Fuel-efficient Vehicles Research Articles

The impact of emissions-based taxes on the retirement of used and inefficient vehicles: The case of Switzerland

Previous evaluations of emissions-based car taxes have estimated their impact on the shares or sales of new vehicles. In contrast, we ask whether taxes that penalize high emitters induce changes in the retirement of existing and inefficient vehicles. We exploit natural experiment conditions in Switzerland to analyze the impact of three different “bonus”/“malus” annual registration fee schemes implemented at the cantonal level. In the three schemes, the bonus rewards new, fuel-efficient vehicles. The malus is retroactive in canton Obwalden, i.e. it is charged on both new and existing high-emitting cars, but prospective in Geneva and Ticino. We use a difference-in-difference design within a survival analysis framework to assess direct treatment effects and spillover effects. While the bonus/malus in Obwalden hastens the retirement of existing high-emitting vehicles (by around 5%), the scheme in Geneva postpones retirement (by some 3%). We find no statistically significant effects in Ticino. These findings have important implications about the desirability of bonus/malus schemes and on their design, as well as on old car scrappage programs.

Blue-Agave Fiber-Reinforced Polypropylene Composites for Automotive Applications

As consumer demand for more fuel-efficient vehicles increases, automakers are looking for innovative ways to reduce the weight of vehicles. Many automotive-grade plastics contain traditional reinforcing fillers, such as glass or talc, to improve the mechanical properties of the material. By replacing these high-density fillers with natural fibers, the material and corresponding weight can be reduced, which results in an improvement of the vehicle fuel economy. The objective of this study was to investigate the use of blue-agave bagasse fibers, which was sourced from tequila manufacturing waste, as a reinforcing agent in polypropylene composites. The effects of the fiber processing method, fiber loading level, and addition of a compatibilizer (polypropylene-grafted maleic anhydride) on the composite properties were determined. Samples were produced via twin-screw extrusion and injection molding. The resulting mechanical properties and morphology of the fracture surfaces were investigated. The fiber processing method (Agave C vs. Agave R) did not significantly affect the composite properties. Higher loading levels of fiber reduced both the elongation at break and impact strength, but increased the stiffness of the agave composites. The compatibilizer increased the fiber matrix adhesion, but reduced impact strength because the polymer matrix was softened.

Greenhouse gas impacts of different modality style classes using latent class travel behavior model

This study analyzed the interconnections between urban structure and socioeconomic, demographic and lifestyle variables and direct ground transport greenhouse gas (GHG) emissions in the Helsinki metropolitan region in Finland by using a latent class choice model (LCCM). The aim of the study was to identify and improve our understanding of the latent modality styles which guide people's everyday travel choices, and the resulting GHG implications. The GHG implications for different modality style groups are particularly interesting, since such analysis has not been included in previous studies. This study used the transport survey of the Helsinki Region Transport (HSL) from 2012, including over 17,000 trips, and consisted of two parts: first, the population was divided into classes based on latent class analysis, and secondly, the mode choices and GHG emissions were modelled for each class. Seven modality style groups were defined with strongly varying GHG impacts and travel profiles. According to the class specific choice modelling the probabilities of selecting different modes also varied significantly. The study offers new information for designing effective mitigation policies for different types of modality style groups. For example, car-oriented groups would benefit from more fuel-efficient vehicles and vehicles with alternative power sources, whereas the multimodal traveler could be more receptive to policies promoting cycling and public transport. Overall, the study depicts the potential of the latent class method to study the emissions caused by heterogeneous populations and to search for efficient GHG mitigation possibilities.

Future Trends and Mitigation Options for Energy Consumption and Greenhouse Gas Emissions in a Developing Country of the Middle East Region: a Case Study of Lebanon’s Road Transport Sector

The road transport system in Lebanon is one of the most unsustainable in the Middle East region due to, in large part, the absence of a national transportation strategy. This study proposes mitigation measures based on Lebanon’s commitments for reducing fossil fuel use and CO2 emissions from road transport by increasing the share of fuel-efficient and hybrid electric vehicles and increasing the utilization of the existing bus service. Results show that increasing the market share of fuel-efficient vehicles to 35% in 2040 stabilizes energy use and emissions. The addition of hybrid vehicles to the first strategy, with a target of 10% market share by 2040, leads to 11% additional savings. Increasing the share of bus passenger kilometers traveled to 45% in 2040 leads to a reversal of adverse impacts. A combined strategy of all three measures leads to 63% reductions in 2040 compared to 2010, which is even superior to their cumulative savings.

Distributed receding horizon control for fuel-efficient and safe vehicle platooning

This paper investigates the problem of fuel-efficient and safe control of autonomous vehicle platoons. We present a two-part hierarchical control method that can guarantee platoon stability with minimal fuel consumption. The first part vehicle controller is derived in the context of receding horizon optimal control by constructing and solving an optimization problem of overall fuel consumption. The Second part platoon controller is a complementation of the first part, which is given on the basis of platoon stability analysis. The effectiveness of the presented platoon control method is demonstrated by both numerical simulations and experiments with laboratory-scale Arduino cars.

Density and Scale Economies in the Public Road Transport: Empirical Evidence for Tunisia

This paper examines economies of scale and density in road transport. This study seeks to broaden the knowledge in the impact of the economies of scale and density drawn in and by the road transport sector in Tunisia. This investigation aims to not only grasp the possibility of a substitutability of the production factors used but also highlight the importance of the global productivity achieved in the operation of means of transport for the users and the community. The investigation of the 12 road transport companies in Tunisia reveals the existence of a homogeneity in the total production of these companies which vary with the size of the networks, the number of stops, and the number of employees, although it exists a strong heterogeneity concerning. This study shows the importance of enhancing a number of policies recommendations for the Tunisian road transport system through the joint improvement of the road infrastructure policy, of the fuel price policy, and of the fuel-efficient road vehicle policy in order to maintain sustainable road transport network.

Study of Friction-Reduction Properties of Fatty Acids and Adsorption Structures of their Langmuir–Blodgett Monolayers using Sum-Frequency Generation Spectroscopy and Atomic Force Microscopy

The establishment of a sustainable society requires fuel-efficient vehicles in which lubricants play important roles. Blending optimum amounts of friction modifiers with lubricant is widely used to reduce fuel consumption caused by frictional loss. However, selection of the appropriate blending still strongly depends on experience. To develop a general selection role for the friction-reduction additives based on a quantitative standard, a number of C18-based fatty acids have been evaluated on the solid surfaces by different methods in the present study. In addition to the measurement of the friction coefficient (μ), the adsorption structures of the Langmuir–Blodgett monolayers of the fatty acids and their effects on the friction-reduction behaviors have been investigated using surface pressure–area (π–A) isotherms, sum-frequency generation vibrational spectroscopy and atomic force microscope. The present experimental results demonstrate a certain relationship between the adsorption structures of the fatty acids on the solid surface and their friction-reduction behaviors.

Service Region Design for Urban Electric Vehicle Sharing Systems

Emerging collaborative consumption business models have shown promise in terms of both generating business opportunities and enhancing the efficient use of resources. In the transportation domain, car-sharing models are being adopted on a mass scale in major metropolitan areas worldwide. This mode of servicized mobility bridges the resource efficiency of public transit and the flexibility of personal transportation. Beyond the significant potential to reduce car ownership, car sharing shows promise in supporting the adoption of fuel-efficient vehicles, such as electric vehicles (EVs), because of these vehicles’ special cost structure with high purchase but low operating costs. Recently, key players in the car-sharing business, such as Autolib’, car2go, and DriveNow, have begun to employ EVs in an operations model that accommodates one-way trips. On the one hand (and particularly in free-floating car sharing), the one-way model results in significant improvements in coverage of travel needs and therefore in adoption potential compared with the conventional round-trip-only model (advocated by Zipcar, for example). On the other hand, this model poses tremendous planning and operational challenges. In this work, we study the planning problem faced by service providers in designing a geographical service region in which to operate the service. This decision entails trade-offs between maximizing customer catchment by covering travel needs and controlling fleet operation costs. We develop a mathematical programming model that incorporates details of both customer adoption behavior and fleet management (including EV repositioning and charging) under imbalanced travel patterns. To address inherent planning uncertainty with regard to adoption patterns, we employ a distributionally robust optimization framework that informs robust decisions to overcome possible ambiguity (or lacking) of data. Mathematically, the problem can be approximated by a mixed integer second-order cone program, which is computationally tractable with practical scale data. Applying this approach to the case of car2go’s service with real operations data, we address a number of planning questions and suggest that there is potential for the future development of this service. The online appendix is available at https://doi.org/10.1287/msom.2016.0611 .

The Impact of Emissions-Based Taxes on the Retirement of Used and Inefficient Vehicles: The Case of Switzerland

Many countries have adopted policies designed to reduce CO2 emissions from road vehicles. Taxes linked to the CO2 emissions rate or the fuel economy of a vehicle (which is inversely related to its CO2 emissions rate) are examples of such policies. These taxes are usually imposed on new vehicles, and previous evaluations have estimated the increases in the shares or sales of new and fuel-efficient vehicles associated with such taxes. In contrast, we ask whether taxes on new cars that penalize high emitters induce changes in the retirement of used and inefficient vehicles. We exploit natural experiment conditions in Switzerland to analyze the impact of two different “bonus”/“malus” schemes implemented at the cantonal level. In both schemes, the bonus rewards new efficient vehicles. The malus is retroactive in canton Obwalden, in the sense that it is charged on both new and existing high-emitting cars, but it is only applied prospectively to new cars in Geneva. We use a difference-in-difference design within a survival analysis setting. We find that a bonus/malus accelerates the retirement of existing high-emitting vehicles in Obwalden, shortening the expected lifetime of the three most popular make-models by 7 to 11 months. The effect is the opposite in Geneva, where we estimate that the expected lifetime of these three popular models is extended by 5 to 8 months. These findings have important implications about the desirability of bonus/malus schemes and on their design, as well as on old car scrappage programs.

A comprehensive framework for automotive sustainability assessment

Business efficiency, stakeholder pressure and the need for legislative compliance compel the automotive sector to design and manufacture fuel-efficient, low-impact, environmentally responsible and sustainable vehicles. Managing and responding to these multiple and sometimes conflicting interests requires the measurement of economic, environmental and societal performance. Although a number of automotive sustainability measures are mentioned within the literature, there is no single and unique approach for the complete and integrated sustainability assessment of vehicles. This study has developed a comprehensive automotive sustainability assessment framework by selecting a set of sustainability assessment criteria from the literature and refining these through an interview study with 24 automotive experts from academia, car manufacturers, consultancies and non-governmental organisations. Based on this approach, 26 midpoint and 9 end-point environmental, resource, social and economic impact categories have been identified for the construction of a framework for automotive sustainability assessment. The proposed framework can be used as a decision-supporting tool at the early stages of the vehicle development process. It allows source and sustainability issues to be identified throughout the entire vehicle life cycle and provides the means to sharpen analysis and discussion around these issues. The framework can also serve as a design structure for a wide range of sustainability assessment methods and tools (e.g. multi-criteria decision adding or sustainability accounting methods). It serves as guidance on what needs to be measured in an integrated sustainability assessment of vehicles and leaves the choice of what to include in the decision-making process to the discretion of individual companies.

Gasoline Price and New Vehicle Fuel Efficiency: Evidence from Canada

Using data on all vehicles registered in Canadian provinces from 2000-2010, we estimate the elasticity of the fuel economy of the new vehicle stock with respect to gasoline price. We demonstrate that a 10% increase in gasoline price causes a 0.8% improvement in the fuel economy of new vehicles. We show that consumers in dense urban areas respond more to changes in fuel price than other consumers and that fuel taxes cause a much larger response in vehicle fuel economy than other components of the gasoline price. This finding has important implications for the assessment of market-based policies for reducing greenhouse gas emissions.

US vehicle fuel-efficiency choices: demographic, behavioral, and cultural factors

In the United States, a large proportion of greenhouse gas emissions are from personal vehicle use, and there are large differences in emissions between the most and least efficient vehicles. We use data from 2009 US National Household Travel Survey to characterize consumers who drive the most and least efficient vehicles. We calculate personal fleet efficiency based on all vehicles owned, reflecting the behavioral aspect of vehicle-use choices by owners. Though higher incomes provide the means to purchase more efficient vehicles, we find those with higher incomes more likely to drive low-efficiency vehicles. Higher education levels correlate with greater vehicle efficiency. While some efficiency differences are based on consumer characteristics such as family size, we find large differences that we attribute mostly to cultural preferences as observed by race, region, etc. From a policy perspective, these differences are of particular interest since they may be amenable to change.

Computational aero-acoustic modelling of external rear-view mirrors on a mid-sized Sedan

Ever-rising fuel costs necessitate design of fuel-efficient vehicles. Consequently, modern vehicle manufacturers are focused on designing low aerodynamic drag vehicles which would in-turn reduce the fuel consumption. This study analyses the contribution of external rear-view mirrors to the total drag force and the overall sound pressure level at the A, B and C pillars, while optimising the external rear-view mirror design accordingly. Solid Works renditions of external rear-view mirror models mounted on a reference luxury sedan were analysed using a commercially available computational fluid dynamic package ANSYS FLUENT. A different approach was followed to carry out the empirical flow visualisation and predict sound pressure levels. The aerodynamic characterisation of the vehicle was done utilising the widely used shear stress transport turbulence model, while the analysis of wind noise and the contributing vortices employed a large eddy simulation. This approach significantly reduced computational time without compromising on accuracy.

A study on the effects of the CAFE standard on consumers

In this paper, we analyzed how the CAFE standard has affected improvements in the fuel economy of vehicles, as examined in other preceding studies, but in addition, we also analyzed how these standards have affected the level of consumer interest in fuel economy. Our goal was to determine what effects the government intervention has had on consumers, and whether such intervention ought to be continued. The results showed that not only has the CAFE standard had a direct and significant impact on improving fuel economy and increasing the market share of fuel-efficient vehicles, it has also boosted the development of technologies for enhancing fuel economy and raised consumer interest in fuel economy, thus indirectly contributing to overcoming market failure. The significance of this study is that we used publically available observed data and analyzed the recent impact of the CAFE standard specifically with a focus on the behavior and strategies exhibited by consumers and automakers. Another significance of this study is that it extends our purview to examine the effects that the CAFE standard has had in other countries (Korea).

HEV Optimal Battery State of Charge Prediction: A Time Series Inspired Approach

Fuel efficiency in hybrid electric vehicles requires a fine balance between combustion engine usage and battery energy, using a carefully designed control algorithm. Owing to the transient nature of HEV dynamics, driving conditions prediction, have unavoidably become a vital part of HEV energy management. The use of vehicle onboard telematics for driving conditions prediction have been widely researched and documented in literature, with most of these studies identifying high equipment cost and lack of route information (for routes unfamiliar to the GPS) as factors currently impeding the commercialization of predictive HEV control using telematics. In view of this challenge, this study inspires a look-ahead HEV energy management approach, which uses time series predictors (neural networks or Markov chains), to forecast future battery state of charge, for a given horizon, along the optimal front (optimal battery state of charge trajectory). The primary contribution of this paper is a detailed theoretical appraisal and comparison of the neural network and Markov chain time series predictors over different driving scenarios (FTP72, SC03, ARTEMIS U130 and WLTC 3 driving cycles). Based on the analysis performed in this study, the following useful inferences are drawn: 1. Prediction accuracy decreases massively and disproportionately on average with increased prediction horizon for multi-input neural networks, 2. In a single-input/single-horizon prediction network, the performance of both the neural network and Markov chain predictors are similar and near optimal, with a mean absolute percentage error of less than 0.7% and a root mean square error of less than 0.6 for all driving cycles analysed, 3. Markov chains appeal as a promising time series predictor for online vehicular applications, as it impacts the relative advantage of high precision and moderate computation time.

Service Region Design for Urban Electric Vehicle Sharing Systems

Emerging collaborative consumption business models have shown promise in terms of both generating business opportunities and enhancing the efficient use of resources. In the transportation domain, car sharing models are being adopted on a mass scale in major metropolitan areas worldwide. This mode of servicized mobility bridges the resource efficiency of public transit and the flexibility of personal transportation. Beyond the significant potential to reduce car ownership, car sharing shows promise in supporting the adoption of fuel-efficient vehicles, such as electric vehicles (EVs), due to these vehicles’ special cost structure with high purchase but low operating costs. Recently, key players in the car sharing business, such as Autolib, Car2Go and DriveNow, have begun to employ EVs in an operations model that accommodates one-way trips. On the one hand (and particularly in free-floating car sharing), the one-way model results in significant improvements in coverage of travel needs and therefore in adoption potential compared with the conventional round-trip-only model (advocated by ZipCar, for example). On the other hand, this model poses tremendous planning and operational challenges. In this work, we study the planning problem faced by service providers in designing a geographical service region in which to operate the service. This decision entails trade-offs between maximizing customer catchment by covering travel needs and controlling fleet operations costs. We develop a mathematical programming model that incorporates details of both customer adoption behavior and fleet management (including EV repositioning and charging) under imbalanced travel patterns. To address inherent planning uncertainty with regard to adoption patterns, we employ a distributionally robust optimization framework that informs robust decisions to overcome possible ambiguity (or lacking) of data. Mathematically, the problem can be approximated by a mixed integer second-order cone program, which is computationally tractable with practical scale data. Applying this approach to the case of Car2Go's service with real operations data, we address a number of planning questions and suggest that there is potential for the future development of this service.

Evaluating the Economic and Social Implications of Participation in Clean Vehicle Rebate Programs: Who’s In, Who’s Out?

To motivate consumers to buy fuel-efficient vehicles, governments have established national and state incentives to change purchasing behaviors. With California’s Clean Vehicle Rebate Project (CVRP) as a case study, this paper assesses the distribution of rebates across census tracts and socioeconomic divisions. Race–ethnicity, income, and socioeconomic and environmental disadvantage were used to understand variations in rebate allocation across census tracts in California between 2010 and 2015. Ordinary least squares (OLS) and negative binomial regressions were conducted to identify the definitive effect that income played in obtaining rebates: wealthier census tracts secure more rebates. Furthermore, this analysis determined a significant and negative relationship between the proportion of Hispanic and African-American residents and the number of rebates received per household, even when controlling for income. These findings suggest that the distribution of CVRP rebates is problematic across economic and racial–ethnic lines, especially given current policies pertaining to climate change equity. This paper aims to inform researchers and policy makers about the barriers of rebate access and provides a discussion to address this de facto bias in rebate allocation by income and race–ethnicity.

Development of Advanced High Strength Steel for Improved Vehicle Safety, Fuel Efficiency and CO2 Emission

Global warming and green house gas emissions are the major issues worldwide and their impacts are clearly visible as a record high temperatures, rising sea, and severe ‘flooding and droughts’. Motor vehicles considered as a major contributor on global warming due to its green house gas emissions. Hence, the automobile industries are under tremendous pressure from government and society to reduce green house gas emission to maximum possible extent. In present work, Dual Phase steel with boron as microalloying is manufactured using thermo-mechanical treatment during hot rolling. Dual phase steel with boron microalloying improved strength by near about 200 MPa than dual phase steel without boron. The boron added dual phase steel can be used for manufacturing stronger and a lighter vehicle which is expected to perform positively on green house gas emissions. The corrosion resistance behavior is also improved with boron addition which would further increase the life cycle of the vehicle even under corrosive atmosphere.

International comparisons of energy and environmental efficiency in the road transport sector

The present work provides an international comparison of the energy intensity and the carbon dioxide intensity in road transport for a group of 90 countries over the period 1980–2012. This paper attempts to perform a comparative analysis to find the most appropriate mapping of the energy performance in road transport taking into account the three dimensions of sustainable energy development, namely road transport-related energy consumption, economic growth and carbon dioxide emissions. An important result of the study is the inverse relationship between energy efficiency and environmental efficiency. Through the calculated Theil coefficient, our empirical findings highlight the existence of spatial and temporal disparities between countries. In 2012, Tunisia occupies the 48th and the 38th rank respectively in terms of energy and environmental efficiency. Based on a general index of energy performance in the road transport sector, it is deemed to have a medium–high energy performance by occupying the 34th rank. The study shows the importance of enhancing a number of policies for the road transport system through the joint improvement of the fuel price policy, of the road infrastructure policy and of the fuel-efficient road vehicles policy, in order to maintain sustainable energy road transport.

Vehicle fuel-efficiency choices, emission externalities, and urban sprawl

This paper shows that a city where both a congestion externality and an externality from greenhouse gas emissions are corrected by efficient policies is more compact than the laissez-faire equilibrium city. Motivated by recent empirical studies showing a positive relationship between population density and vehicle fuel-efficiency, the consumer is assumed to choose vehicle fuel-efficiency jointly with housing consumption and residential location. By incorporating the consumer׳s vehicle choice into the urban spatial model, we can represent the total amount of vehicle emissions released by the city residents. We first establish the well-known result that the congestion externality as a source of market failure is associated with excessive urban sprawl. We then show that vehicle emissions are an additional source of market failure, which also leads to excessive urban sprawl. The source of excessive sprawl arising from the emission externality is the use of larger and less-fuel efficient vehicles in more sprawled cities, which is different from that of the congestion externality. We also analyze the effect of the Corporate Average Fuel Economy (CAFE) standards on urban spatial structure and its efficacy as a second-best tool for correcting the emission externality.