Estimating fuel consumption and emissions based on reconstructed vehicle trajectories

Microscopic emission models are widely used in emission estimation and environment evaluation. Traditionally, microscopic traffic simulation models and probe vehicles are two sources of inputs to a microscopic emission model. However, they are not effective in reflecting all vehicles' real‐world operating conditions. Using each vehicle's spot speed data recorded by detectors, this paper provides a new method to estimate all vehicles' real‐world activities data. These data can then be used as inputs to a microscopic emission model to estimate vehicle fuel consumption and emissions. The main task is to reconstruct trajectory of each vehicle and calculate second‐by‐second speed and acceleration from the activities data. The Next Generation Simulation dataset and the Comprehensive Modal Emissions Model are used in this study to calculate and analyze the emission results for both lane‐level and link‐level. The results showed that using the proposed method for estimating vehicle fuel consumption and emissions is promising. Copyright © 2012 John Wiley & Sons, Ltd.     

The effect of uncertainty on US transport-related GHG emissions and fuel consumption out to 2050

The future of US transport energy requirements and emissions is uncertain. Transport policy research has explored a number of scenarios to better understand the future characteristics of US light-duty vehicles. Deterministic scenario analysis is, however, unable to identify the impact of uncertainty on the future US vehicle fleet emissions and energy use. Variables determining the future fleet emissions and fuel use are inherently uncertain and thus the shortfall in understanding the impact of uncertainty on the future of US transport needs to be addressed. This paper uses a stochastic technology and fleet assessment model to quantify the uncertainties in US vehicle fleet emissions and fuel use for a realistic yet ambitious pathway which results in about a 50% reduction in fleet GHG emissions in 2050. The results show the probability distribution of fleet emissions, fuel use, and energy consumption over time out to 2050. The expected value for the fleet fuel consumption is about 450 and 350 billion litres of gasoline equivalent with standard deviations of 40 and 80 in 2030 and 2050, respectively. The expected value for the fleet GHG emissions is about 1360 and 850 Mt CO₂ equivalent with standard deviation of 130 and 230 in 2030 and 2050 respectively. The parameters that are major contributors to variations in emissions and fuel consumption are also identified and ranked through the uncertainty analysis. It is further shown that these major contributors change over time, and include parameters such as: vehicle scrappage rate, annual growth of vehicle kilometres travelled in the near term, total vehicle sales, fuel economy of the dominant naturally-aspirated spark ignition vehicles, and percentage of gasoline displaced by cellulosic ethanol. The findings in this paper demonstrate the importance of taking uncertainties into consideration when choosing amongst alternative fuel and emissions reduction pathways, in the light of their possible consequences.     

Assessing additional fuel consumption from cabin thermal comfort and auxiliary needs on the worldwide harmonized light vehicles test cycle

Standards for fuel consumption and carbon dioxide emissions are implemented worldwide in most light-duty vehicle markets. Regulatory drive cycles, defined as specific time-speed patterns, are used to measure levels of fuel consumption and emissions. These measurements should realistically reflect real world driving performance, however there is increasing concern about their adequacy due to the discrepancies observed between certified and real world consumption and emissions values. One of the main reasons for the discrepancy is that current testing protocols do not account for non-mechanical vehicle energy needs, such as passengers’ thermal comfort needs and the use of electric auxiliaries on-board. Cabin heating and cooling can especially lead to considerable increase in vehicle energy consumption. This paper presents a simulation-based assessment framework to account for the additional fuel consumption related to the cabin thermal energy and auxiliary needs under the worldwide-harmonized light vehicles test procedure (WLTP). A vehicle cabin model is developed and the thermal comfort energy needs are derived for cooling and heating, depending on ambient external temperature under cold, moderate and warm climates. A modification to the WLTP is proposed by including the generated power profiles for thermal comfort and auxiliary needs. Dynamic programming is used to compute the fuel consumption on the modified WLTP for a rechargeable series hybrid electric vehicle (SHEV) architecture. Results show consumption increases of 20% to 96% compared to the currently adopted WLTP, depending on the considered climate.     

Recent advances in automotive technology and the cost-effectiveness of fuel economy improvement

The potential for improving the fuel economy of conventional, gasoline-powered automobiles through optimized application of recent technology advances is analyzed. Results are presented at three levels of technical certainty, ranging from technologies already in use to technologies facing technical constraints (such as emissions control problems) which might inhibit widespread use. A fleet-aggregate, engineering-economic analysis is used to estimate a range of U.S. new car fleet average fuel economy levels achievable given roughly 10 years of lead time. Technology cost estimates are compared to fuel savings in order to determine likely cost-effective levels of fuel economy, which are found to range from 39 miles per gallon to 51 miles per gallon depending on technology certainty level. The corresponding estimated increases in average new car price range from $540 to $790 (1993$). Estimated fuel savings payback times average less than 3 years and the cost of conserved energy averages $0.50 per gallon, indicating that these levels of fuel economy improvement are cost-effective over a vehicle lifetime. A vehicle stock turnover model is used to project the reductions in gasoline consumption and associated emissions that would follow if the estimated fuel economy levels are achieved. Potential trade-offs regarding vehicle performance, safety, and emissions are also discussed.     

VMT, energy consumption, and GHG emissions forecasting for passenger transportation

Globalization, greenhouse gas emissions and energy concerns, emerging vehicle technologies, and improved statistical modeling capabilities make the present moment an opportune time to revisit aggregate vehicle miles traveled (VMT), energy consumption, and greenhouse gas (GHG) emissions forecasting for passenger transportation. Using panel data for the 48 continental states during the period 1998-2008, the authors develop simultaneous equation models for predicting VMT on different road functional classes and examine how different technological solutions and changes in fuel prices can affect passenger VMT. Moreover, a random coefficient panel data model is developed to estimate the influence of various factors (such as demographics, socioeconomic variables, fuel tax, and capacity) on the total amount of passenger VMT in the United States. To assess the influence of each significant factor on VMT, elasticities are estimated. Further, the authors investigate the effect of different policies governing fuel tax and population density on future energy consumption and GHG emissions. The presented methodology and estimation results can assist transportation planners and policy-makers in determining future energy and transportation infrastructure investment needs.     

Cutting vehicle emissions with regenerative braking

This paper presents an analysis of vehicle regenerative braking systems as a quick and relatively easy means of achieving higher overall fuel efficiency and lowering carbon emissions. The system involves the installation of an additional electric motor/generator in parallel to the vehicle’s internal combustion engine and is used in conjunction with a DCDC converter and ultracapacitor. The system is used to recapture the energy lost in vehicle braking, significantly reducing a vehicle’s overall energy consumption and lowering vehicle emissions. Experimentally-based evidence is collected and compared for two sample vehicles to deduce the potential fuel and emissions saving.     

A valuation of the environmental performance of vehicles: an analysis and comparison of two methodologies

The European Clean Vehicle Directive was introduced in 2009 to create an obligation on public authorities to take into account the impact of energy consumption, carbon dioxide (CO₂) emissions and pollutant emissions into their purchasing decisions for road transport vehicles. This should stimulate the market for clean and energy-efficient vehicles and improve transport's impact on environment, climate change and energy use. Therefore the so-called ‘Operational Lifetime Cost’ of a vehicle is calculated, divided into the cost for energy consumption, CO₂ and pollutant (nitrous oxide, particulate matter, non-methane hydrocarbons) emissions. In Belgium, a different methodology has been developed to calculate the environmental impact of a vehicle, called ‘Ecoscore’, based on a well-to-wheel approach. More pollutants are included compared to the Clean Vehicle methodology, but also indirect emissions are taken into account. In this paper, both methodologies are compared and used to analyze the environmental performance of passenger cars with different fuel types and from different vehicle segments. Similar rankings between both methodologies are obtained; however, the large impact of energy use (and CO₂ emissions) in the Clean Vehicle methodology disadvantages compressed natural gas cars, as well as diesel cars equipped with particulate filters, compared to the Ecoscore methodology.     

Fuel price elasticities in the U.S. combination trucking sector

This paper estimates fuel price elasticities of combination trucking operations in the United States between 1970 and 2012. We evaluate trucking operations in terms of vehicle miles traveled and fuel consumption for combination trucks. Our explanatory variables include measures of economic activity, energy prices, and indicator variables that account for important regulatory shifts and changes in data collection and reporting in national transportation datasets. Our results suggest that fuel price elasticities in the United States’ trucking sector have shifted from an elastic environment in the 1970s to a relatively inelastic environment today. We discuss the importance of these results for policymakers in light of new policies that aim to limit energy consumption and reduce greenhouse gas emissions from heavy-duty vehicles.     

Eco-driving: An economic or ecologic driving style?

In this work the trade-off between economic, therefore fuel saving, and ecologic, pollutant emission reducing, driving is discussed. The term eco-driving is often used to refer to a vehicle operation that minimizes energy consumption. However, for eco-driving to be environmentally friendly not only fuel consumption but also pollutant emissions should be considered. In contrast to previous studies, this paper will discuss the advantages of eco-driving with respect to improvements in fuel consumption as well as pollutant gas emissions. Simulating a conventional passenger vehicle and applying numerical trajectory optimization methods best vehicle operation for a given trip is identified. With hardware-in-the-loop testing on an engine test bench the fuel and emissions are measured. An approach to integrate pollutant emission and dynamically choose the ecologically optimal gear is proposed.     

Customizing driving cycles to support vehicle purchase and use decisions: Fuel economy estimation for alternative fuel vehicle users

Wider deployment of alternative fuel vehicles (AFVs) can help with increasing energy security and transitioning to clean vehicles. Ideally, adopters of AFVs are able to maintain the same level of mobility as users of conventional vehicles while reducing energy use and emissions. Greater knowledge of AFV benefits can support consumers’ vehicle purchase and use choices. The Environmental Protection Agency’s fuel economy ratings are a key source of potential benefits of using AFVs. However, the ratings are based on pre-designed and fixed driving cycles applied in laboratory conditions, neglecting the attributes of drivers and vehicle types. While the EPA ratings using pre-designed and fixed driving cycles may be unbiased they are not necessarily precise, owning to large variations in real-life driving. Thus, to better predict fuel economy for individual consumers targeting specific types of vehicles, it is important to find driving cycles that can better represent consumers’ real-world driving practices instead of using pre-designed standard driving cycles. This paper presents a methodology for customizing driving cycles to provide convincing fuel economy predictions that are based on drivers’ characteristics and contemporary real-world driving, along with validation efforts. The methodology takes into account current micro-driving practices in terms of maintaining speed, acceleration, braking, idling, etc., on trips. Specifically, using a large-scale driving data collected by in-vehicle Global Positioning System as part of a travel survey, a micro-trips (building block) library for California drivers is created using 54 million seconds of vehicle trajectories on more than 60,000 trips, made by 3000 drivers. To generate customized driving cycles, a new tool, known as Case Based System for Driving Cycle Design, is developed. These customized cycles can predict fuel economy more precisely for conventional vehicles vis-à-vis AFVs. This is based on a consumer’s similarity in terms of their own and geographical characteristics, with a sample of micro-trips from the case library. The AFV driving cycles, created from real-world driving data, show significant differences from conventional driving cycles currently in use. This further highlights the need to enhance current fuel economy estimations by using customized driving cycles, helping consumers make more informed vehicle purchase and use decisions.     

Development of two driving cycles for utility vehicles

Driving cycles are used to assess vehicle fuel consumption and pollutant emissions. The premise in this article is that suburban road-work vehicles and airport vehicles operate under particular conditions that are not taken into account by conventional driving cycles. Thus, experimental data were acquired from two pickup trucks representing both vehicle fleets that were equipped with a data logger. Based on experimental data, the suburban road-work vehicle showed a mixed driving behavior of high and low speed with occasional long periods of idling. In the airport environment, however, the driving conditions were restricted to airport grounds but were characterized by many accelerations and few high speeds. Based on these measurements, microtrips were defined and two driving cycles proposed. Fuel consumption and pollutant emissions were then measured for both cycles and compared to the FTP-75 and HWFCT cycles, which revealed a major difference: at least a 31% increase in fuel consumption over FTP-75. This increased fuel consumption translates into higher pollutant emissions. When CO₂ equivalent emissions are taken into account, the proposed cycles show an increase of at least 31% over FTP-75 and illustrate the importance of quantifying fleet speed patterns to assess CO₂ equivalent emissions so that the fleet manager can determine potential gains in energy or increased pollutant emissions.     

A comparative life-cycle energy and emissions analysis for intercity passenger transportation in the U.S. by aviation,intercity bus,and automobile

Intercity passenger trips constitute a significant source of energy consumption, greenhouse gas emissions, and criteria pollutant emissions. The most commonly used city-to-city modes in the United States include aircraft, intercity bus, and automobile. This study applies state-of-the-practice models to assess life-cycle fuel consumption and pollutant emissions for intercity trips via aircraft, intercity bus, and automobile. The analyses compare the fuel and emissions impacts of different travel mode scenarios for intercity trips ranging from 200 to 1600 km. Because these modes operate differently with respect to engine technology, fuel type, and vehicle capacity, the modeling techniques and modeling boundaries vary significantly across modes. For aviation systems, much of the energy and emissions are associated with auxiliary equipment activities, infrastructure power supply, and terminal activities, in addition to the vehicle operations between origin/destination. Furthermore, one should not ignore the embodied energy and initial emissions from the manufacturing of the vehicles, and the construction of airports, bus stations, highways and parking lots. Passenger loading factors and travel distances also significantly influence fuel and emissions results on a per-traveler basis. The results show intercity bus is generally the most fuel-efficient mode and produced the lowest per-passenger-trip emissions for the entire range of trip distances examined. Aviation is not a fuel-efficient mode for short trips (<500 km), primarily due to the large energy impacts associated with takeoff and landing, and to some extent from the emissions of ground support equipment associated with any trip distance. However, aviation is more energy efficient and produces less emissions per-passenger-trip than low-occupancy automobiles for trip distances longer than 700–800 km. This study will help inform policy makers and transportation system operators about how differently each intercity system perform across all activities, and provides a basis for future policies designed to encourage mode shifts by range of service. The estimation procedures used in this study can serve as a reference for future analyses of transportation scenarios.     

Comparative evaluation of eight legislated driving schedules in terms of cycle metrics and emissions from a diesel-powered turbocharged van

The present work compares, on a fundamental basis, the performance and emissions of a diesel-engined large van running on eight legislated driving cycles, namely the European NEDC, the U.S. FTP-75, HFET, US06, LA-92 and NYCC, the Japanese JC08 and the Worldwide WLTC 3-2. It aims to identify differences and similarities between various influential driving cycles valid in the world, and correlate important cycle metrics with vehicle exhaust emissions. The results derive from a computational code based on an engine mapping approach, with experimentally derived correction coefficients applied to account for transient discrepancies; the code is coupled to a comprehensive vehicle model. Soot as well as nitrogen monoxide are the examined pollutants. Only the driving cycle schedule is under investigation in this work, and not the whole test procedure, in order to identify vehicle speed (transient) effects of the individual cycles only. The recently developed WLTC 3-2 is the cycle with a very broad and at the same time dense coverage of the vehicle’s/engine’s operating activity, being thus particularly representative of ‘average’ real-world driving. Even broader is the distribution of the US06, whereas particularly thin and narrow that of the modal NEDC. It is also revealed that the more transient cycles, e.g. the NYCC or the US06, are also the ones with the highest amount of engine-out pollutant emissions and energy consumption. Relative positive acceleration and stops per km are found to correlate very well with energy and fuel consumption and all emitted pollutants.     

Development a new power management strategy for power split hybrid electric vehicles

Reduction of greenhouse gas emission and fuel consumption as one of the main goals of automotive industry leading to the development hybrid vehicles. The objective of this paper is to investigate the energy management system and control strategies effect on fuel consumption, air pollution and performance of hybrid vehicles in various driving cycles. In order to simulate the hybrid vehicle, the combined feedback–feedforward architecture of the power-split hybrid electric vehicle based on Toyota Prius configuration is modeled, together with necessary dynamic features of subsystem or components in ADVISOR. Multi input fuzzy logic controller developed for energy management controller to improve the fuel economy of a power-split hybrid electric vehicle with contrast to conventional Toyota Prius Hybrid rule-based controller. Then, effects of battery’s initial state of charge, driving cycles and road grade investigated on hybrid vehicle performance to evaluate fuel consumption and pollution emissions. The simulation results represent the effectiveness and applicability of the proposed control strategy. Also, results indicate that proposed controller is reduced fuel consumption in real and modal driving cycles about 21% and 6% respectively.     

A field evaluation case study of the environmental and energy impacts of traffic calming

This study quantifies the energy and environmental impact of a selection of traffic calming measures using a combination of second-by-second floating-car global positioning system data and microscopic energy and emission models. It finds that traffic calming may result in negative impacts on vehicle fuel consumption and emission rates if drivers exert aggressive acceleration levels to speed up to their journeys. Consequently by eliminating sharp acceleration maneuvers significant savings in vehicle fuel consumption and emission rates are achievable through driver education. The study also demonstrates that high emitting vehicles produce CO emissions that are up to 25 times higher than normal vehicle emission levels while low emitting vehicles produce emissions that are 15–35% of normal vehicles. The relative increases in vehicle fuel consumption and emission levels associated with the sample traffic calming measures are consistent and similar for normal, low, and high emitting vehicles.     

Hardware-in-the-loop testbed for evaluating connected vehicle applications

Connected vehicle environment provides the groundwork of future road transportation. Researches in this area are gaining a lot of attention to improve not only traffic mobility and safety, but also vehicles’ fuel consumption and emissions. Energy optimization methods that combine traffic information are proposed, but actual testing in the field proves to be rather challenging largely due to safety and technical issues. In light of this, a Hardware-in-the-Loop-System (HiLS) testbed to evaluate the performance of connected vehicle applications is proposed. A laboratory powertrain research platform, which consists of a real engine, an engine-loading device (hydrostatic dynamometer) and a virtual powertrain model to represent a vehicle, is connected remotely to a microscopic traffic simulator (VISSIM). Vehicle dynamics and road conditions of a target vehicle in the VISSIM simulation are transmitted to the powertrain research platform through the internet, where the power demand can then be calculated. The engine then operates through an engine optimization procedure to minimize fuel consumption, while the dynamometer tracks the desired engine load based on the target vehicle information. Test results show fast data transfer at every 200 ms and good tracking of the optimized engine operating points and the desired vehicle speed. Actual fuel and emissions measurements, which otherwise could not be calculated precisely by fuel and emission maps in simulations, are achieved by the testbed. In addition, VISSIM simulation can be implemented remotely while connected to the powertrain research platform through the internet, allowing easy access to the laboratory setup.     

Help or hindrance? The travel,energy and carbon impacts of highly automated vehicles

Experts predict that new automobiles will be capable of driving themselves under limited conditions within 5–10 years, and under most conditions within 10–20 years. Automation may affect road vehicle energy consumption and greenhouse gas (GHG) emissions in a host of ways, positive and negative, by causing changes in travel demand, vehicle design, vehicle operating profiles, and choices of fuels. In this paper, we identify specific mechanisms through which automation may affect travel and energy demand and resulting GHG emissions and bring them together using a coherent energy decomposition framework. We review the literature for estimates of the energy impacts of each mechanism and, where the literature is lacking, develop our own estimates using engineering and economic analysis. We consider how widely applicable each mechanism is, and quantify the potential impact of each mechanism on a common basis: the percentage change it is expected to cause in total GHG emissions from light-duty or heavy-duty vehicles in the U.S. Our primary focus is travel related energy consumption and emissions, since potential lifecycle impacts are generally smaller in magnitude. We explore the net effects of automation on emissions through several illustrative scenarios, finding that automation might plausibly reduce road transport GHG emissions and energy use by nearly half – or nearly double them – depending on which effects come to dominate. We also find that many potential energy-reduction benefits may be realized through partial automation, while the major energy/emission downside risks appear more likely at full automation. We close by presenting some implications for policymakers and identifying priority areas for further research.     

I can’t believe your attitude: a joint estimation of best worst attitudes and electric vehicle choice

The number of conventionally fuelled motor vehicles in use is increasing worldwide despite warnings about finite fossil fuel and the detrimental impacts of burning such fuels. While electric vehicles, the subject of much research, generate far less emissions and offer the potential for power from renewable sources, they are yet to significantly penetrate the market. Tangible barriers such as price and vehicle range still exist, but consumer attitudes also drive behaviour. This paper examines attributes in a framework relatively new to transportation and energy policy; best–worst scaling. This method is widely considered an improvement over traditional methods of eliciting attitudes and beliefs, where respondents select attitudes they find best or worst from a set of attitudinal statements. To avoid potential endogeneity bias, we jointly model attitudes and choice for the first time with best–worst data. It is found that energy crisis, air quality and climate change concerns influence behaviour with respect to vehicle range and that travel behaviour change and forms of government incentives are needed influences on behaviour with respect to vehicle emissions. It is argued that correctly modelling attitudes reduces the error term of the vehicle choice model and provides policy makers with an improved lens for assessing behaviour. Additionally, the methods described within can easily be adapted to other policy scenarios.     

An econometric model of vehicle use in the household sector

Vehicle-use modelling at the household level has taken on new importance with the pressures on governments to encourage more efficient utilisation of increasingly scarce nonreplenishible liquid fuels. The fundamental energy equation recognizes two direct influences on consumption—the fuel efficiency of the vehicle and the amount of use. Until recently, the interrelationship between vehicle choice and vehicle utilisation at the household level was acknowledged but ignored. The availability of reliable vehicle-use data at the household level now enables a more serious effort at amending the imbalance of research effort where the reliance has been predominantly on vehicle choice modelling and gross (exogenous) assumptions on utilisation as a basis for predicting fuel consumption. This paper proposes an econometric method for identifying the influences on household vehicle use. It differs from previous empirical work in that vehicle kilometers, fuel cost per kilometer and vehicle fuel efficiency are endogenous, with utilisation of each vehicle endogeneously dependent on the utilisation of each and every household vehicle. The data are drawn from wave 1 of a four-wave panel of 1436 households in the Sydney metropolitan area. The empirical findings expose a set of influences on use hitherto not considered. The model specification provides an appropriate module for integration with household-based discrete choice models of vehicle choice.     

Impacts of replacement of engine powered vehicles by electric vehicles on energy consumption and CO2 emissions

This paper evaluates the impacts on energy consumption and carbon dioxide (CO₂) emissions from the introduction of electric vehicles into a smart grid, as a case study. The AVL Cruise software was used to simulate two vehicles, one electric and the other engine-powered, both operating under the New European Driving Cycle (NEDC), in order to calculate carbon dioxide (CO₂) emissions, fuel consumption and energy efficiency. Available carbon dioxide data from electric power generation in Brazil were used for comparison with the simulated results. In addition, scenarios of gradual introduction of electric vehicles in a taxi fleet operating with a smart grid system in Sete Lagoas city, MG, Brazil, were made to evaluate their impacts. The results demonstrate that CO₂ emissions from the electric vehicle fleet can be from 10 to 26 times lower than that of the engine-powered vehicle fleet. In addition, the scenarios indicate that even with high factors of CO₂ emissions from energy generation, significant reductions of annual emissions are obtained with the introduction of electric vehicles in the fleet.