydrogen from methanol and grid power electrolysis are lower than other scenarios (6.8%–12.5%). The choice of hydrogen transportation mode is closely related to the distance between the production and use sites. For short and medium-distance, long-tube trailers can be used to transport gas hydrogen, and in the case of long-distance transportation, such as more than 800 km, the use of liquefaction or pipeline transportation can better ensure the energy efficiency advantage of hydrogen fuel cell vehicle. Nevertheless, for the same transport distance, the energy efficiency of well to wheel can be improved less effectively by increasing the hydrogen transportation capacity, and more effectively by improving the energy efficiency of the vehicle operation, the feedstock production and the hydrogen production.