Results for “fuel consumption estimation”

DOE OSTI2017

Data-driven fuel consumption estimation: A multivariate adaptive regression spline approach

Providing guidance and information to drivers to help them make fuel-efficient route choices remains an important and effective strategy in the near term to reduce fuel consumption from the transportation sector. One key component in implementing this strategy is a fuel-consumption estimation model. In this paper, we developed a mesoscopic fuel consumption estimation model that can be implemented into an eco-routing system. Our proposed model presents a framework that utilizes large-scale, real-world driving data, clusters road links by free-flow speed and fits one statistical model for each of cluster. This model includes predicting variables that were rarely or never considered before, such as free-flow speed and number of lanes. We applied the model to a real-world driving data set based on a global positioning system travel survey in the Philadelphia-Camden-Trenton metropolitan area. Results from the statistical analyses indicate that the independent variables we chose influence the fuel consumption rates of vehicles. But the magnitude and direction of the influences are dependent on the type of road links, specifically free-flow speeds of links. Here, a statistical diagnostic is conducted to ensure the validity of the models and results. Although the real-world driving data we used to develop statistical relationships are specific to one region, the framework we developed can be easily adjusted and used to explore the fuel consumption relationship in other regions.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2015

Estimate of Fuel Consumption and GHG Emission Impact on an Automated Mobility District: Preprint

This study estimates the range of fuel and emissions impact of an automated-vehicle (AV) based transit system that services campus-based developments, termed an automated mobility district (AMD). The study develops a framework to quantify the fuel consumption and greenhouse gas (GHG) emission impacts of a transit system comprised of AVs, taking into consideration average vehicle fleet composition, fuel consumption/GHG emission of vehicles within specific speed bins, and the average occupancy of passenger vehicles and transit vehicles. The framework is exercised using a previous mobility analysis of a personal rapid transit (PRT) system, a system which shares many attributes with envisioned AV-based transit systems. Total fuel consumption and GHG emissions with and without an AMD are estimated, providing a range of potential system impacts on sustainability. The results of a previous case study based of a proposed implementation of PRT on the Kansas State University (KSU) campus in Manhattan, Kansas, serves as the basis to estimate personal miles traveled supplanted by an AMD at varying levels of service. The results show that an AMD has the potential to reduce total system fuel consumption and GHG emissions, but the amount is largely dependent on operating and ridership assumptions. The study points to the need to better understand ride-sharing scenarios and calls for future research on sustainability benefits of an AMD system at both vehicle and system levels.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2026

Diesel Fuel Consumption in Prominent U.S. Open-Pit Mines: Site-Level Estimates

This report presents a comprehensive framework for estimating diesel fuel consumption and prices at open-pit mines in the United States. The framework includes transparent methods for calculating site-level diesel energy use when direct reporting is unavailable, and a structured confidence evaluation for each method. The framework is demonstrated to estimate current diesel consumption at 21 open-pit mines in the United States. Initial findings support ongoing efforts to strengthen the competitiveness and security of the U.S. industrial base by supporting data-driven supply chain analysis and decision-making, improved transparency in mining sector energy use, and targeted deployment of energy innovation and cost-reduction strategies. Future updates to the dataset—coupled with expanded data transparency and method validation—will help ensure that the findings remain relevant as the sector continues to evolve.

02 PETROLEUM↗

DOE OSTI2022

Point Cloud Based Mapping of Understory Shrub Fuel Distribution, Estimation of Fuel Consumption and Relationship to Pyrolysis Gas Emissions on Experimental Prescribed Burns

Forest fires spread via production and combustion of pyrolysis gases in the understory. The goal of the present paper is to understand the spatial location, distribution, and fraction (relative to the overstory) of understory plants, in this case, sparkleberry shrub, namely its degree of understory consumption upon burn, and to search for correlations between the degree of shrub consumption to the composition of emitted pyrolysis gases. Data were collected in situ at seven small experimental prescribed burns at Ft. Jackson, an army base in South Carolina, USA. Using airborne laser scanning (ALS) to map overstory tree crowns and terrestrial laser scanning (TLS) to characterize understory shrub fuel density, both pre- and postburn estimates of sparkleberry coverage were obtained. Sparkleberry clump polygons were manually digitized from a UAV-derived orthoimage of the understory and intersected with the TLS point cloud-derived rasters of pre- and postburn shrub fuel bulk density; these were compared in relation to overstory crown cover as well as to ground truth. Shrub fuel consumption was estimated from the digitized images; sparkleberry clump distributions were generally found to not correlate well to the overstory tree crowns, suggesting it is shade-tolerant. Moreover, no relationship was found between the magnitude of the fuel consumption and the chemical composition of pyrolysis gases, even though mixing ratios of 25 individual gases were measured.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Validation and Calibration of Energy Models with Real Vehicle Data from Chassis Dynamometer Experiments

Accurate estimation of vehicle fuel consumption typically requires detailed modeling of complex internal powertrain dynamics, often resulting in computationally intensive simulations. However, many transportation applications-such as traffic flow modeling, optimization, and control-require simplified models that are fast, interpretable, and easy to implement, while still maintaining fidelity to physical energy behavior. This work builds upon a recently developed model reduction pipeline that derives physics-like energy models from high-fidelity Autonomie vehicle simulations. These reduced models preserve essential vehicle dynamics, enabling realistic fuel consumption estimation with minimal computational overhead. While the reduced models have demonstrated strong agreement with their Autonomie counterparts, previous validation efforts have been confined to simulation environments. This study extends the validation by comparing the reduced energy model's outputs against real-world vehicle data. Focusing on the MidSUV category, we tune the baseline Autonomie model to closely replicate the characteristics of a Toyota RAV4. We then assess the accuracy of the resulting reduced model in estimating fuel consumption under actual drive conditions. Our findings suggest that, when the reference Autonomie model is properly calibrated, the simplified model produced by the reduction pipeline can provide reliable, semi-principled fuel rate estimates suitable for large-scale transportation applications.

42 ENGINEERING↗

DOE OSTI2017

Green Routing Fuel Saving Opportunity Assessment: A Case Study on California Large-Scale Real-World Travel Data

New technologies, such as connected and automated vehicles, have attracted more and more researchers for improving the energy efficiency and environmental impact of current transportation systems. The green routing strategy instructs a vehicle to select the most fuel-efficient route before the vehicle departs. It benefits the current transportation system with fuel saving opportunity through identifying the greenest route. This paper introduces an evaluation framework for estimating benefits of green routing based on large-scale, real-world travel data. The framework has the capability to quantify fuel savings by estimating the fuel consumption of actual routes and comparing to routes procured by navigation systems. A route-based fuel consumption estimation model, considering road traffic conditions, functional class, and road grade is proposed and used in the framework. An experiment using a large-scale data set from the California Household Travel Survey global positioning system trajectory data base indicates that 31% of actual routes have fuel savings potential with a cumulative estimated fuel savings of 12%.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2017

Green Routing Fuel Saving Opportunity Assessment: A Case Study on California Large-Scale Real-World Travel Data: Preprint

New technologies, such as connected and automated vehicles, have attracted more and more researchers for improving the energy efficiency and environmental impact of current transportation systems. The green routing strategy instructs a vehicle to select the most fuel-efficient route before the vehicle departs. It benefits the current transportation system with fuel saving opportunity through identifying the greenest route. This paper introduces an evaluation framework for estimating benefits of green routing based on large-scale, real-world travel data. The framework has the capability to quantify fuel savings by estimating the fuel consumption of actual routes and comparing to routes procured by navigation systems. A route-based fuel consumption estimation model, considering road traffic conditions, functional class, and road grade is proposed and used in the framework. An experiment using a large-scale data set from the California Household Travel Survey global positioning system trajectory data base indicates that 31% of actual routes have fuel savings potential with a cumulative estimated fuel savings of 12%.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2018

Fuel consumption for various driving styles in conventional and hybrid electric vehicles: Integrating driving cycle predictions with fuel consumption optimization

Here, improving fuel economy and lowering emissions are key societal goals. Standard driving cycles, pre-designed by the US Environmental Protection Agency (EPA), have long been used to estimate vehicle fuel economy in laboratory-controlled conditions. They have also been used to test and tune different energy management strategies for hybrid electric vehicles (HEVs). This paper aims to estimate fuel consumption for a conventional vehicle and a HEV using personalized driving cycles extracted from real-world data to study the effects of different driving styles and vehicle types on fuel consumption when compared to the estimates based on standard driving cycles. To do this, we extracted driving cycles for conventional vehicles and HEVs from a large-scale U.S. survey that contains real-world GPS-based driving records. Next, the driving cycles were assigned to one of three categories: volatile, normal, or calm. Then, the driving cycles were used along with a driver-vehicle simulation that captures driver decisions (vehicle speed during a trip), powertrain, and vehicle dynamics to estimate fuel consumption for conventional vehicles and HEVs with power-split powertrain. To further optimize fuel consumption for HEVs, the Equivalent Consumption Minimization Strategy (ECMS) is applied. The results show that depending on the driving style and the driving scenario, conventional vehicle fuel consumption can vary widely compared with standard EPA driving cycles. Specifically, conventional vehicle fuel consumption was 13% lower in calm urban driving, but almost 34% higher for volatile highway driving compared with standard EPA driving cycles. Interestingly, when a driving cycle is predicted based on the application of case-based reasoning and used to tune the power distribution in a hybrid electric vehicle, its fuel consumption can be reduced by up to 12% in urban driving. Implications and limitations of the findings are discussed.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2017

Comparison of Vehicle-Broadcasted Fuel Consumption Rates against Precise Fuel Measurements for Medium- and Heavy-Duty Vehicles and Engines

Vehicles continuously report real-time fuel consumption estimates over their data bus, known as the controller area network (CAN). However, the accuracy of these fueling estimates is uncertain to researchers who collect these data from any given vehicle. To assess the accuracy of these estimates, CAN-reported fuel consumption data are compared against fuel measurements from precise instrumentation. The data analyzed consisted of eight medium/heavy-duty vehicles and two medium-duty engines. Varying discrepancies between CAN fueling rates and the more accurate measurements emerged but without a vehicular trend-for some vehicles the CAN under-reported fuel consumption and for others the CAN over-reported fuel consumption. Furthermore, a qualitative real-time analysis revealed that the operating conditions under which these fueling discrepancies arose varied among vehicles. A drive cycle analysis revealed that while CAN fueling estimate accuracy differs for individual vehicles, that CAN estimates capture the relative fuel consumption differences between drive cycles within 4% for all vehicles and even more accurately for some vehicles. Furthermore, in situations where only CAN-reported data are available, CAN fueling estimates can provide relative fuel consumption trends but not accurate or precise fuel consumption rates.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2021

Hybrid electric buses fuel consumption prediction based on real-world driving data

Estimating fuel consumption by hybrid diesel buses is challenging due to its diversified operations and driving cycles. Here, long-term transit bus monitoring data were utilized to empirically compare fuel consumption of diesel and hybrid buses under various driving conditions. Artificial neural network (ANN) based high-fidelity microscopic (1 Hz) and mesoscopic (5–60 min) fuel consumption models were developed for hybrid buses. The microscopic model contained 1 Hz driving, grade, and environment variables. The mesoscopic model aggregated 1 Hz data into 5 to 60-minute traffic pattern factors and predicted average fuel consumption over its duration. The prediction results show mean absolute percentage errors of 1–2% for microscopic models and 5–8% for mesoscopic models. The data were partitioned by different driving speeds, vehicle engine demand, and road grade to investigate their impacts on prediction performance.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2019

A Simple Hybrid Electric Vehicle Fuel Consumption Model for Transportation Applications(DOI: 10.5772/intechopen.89055)

This study presents a simple power-based microscopic hybrid electric vehicle (HEV) fuel consumption model for use in microscopic traffic software and various connected and automated vehicle (CAV) applications, including eco-routing and eco-drive systems. While numerous HEV energy consumption models have been developed, these models are complex and require vehicle engine data deeming them difficult to implement and making them nontransferable. The proposed model was developed using empirical data for a 2010 Toyota Prius—the most popular HEV. The model was then extended to other HEVs thus extending the domain of application of the model. The proposed fuel consumption model estimates the instantaneous fuel consumption rates of an HEV using instantaneous vehicle operational input variables, including the vehicle’s speed, acceleration, and roadway grade, which can be acquired from global positioning system (GPS) equipment or other sensors. The model estimates vehicle fuel consumption rates consistent with empirical data producing an average error of 2.1% for the Toyota Prius and up to 4% for other HEVs demonstrating the applicability and transferability of the model to various HEVs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2012

Annual Fossil-Fuel CO2 Emissions: Isomass of Emissions Gridded by One Degree Latitude by One Degree Longitude (1751 - 2008) (V. 2011)

The basic data provided in these data files are derived from time series of Global, Regional, and National Fossil-Fuel CO2 Emissions (http://cdiac.ess-dive.lbl.gov/trends/emis/overview_2008.html) and references therein. The data accessible here take these tabular, national, mass-emissions data, multiply them by stable carbon isotopic signature (del 13C) as described in Andres et al. (2000), and distribute them spatially on a one degree latitude by one degree longitude grid. The within-country spatial distribution is achieved through a fixed population distribution as reported in Andres et al. (1996). Note that the mass-emissions data used here are based on fossil-fuel consumption estimates as these are more representative of within country emissions than fossil-fuel production estimates (see http://cdiac.ess-dive.lbl.gov/faq.html#Q10 for a description why emission totals based upon consumption differ from those based upon production).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

Monthly Fossil-Fuel CO2 Emissions: Mass of Emissions Gridded by One Degree Latitude by One Degree Longitude - 2015

The monthly, fossil-fuel CO2 emissions estimates from 1950-2011 provided in this database are derived from time series of global, regional, and national fossil-fuel CO2 emissions (Boden et al. 2015), the references therein, and the methodology described in Andres et al. (2011). The data accessible here take these tabular, national, mass-emissions data and distribute them spatially on a one degree latitude by one degree longitude grid. The within-country spatial distribution is achieved through a fixed population distribution as reported in Andres et al. (1996). Note that the mass-emissions data used here are based on fossil-fuel consumption estimates as these are more representative of within country emissions than fossil-fuel production estimates (see http://cdiac.ess-dive.lbl.gov/faq.html#Q10 for a description why emission totals based upon consumption differ from those based upon production).

Andres, R.J.↗

DOE OSTI2012

Annual Fossil-Fuel CO2 Emissions: Isomass of Emissions Gridded by One Degree Latitude by One Degree Longitude (1751 - 2009) (V. 2012)

The annual, isotopic (δ 13C) fossil-fuel CO2 emissions estimates from 1751-2009 provided in this database are derived from time series of global, regional, and national fossil-fuel CO2 emissions (Boden et al. 2012) and references therein. The data accessible here take these tabular, national, mass-emissions data, multiply them by stable carbon isotopic signatures (δ 13C) as described in Andres et al. (2000), and distribute them spatially on a one degree latitude by one degree longitude grid. The within-country spatial distribution is achieved through a fixed population distribution as reported in Andres et al. (1996). Note that the mass-emissions data used here are based on fossil-fuel consumption estimates as these are more representative of within country emissions than fossil-fuel production estimates (see http://cdiac.ess-dive.lbl.gov/faq.html#Q10 for a description why emission totals based upon consumption differ from those based upon production).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2013

Annual Fossil-Fuel CO2 Emissions: Isomass of Emissions Gridded by One Degree latitude by One Degree Longitude (V. 2013)

The monthly, isotopic (δ 13C) fossil-fuel CO2 emissions estimates from 1950-2013 provided in this database are derived from time series of global, regional, and national fossil-fuel CO2 emissions (Boden et al. 2016), the references therein, and the methodology described in Andres et al. (2011). The data accessible here take these tabular, national, mass-emissions data, multiply them by stable carbon isotopic signatures (δ 13C) as described in Andres et al. (2000), and distribute them spatially on a one degree latitude by one degree longitude grid. The within-country spatial distribution is achieved through a fixed population distribution as reported in Andres et al. (1996). Note that the mass-emissions data used here are based on fossil-fuel consumption estimates as these are more representative of within country emissions than fossil-fuel production estimates (see http://cdiac.ess-dive.lbl.gov/faq.html#Q10 for a description why emission totals based upon consumption differ from those based upon production).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

Annual Fossil-Fuel CO2 Emissions: Isomass of Emissions Gridded by One Degree Latitude by One Degree Longitude (V. 2015)

The monthly, isotopic (δ 13C) fossil-fuel CO2 emissions estimates from 1950-2013 provided in this database are derived from time series of global, regional, and national fossil-fuel CO2 emissions (Boden et al. 2016), the references therein, and the methodology described in Andres et al. (2011). The data accessible here take these tabular, national, mass-emissions data, multiply them by stable carbon isotopic signatures (δ 13C) as described in Andres et al. (2000), and distribute them spatially on a one degree latitude by one degree longitude grid. The within-country spatial distribution is achieved through a fixed population distribution as reported in Andres et al. (1996). Note that the mass-emissions data used here are based on fossil-fuel consumption estimates as these are more representative of within country emissions than fossil-fuel production estimates (see http://cdiac.ess-dive.lbl.gov/faq.html#Q10 for a description why emission totals based upon consumption differ from those based upon production).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2012

Monthly Fossil-Fuel CO2 Emissions: Isomass of Emissions Gridded by One Degree Latitude by One Degree Longitude (1950 - 2009) (V. 2012)

The monthly, isotopic (δ 13C) fossil-fuel CO2 emissions estimates from 1950-2013 provided in this database are derived from time series of global, regional, and national fossil-fuel CO2 emissions (Boden et al. 2016), the references therein, and the methodology described in Andres et al. (2011). The data accessible here take these tabular, national, mass-emissions data, multiply them by stable carbon isotopic signatures (δ 13C) as described in Andres et al. (2000), and distribute them spatially on a one degree latitude by one degree longitude grid. The within-country spatial distribution is achieved through a fixed population distribution as reported in Andres et al. (1996). Note that the mass-emissions data used here are based on fossil-fuel consumption estimates as these are more representative of within country emissions than fossil-fuel production estimates (see http://cdiac.ess-dive.lbl.gov/faq.html#Q10 for a description why emission totals based upon consumption differ from those based upon production).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2013

Monthly Fossil-Fuel CO2 Emissions: Isomass of Emissions Gridded by One Degree Latitude by One Degree Longitude (1950 - 2010) (V. 2013)

The monthly, isotopic (δ 13C) fossil-fuel CO2 emissions estimates from 1950-2013 provided in this database are derived from time series of global, regional, and national fossil-fuel CO2 emissions (Boden et al. 2016), the references therein, and the methodology described in Andres et al. (2011). The data accessible here take these tabular, national, mass-emissions data, multiply them by stable carbon isotopic signatures (δ 13C) as described in Andres et al. (2000), and distribute them spatially on a one degree latitude by one degree longitude grid. The within-country spatial distribution is achieved through a fixed population distribution as reported in Andres et al. (1996). Note that the mass-emissions data used here are based on fossil-fuel consumption estimates as these are more representative of within country emissions than fossil-fuel production estimates (see http://cdiac.ess-dive.lbl.gov/faq.html#Q10 for a description why emission totals based upon consumption differ from those based upon production).

54 ENVIRONMENTAL SCIENCES↗