Critical aggressive acceleration values and models for fuel consumption when starting and driving a passenger car running on LPG

The models based on vehicle speed have been used to estimate fuel consumption and CO₂ emissions. However, these models could not properly estimate the change in fuel consumption and CO₂ emissions as the speed changes. As for the alternative method, people try to consider using acceleration instead of speed. Although acceleration has been seriously considered, determining critical aggressive acceleration value in relation to fuel consumption and CO₂ emissions is difficult to find. In this study, evaluation models of fuel consumption were developed using instantaneous acceleration, and we defined the critical aggressive acceleration values for different states of the vehicle from the viewpoints of fuel consumption and emissions. We used a mid-sized Liquefied Petroleum Gas (LPG) passenger car and obtained instantaneous data from a digital tachograph installed in the car while it accelerates. We developed two fuel consumption models and found critical aggressive accelerations, respectively: a model of starting vehicle that measures range of speed required to overcome the inertia during acceleration from stop state, and the other model for the driving state. We used Classification and Regression Tree (CART) analysis to find the critical aggressive accelerations at which the increments of fuel consumption change abruptly. As a result, the critical aggressive accelerations causing abrupt change in the increments of fuel consumption were found to be 2.598 m/s² for the starting of vehicles and 1.4705 m/s² when driving them. We also found that the increments of fuel consumption can be explained through quadratic and exponential functions with instantaneous acceleration.     

Eco-driving by replicating best driving practices

Some transit companies have reduced up to 25% of their fuel consumption by training their drivers on eco-driving. We propose to further reduce fuel consumption by using best driving practices, which consist of guiding drivers on the recommended ranges of vehicle speed and engine RPM that lead to minimum fuel consumptions. These recommendations are obtained by monitoring the engine operational parameters used by drivers who have shown the lowest fuel consumption while driving vehicles of similar technology and running on the same section of the road. To demonstrate the applicability of this alternative, we monitored a fleet of 15 transit buses of recent technology serving a ～72-km long route of general characteristics during eight months of normal operation. For each kilometer, trips with the lowest fuel consumption were selected, and their interquartile ranges of RPM and speed were obtained. Then, a well-trained driver in eco-driving traveled over the same road with same type of buses, using these recommended ranges. Using his eco-driving training, he obtained a 0.5% reduction in fuel consumption with respect to the average of the population, and by using the best driving practices, his savings increased up to 10.1%. These savings were linked to an increase in the average energy efficiency of the engine from 27.6% to 28.9%, which demonstrates that when drivers use these best driving practices, they tend to operate the engine under conditions closer to the load—RPM engine area of engine highest efficiency.     

Examining individuals preferences for hybrid electric and alternatively fuelled vehicles

This paper examines individuals motivations when purchasing vehicles, focusing upon what factors would encourage individuals to purchase hybrid electrical vehicle (HEV) or alternatively fuelled vehicle (AFV). AFVs in this paper refer to any cars run on alternatives to petrol and diesel. This research attempts to ascertain whether reductions in fuel costs, vehicle registration tax (VRT), or green house gas emissions would encourage individuals to purchase a HEV or an AFV instead of a conventional vehicle. VRT is an Irish tax that is levied on the purchase of new vehicles. One of the motivations to conduct this research was to examine a new car tax and VRT scheme introduced by the Irish government in 2008. This new policy rewards the purchase of environmentally friendly cars, with lower VRT and car tax rates. To understand individuals’ perceptions of these new taxes a survey was sent to recent customers of a car company in Ireland. The survey asked respondents about their recently purchased vehicle and how important they considered vehicle attributes such as environmental performance, fuel cost, and safety, before making their car purchase. The survey also contained a number of stated preference experiments that were designed to ascertain what factors influence individuals’ decisions when purchasing their new car. The results showed that respondents did not rate green house gas emissions or VRT as crucial attributes when purchasing a new vehicle. The vehicle attributes that respondents rated most highly were reliability, automobile safety, fuel costs, and the cost price. The majority of respondents agreed that HEVs and AFVs are better for the environment, cheaper to run than conventional vehicles and would be the vehicle of choice in ten years time.     

Quantifying grade effects on vehicle fuel consumption for use in sustainable highway design

The paper develops an analytical formulation that quantifies the effect of vertical grade on vehicle fuel consumption and then illustrates the use of the developed procedure in the identification of the fuel efficient freeway layouts. Specifically, the Virginia Tech Comprehensive Power-based Fuel consumption Model (VT-CPFM) was used to develop the formulation and then applied using 2015's 10 most-sold vehicles in the U.S. and Europe to quantify the vertical grade effect on vehicle fuel consumption rates. An increase in fuel consumption of approximately 140% was found when the roadway grade increased from 0.5% to 6%. The proposed selection procedure uses Geographical Information System (GIS) applications in the design phase to evaluate possible freeway layouts. A multi-criteria analysis is performed to rank the feasible alternatives. The yearly fuel consumed by cars traveling on each feasible layout is then predicted and the alternatives are sorted in ascendingly. If the alternative selected by the multi-criteria analysis gives the least yearly fuel consumption, then that alternative should be constructed. If not, then the alternative that results in the least yearly fuel consumption should be re-evaluated with respect to the one selected by the multi-criteria analysis tool. The proposed procedure is validated using a real case study involving the construction of a new freeway in Cameroon. As much as a 12% difference in fuel consumption was found between the alternative with the least estimated yearly fuel consumption and that selected based on a multiple-criteria decision analysis.     

Socially optimal replacement of conventional with electric vehicles for the US household fleet

In this study, a framework is proposed for minimizing the societal cost of replacing gas-powered household passenger cars with battery electric ones (BEVs). The societal cost consists of operational costs of heterogeneous driving patterns' cars, government investments for charging deployment, and monetized environmental externalities. The optimization framework determines the timeframe needed for conventional vehicles to be replaced with BEVs. It also determines the BEVs driving range during the planning timeframe, as well as the density of public chargers deployed on a linear transportation network over time. We leverage data sets that represent US household driving patterns, as well as the automobile and the energy markets, to apply the model. Results indicate that it takes 8 years for 80% of our conventional vehicle sample to be replaced with electric vehicles, under the base case scenario. The socially optimal all-electric driving range is 204 miles, with chargers placed every 172 miles on a linear corridor. All public chargers should be deployed at the beginning of the planning horizon to achieve greater savings over the years. Sensitivity analysis reveals that the timeframe for the socially optimal conversion of 80% of the sample varies from 6 to 12 years. The optimal decision variables are sensitive to battery pack and vehicle body cost, gasoline cost, the discount rate, and conventional vehicles' fuel economy. Faster conventional vehicle replacement is achieved when the gasoline cost increases, electricity cost decreases, and battery packs become cheaper over the years.     

A comparative study of driving performance in metropolitan regions using large-scale vehicle trajectory data: Implications for sustainable cities

Volatile driving, characterized by hard accelerations and braking, can contribute substantially to higher energy consumption, tailpipe emissions, and crash risks. Drivers’ decisions to maintain speed, accelerate, brake rapidly, or jerk their vehicle are largely constrained by their unique regional and metropolitan contexts. These contexts may be characterized by their geography, roadway structure, traffic management, driving population, etc. This study captures how people generally drive in a region using large-scale vehicle trajectory data, implying how energy is consumed and how emissions are produced in regional transportation systems. Specifically, driving performance in four U.S. metropolitan areas (Los Angeles, San Francisco, Sacramento, and Atlanta) is compared, taking advantage of large-scale behavioral data (78.7 million seconds of speed records), collected by in-vehicle global positioning systems (GPSs) as part of regional surveys. Comparative analysis shows significant regional differences in terms of volatile driving and time spent to accelerate, brake, and jerk the vehicle during daily trips. Correlates of higher volatility are also explored, e.g., battery electric vehicles show low volatility, as expected. This study proposes a novel way to compare regional driving performance by successfully turning GPS driving data into valuable knowledge that can be applied in practice by developing regional driving performance indices. The new indices can also be used to compare regional performance over time and to imply the levels of sustainability of regional transportation systems. This study contributes by proposing a way to extract useful information from large-scale driving data.     

Light-duty electric vehicles in the gulf? Techno-economic assessment and policy implications

We investigate the economic and technological potential for adoption of Electric Vehicles (EVs) in Abu Dhabi through a parametric assessment, a public attitude survey, and a diffusion model. Abu Dhabi, similar to fossil-fuel-rich countries in its region, features a car-dependent society and energy subsidies while being situated in hot, desert environment challenging to EVs. We compare conventional vehicles and EVs in the region accounting for the higher use of air-conditioning (AC) in the actual climatic and driving pattern conditions of Abu Dhabi developing an EV AC consumption penalty model. Average annual AC penalty in fuel consumption over normal testing conditions is 16% for conventional vehicles and 25% for EVs based on hourly weather and vehicle utilization patterns. For 250 days per year, the expected EV range is higher than 75% of the nominal value. During the five hottest days of the recorded year, it can drop to 60% but still covering the average daily driving of the majority of users with a single charge with a 25kWh battery. With partial subsidies offered for both fuel and electricity, EV adoption impacts the state in terms of opportunity costs for these fuels in addition to environmental externalities. We calculate the state and user benefits in a parametric analysis dependent on driving distance and battery costs. If the battery cost is $325/kWh, direct and internalized benefits and costs are balanced when the EV is driven 60 km/day. A diffusion scenario of 5% by 2030 results in cumulative net savings of 6.4 million barrels of oil, a reduction of 517.000 tonnes of CO₂, and a cumulative net benefit of $127 million.     

Demand for electric vehicles in hybrid households: an exploratory analysis

Previous studies of the potential market for battery electric vehicles (BEVs) from different research streams have failed to converge on a single, robust estimate. What these previous research streams share are untested or implausible assumptions about consumer response to new transportation technology. We frame the BEV purchase decision in terms of a household's entire stock of vehicles, car purchase behavior and travel behavior. Within this framework, households which own both electric vehicles and gasoline vehicles are called ‘hybrid households’. Because nearly all consumers are unfamiliar with the characteristics of BEVs, we designed an interactive interview based on week-long travel diaries, which we call Purchase Intentions and Range Estimation Games (PIREG) to explore hypothetical hybrid household vehicle use. Our primary finding is that consumers' perceived driving range needs are substantially lower than previous hypothetical stated preference studies conclude. We find evidence of a viable market for BEVs with 60 to 100 miles driving range.     

Analysis of the effect of charging infrastructure design on electric taxi driving profiles: A case study approach on the example of Singapore

Replacing conventional vehicle taxis with electric vehicles would be an efficient measure to reduce greenhouse gas emissions. Due to the limited range and long charging times of current battery electric vehicles, it is of utmost importance to provide sufficient charging facilities. This article analyses the impact of the placement and charging power of charging stations on potential mileage and revenue of electric taxis on the example of Singapore. Therefore, we developed an agent-based electric taxi simulation model to investigate electric taxis’ driving profiles with respect to different vehicle types and charging infrastructure designs. This model is also capable of simulating conventional taxi driving profiles. The validation of these simulation results with real taxi data showed that the model is reproducing taxi driving profiles with high accuracy in great detail. We found out that electric taxis could reach the same mileage and revenue as conventional taxis if charging with a power of 160?kW is possible. Furthermore, we discovered that waiting times for available charging stations have a stronger effect on revenue than the length of detours to reach charging stations. Based on these findings, we concluded that it is more important to reduce waiting times by placing sufficient numbers of charging stations at each location before expanding the charging network by installing small numbers of charging stations at many locations.     

Toward a solar city: Trade-offs between on-site solar energy potential and vehicle energy consumption in San Francisco,California

This study demonstrates the trade-offs between vehicle energy consumption and on-site solar energy potential in a city landscape. While higher urban density may curb many of the problems associated with sprawl mainly by reducing vehicle travels and associated energy use, it can also limit on-site rooftop solar energy utilization due to more shade on rooftops in dense urban settings and less available rooftop area per person. Using travel survey, Geographic Information System (GIS) and Light Detection and Ranging (LiDAR) data, we estimated vehicle energy use and rooftop solar potential in the City of San Francisco as a case study and calculated possible offsetting effects between vehicle energy consumption and rooftop solar potential. Given the prevalence of gasoline-based vehicles and today's solar photovoltaic (PV) panel efficiency, vehicle energy use per capita appears to exceed energy generated by rooftop solar PVs per capita across all density ranges, especially in lower density environments. At the point when electric cars and advanced, highly efficient solar PV panels penetrate the market, the results change based on the combination of different technological options. A significant reduction of energy consumption can be achieved through the immediate and rapid spread of energy efficient technologies in vehicles and solar PVs along with the long-term effect from gradual urban densification.     

Fuel economy gaps within and across garages: A bivariate random parameters seemingly unrelated regression approach

The key objective of this study is to investigate the interrelationship between fuel economy gaps and to quantify the differential effects of several factors on fuel economy gaps of vehicles operated by the same garage. By using a unique fuel economy database (fueleconomy.gov), users’ self-reported fuel economy estimates and government’s fuel economy ratings are analyzed for more than 7000 garages across the United States. The empirical analysis, nonetheless, is complicated owing to the presence of important methodological concerns including potential interrelationship between vehicles within the same garage and unobserved heterogeneity. To address these concerns, bivariate seemingly unrelated fixed and random parameter models are presented. With government’s test cycle ratings tending to over-estimate the actual on-road fuel economy, a significant variation is observed in the fuel economy gaps for the two vehicles across garages. A wide variety of factors such as driving style, fuel economy calculation method, and several vehicle-specific characteristics are considered. Drivers who drive for maximum gas mileage or drives with the traffic flow have greater on-road fuel economy relative to the government’s ratings. Contrarily, volatile drivers have smaller on-road fuel economy relative to the official ratings. Compared to the previous findings, our analysis suggests that the relationship between fuel type and fuel economy gaps is complex and not unidirectional. Regarding several vehicle and manufacturer related variables, the effects do not just significantly vary in magnitude but also in the direction, underscoring the importance of accounting for within-garage correlation and unobserved heterogeneity for making reliable inferences.     

Heterogeneous preferences of green vehicles by vehicle size: Analysis of Seoul case

Although there are several policies for promoting green vehicles, green vehicles have yet to penetrate the market to the extent desired. To attain the goal, a complete understanding of consumers’ preferences of green vehicles is essential. This paper proposes and specifies the fuel-type choice models among conventional gasoline vehicles, hybrid electric vehicles, plug-in hybrid electric vehicles, and electric vehicles by vehicle size using the stated preferences data collected in Seoul, Korea. The results highlight the need to accommodate the correlation between similar alternatives and the unobserved heterogeneity within the context of choosing a green vehicle. The choice probabilities of green vehicles are affected by the relative impacts of the vehicles’ attributes and socio-demographic variables, and both of these variables are affected by the sizes of the vehicles. For compact size and mid-size vehicles, the effects of operating costs were less than those of purchase prices; however, for the subcompact vehicles, the effects of operating cost were greater than those of purchase price. The parameter of operating costs was not statistically significant in the full-size model. With respect to electric vehicles, the availability of fuel stations would be more important in Seoul than in the U.S. These results can also be useful for policy makers in that they provide information about the impact of green vehicles’ attributes on the choice probabilities of green vehicles.     

Emissions,performance, and design of UK passenger vehicles

Consumer, legal, and technological factors influence the design, performance, and emissions of light-duty vehicles (LDVs). This work examines how design choices made by manufacturers for the UK market result in emissions and performance of vehicles throughout the past decade (2001–2011). LDV fuel consumption, CO₂ emissions, and performance are compared across different combinations of air and fuel delivery system using vehicle performance metrics of power density and time to accelerate from rest to 100 km/h (62 mph, t_z-62). Increased adoption of direct injection and turbocharging technologies helped reduce spark ignition (SI, gasoline vehicles) and compression ignition (CI, diesel vehicles) fuel consumption by 22% and 19%, respectively, over the decade. These improvements were largely achieved by increasing compression ratios in SI vehicles (3.6%), turbocharging CI vehicles, and engine downsizing by 5.7–6.5% across all technologies. Simultaneously, vehicle performance improved, through increased engine power density resulting in greater acceleration. Across the decade, t_z-62 fell 9.4% and engine power density increased 17% for SI vehicles. For CI vehicles, t_z-62 fell 18% while engine power density rose 28%. Greater fuel consumption reductions could have been achieved if vehicle acceleration was maintained at 2001 levels, applying drive train improvements to improved fuel economy and reduced CO₂ emissions. Fuel consumption and CO₂ emissions declined at faster rates once the European emissions standards were introduced with SI CO₂ emissions improving by 3.4 g/km/year for 2001–2007 to 7.8 g/km/year thereafter. Similarly, CI LDVs declined by 2.0 g/km/year for 2001–2007 and 6.1 g/km/year after.     

Where are the electric vehicles? A spatial model for vehicle-choice count data

Electric vehicles (EVs) are predicted to increase in market share as auto manufacturers introduce more fuel efficient vehicles to meet stricter fuel economy mandates and fossil fuel costs remain unpredictable. Reflecting spatial autocorrelation while controlling for a variety of demographic and locational (e.g., built environment) attributes, the zone-level spatial count model in this paper offers valuable information for power providers and charging station location decisions. By anticipating over 745,000 personal-vehicle registrations across a sample of 1000 census block groups in the Philadelphia region, a trivariate Poisson-lognormal conditional autoregressive (CAR) model anticipates Prius hybrid EV, other EV, and conventional vehicle ownership levels. Initial results signal higher EV ownership rates in more central zones with higher household incomes, along with significant residual spatial autocorrelation, suggesting that spatially-correlated latent variables and/or peer (neighbor) effects on purchase decisions are present. Such data sets will become more comprehensive and informative as EV market shares rise. This work’s multivariate Poisson-lognormal CAR modeling approach offers a rigorous, behaviorally-defensible framework for spatial patterns in choice behavior.     

Infrastructure development for alternative fuel vehicles on a highway road system

A new mathematical model for positioning alternative fuel (AF) refueling stations on directed-transportation networks with the objective of maximizing the coverage of path flow volume is proposed. This model is especially designed for developing an AF infrastructure on toll roads and other highways, where vehicles do not need to exit the road network for refueling, some candidate station locations are not located at interchanges, and some stations may only service vehicles on one driving direction. The proposed model is applied to the Pennsylvania Turnpike System using the 2011 truck traffic data and considering different vehicle driving ranges.     

An analysis of per mile pollution fees for motor vehicles in California''s south coast

This paper examines the effect of pollution fees based on per mile emissions of reactive organic gases and oxides of nitrogen on tons of pollutants emitted, driving, fuel use and efficiency, new vehicle sales, and welfare in the South Coast region of California. The results suggest that VMT and gasoline demand would be reduced (although at a declining rate) while mpg would improve as households shifted to newer vehicles. Sales of new vehicles are projected to initially rise, then drop below base forecast levels as turnover of vehicles is reduced. Finally, the projected effects on household consumer surplus suggest that pollution fees may be regressive (although less so as households adjust their vehicle holdings), as a significant portion of older, higher polluting vehicles are owned by lower income households. The paper also compares pollution fees with voluntary accelerated vehicle retirement programs.     

Modeling green vehicle adoption: An integrated approach for policy evaluation

This article employs an integrated discrete-continuous car ownership model to jointly forecast households’ future preferences on vehicle type, quantity and use, and to estimate greenhouse gas (GHG) emissions. The model system is estimated on a dataset collected from a web-based stated preference survey conducted in Maryland in 2014. The data contain vehicle purchase decisions and sociodemographic information of 456 households who were requested to state their future preferences over a 9-year period (2014–2022). In each time period, a respondent is faced to four alternatives that include the current vehicle, a new gasoline vehicle, a new hybrid electric vehicle, and a new battery electric vehicle. Intertemporal choices between conventional and “green” vehicles such as hybrid and electric cars capture dynamics in vehicle purchase decisions. Short run and medium-long run situations were predicted and compared based on the first 4-year data and the entire 9-year data of the dynamic panel. Vehicle GHG emissions were calculated correspondingly. We find the introduction of “green” vehicles makes a positive impact on car ownership and use, especially in a medium-long run. Two “green” taxation policies, gasoline tax and ownership tax, were proposed and their impact on vehicle use and emission reductions was evaluated. Results indicate that: (a) gasoline tax is a more effective way to reduce vehicle miles traveled and GHG emissions and (b) gasoline tax makes a higher impact on car use and emission reductions in the medium-long run, while ownership tax makes a higher impact in the short run.     

Impulsive consumption in the experiential context

Experiential consumption includes the retention and recollection process of both positive feelings and memorable experiences after any onsite experiential consumption has ended. In order to better understand the unique characteristics of experiential consumption with regard to leisure and tourism, this study investigated impulsive consumption in the event tourism context. A sample of 83 attendees of a cultural art event was used to compare their intentional consumption with actual consumption on six spending categories. An onsite PDA survey coupled with a post hoc online survey was employed for the repeated measures method. The findings showed that there were significant differences between intentional and actual spending on dining and transportation as well as on the total amount of spending. Leisure and tourism destination marketers are advised to develop more situational and experiential products to increase positive impulsive consumption.     

Selecting sustainable electric bus powertrains using multipreference evolutionary algorithms

Constant improvement of vehicle technologies towards more efficient powertrains and reduced pollutant emissions, frequently leads to the increase of the vehicle or fuel costs, compromising its viability. Multi-objective optimization methods are commonly used to solve such problems, finding optimal trade-off solutions relatively conflicting objectives. Nevertheless, vehicle driving performance, is often disregarded from the optimization process or considered only as a fixed constraint. This may raise some issues, which are discussed in this paper: (a) vehicle dynamics are not improved, (b) trade-off optimal solutions are not distinguishable, (c) interesting solutions near constraints limits won´t be considered if constraints are not marginally relaxed.

This paper proposes a method to optimize three electric-drive vehicle options for an urban bus, a battery electric (BEV), a fuel cell hybrid (FC-HEV) and a plug-in hybrid (FC-PHEV), aiming minimum carbon footprint, maximum financial indicator and simultaneously improved driving performance (speed, acceleration, and electric range). The carbon footprint is assessed by a life cycle (LC) approach, considering the impact of the fuel production and use, and vehicle embodied materials; while the financial assessment considers the vehicle and fuel costs. The spherical pruning multi-objective differential evolution algorithm (spMODE-II) is used in the optimization, considering different preference regions within the problem constraints and objectives. The vehicle solutions optimality and suitability are compared with other multi-objective algorithm, NSGA-II.

The FC-HEV achieved the lowest LC emissions (547 g/km), and the FC-PHEV the maximum financial gain (0.19 $/km), while the BEV achieved the best trade-off of solutions.     

Fair profit contract for a carrier collaboration framework in a green hub network under soft time-windows: Dual lexicographic max–min approach

This paper models a novel and practical bi-objective hub-location problem under a centralized carrier collaboration framework between one holding company and multiple carriers. The holding company first establishes a hub-and-spoke network in order to locate p hubs and to assign the center nodes to the located hubs. Then, it allocates the transportation routes of the hub network to the carriers. In contrast, the carriers should select an appropriate vehicle type to serve the transportation requests in a green hub network. The carriers are also able to meet the transportation requests within a certain time-window based on a soft time-window mechanism. Moreover, aiming to emphasize green transportation, a vehicle emission model is used to take into account CO₂ emissions of the vehicles where the fuel consumption is a function of speed level. Aiming to identify a win–win deal between the holding company and the carriers, a dual lexicographic max–min (LMM) approach is used in order to optimize their profits in a fair way. Finally, some numerical experiments are presented to demonstrate the applicability of the proposed methodology. The computational results show that not only the holding company and the carriers can better generate a fair profit contract among themselves using the LMM approach, but also both can obtain more profit in the worst case for their businesses rather than using the max–min approach. In addition, sensitivity analyses show that increasing the size of the soft time-window leads to a reduction in the delivery schedule violations, while results in raising the total profit. Moreover, the tax cost of fuel consumption as well as the number of potential vehicles has a substantial impact on both the fuel consumption and carrier’s profit.