No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Fuel consumption model for heavy duty diesel trucks: Model development and testing
Abstract
A simple, efficient, and realistic fuel consumption model is essential to support the development of effective eco-freight strategies, including eco-routing and eco-driving systems. The majority of the existing heavy duty truck (HDT) fuel consumption models, however, would recommend that drivers accelerate at full throttle or brake at full braking to minimize their fuel consumption levels, which is obviously not realistic. To overcome this shortcoming, the paper applies the Virginia Tech Comprehensive Power-based Fuel consumption Model (VT-CPFM) framework to develop a new model that is calibrated and validated using field data collected using a mobile emissions research laboratory (MERL). The results demonstrate that the model accurately predicts fuel consumption levels consistent with field observations and outperforms the comprehensive modal emissions model (CMEM) and the motor vehicle emissions simulator (MOVES) model. Using the model it is demonstrated that the optimum fuel economy cruise speed ranges between 32 and 52 km/h with steeper roads and heavier trucks resulting in lower optimum cruise speeds. The results also demonstrate that the model generates accurate emission estimates that are consistent with field measurements. Finally, the model can be easily calibrated using data collected using non-engine instrumentation (e.g. Global Positioning System) and readily implemented in traffic simulation software, smartphone applications and eco-freight programs.
... Moreover, physics-based approaches can be accurate, but they often lack computing efficiency [8,9]. Additionally, they require knowledge of various vehicle dynamics, powertrain parameters, and multidimensional maps, which are usually unavailable. ...
... , where Q 1 and Q 3 are the sample 25 th and 75 th percentiles. VM 9 Quantile coefficient of variation of longitudinal acceleration 100 × changes. They often choose to accelerate rather than slow down or stop, favoring speed over safety. ...
This study is a significant endeavor involving the development and testing of a comprehensive methodology to incorporate driving behavior into the analysis and prediction of vehicle fuel consumption. It underscores the crucial importance of understanding how different driving behavior affect fuel efficiency. The framework we present is a theoretical construct and a practical tool. It provides a robust, multi-step process for linking driving behavior to fuel consumption, leveraging both traditional statistical methods and advanced machine learning techniques to derive actionable insights. To test the framework, we used a naturalistic data that includes about 5408 different road users in a mixed traffic environment and urban settings in Germany. We applied a microscopic fuel consumption model to calibrate the framework and an unsupervised clustering algorithm to classify the behavior of the driver interacting with each other and with vulnerable road users. The framework includes developing Linear regression model as a baseline, which yields an R-squared of 0.511 and a Mean Squared Error (MSE) of 0.031, indicating moderate predictive accuracy. The final step includes choosing Random Forest as a better model, which achieves a higher R-squared of 0.956 and a lower MSE of 0.003. We also found that conservative and aggressive driving leads to significantly higher and more discrepancy in fuel consumption than normal driving behavior. These insights can promote more efficient driving practices, leading to significant fuel savings and environmental benefits.
... For the vehicle's power source, due to the characteristics of the engine, the engine energy consumption model is generally ftted by empirical data, such as the transient polynomial fuel consumption model (TPFM) [34]. In the literature [35], empirical formulas for fuel consumption modeling of heavy-duty vehicles are established by analyzing experimental data. Rolling resistance coefcient [36] and air resistance coefcient [25,37] will also afect the fuel consumption of platoons. ...
... . Te fuel consumption model of a vehicle is infuenced by various factors, such as engine type, air resistance, road class, body mass, and tire size [35]. Te complexity of the engine fuel consumption model is further compounded by the impact of transmission gear ratios and clutches on fuel consumption. ...
Connected and automated vehicle platoons (CAVPs) are considered an effective way to alleviate traffic congestion, reduce the incidence of traffic accidents, and improve vehicle economy in the intelligent transportation system (ITS). Vehicles in the CAVPs can communicate with each other through V2X technology, which could optimize the economy of the platoon. Cooperative adaptive cruise control (CACC) can make effective use of the characteristics of CAVPs and contribute to resource conservation, ecological driving, and traffic system development. In this paper, a two-stage CACC method is proposed for CAVPs to reduce fuel consumption in the multislope road section. In the first stage, the optimal velocity profiles for the leader based on dynamic programming (DP) are planned according to the road information and the fuel consumption model. In the second stage, a vehicle longitudinal third-order differential dynamics model is utilized to build the platoon time-delay system considering communication delay and actuator delay. A feedback controller is developed for each vehicle considering the internal stability and the string stability of the CAVPs. Results show that the proposed method can save 5.33% of fuel consumption compared to the constant speed cooperative adaptive cruise control (CS-CACC) method and has a better tracking performance compared to the model predictive control (MPC) method. The CACC method proposed in this paper can provide a theoretical basis and data support for building an ecological CACC strategy for CAVPs.
... Our assumptions on moisture, gross calorific value, weight, and truck volume capacity to calculate average transportation costs for different biomass products are described in Table 2. [36]. Source: [36][37][38][39][40][41]. ...
... Because transportation companies typically charge for both the outbound and inbound transportation of biomass, we double the distance between regions i and j (D ij ). Based on a review of the offers by transportation companies, we obtain the average transportation cost per kilometre (C 0 ) that is increasing over time t due to pricing fuel within the EU ETS2 (column 3, Table 3) and k = 1.08 kg CO 2 /km (we assume large transport trucks consume roughly 0.4 L of diesel per kilometre [40] and each litre of diesel produces 2.7 kg of CO 2 [41], giving k = 1.08 [kg CO 2 /km]). Dividing the total transportation cost between two regions i and j by the embodied energy E b of transported biomass product b, we obtain transportation costs TC btij , which are expressed in €/GJ between regions i and j by biomass products b: ...
Forests are a potentially carbon-negative energy source and function as carbon sinks. However, both of these functions have become threatened significantly by spruce bark beetle infestation in Czechia. This paper assesses how this ecological issue may affect the future energy mix, and in the process, affect carbon emission reduction targets and the available share of renewable energy sources (RESs). We assess several forest development scenarios with three policy incentives: subsidising wood pellet production, striving for climate neutrality, and adhering to ecological constraints. We build a spatially enriched energy system model, TIMES-CZ, based on Eurostat’s NUTS3 regions. We find that the spruce bark beetle infestation may not exert a lasting influence on overall decarbonisation pathways, the energy mix, or system-wide costs in any forest scenario. The RESs share is affected only until 2030, and the effect is minimal, at <1.5 percentage points. Nevertheless, Czechia’s RES contribution is far below the 45% 2030 EU target. Subsidising wood pellet production is a costly transition that does not contribute to meeting the target. Limiting forest biomass availability and adhering to ecological constraints increase the overall system costs and worsen the chances of meeting decarbonisation targets.
... Moreover, the model can be applied on publicly available data, which makes it feasible to be used in any geographical location. The model was also tested for different vehicle types including light-and heavy-duty vehicles (Rakha et al., 2011;Wang and Rakha, 2017), and buses (Edwardes and Rakha, 2014). More details on the model and how it can be implemented can be found in (Rakha et al., 2011). ...
... The speed profiles in Fig. 1(b) and 2(d) shows that flashing green signal indication has created a relatively more optimal deceleration pattern with a gradual decrease in the speed than that at Fig. 1(a) and 2(c). This behavior cost less fuel than making more abrupt braking events as in the case of control condition (Rakha et al., 2011;Wang and Rakha, 2017;Edwardes and Rakha, 2014;Muñoz-Organero and Magaña, 2013). On average, T1 produced lower fuel consumption than T0. ...
... Additionally, because the function for fuel consumption is a seconddegree polynomial with respect to vehicle specific power (VSP), the partial derivative with respect to torque is a function of torque and the bang-bang control is not produced [20]. The model also can be used for different vehicle classes including light-duty vehicles [20], heavyduty vehicles [21], and buses [22]. ...
... VT-CPFM function for fuel consumption is a second-degree polynomial with respect to vehicle specific power (VSP) using Equation (6) [20]. We used VT-CPFM in this study for the different vehicle classes including light-duty vehicles [20], heavy-duty vehicles [21], and buses [22] that passed through the study area. We found that the total fuel consumption resulted from all the 750 vehicles passing through the approach is about 207L during the 14-min study period; about 0.28L per vehicle; or 0.25 L/s. ...
This paper presents a novel method to compute various measures of effectiveness (MOEs) at a signalized intersection using vehicle trajectory data collected by flying drones. Specifically, this study investigates the use of drone raw data at a busy three-way signalized intersection in Athens, Greece, and builds on the open data initiative of the pNEUMA experiment. Using a microscopic approach and shockwave analysis on data extracted from real-time videos, we estimated the maximum queue length, whether, when, and where a spillback occurred, vehicle stops, vehicle travel time and delay, crash rates, and fuel consumption. The results of the various MOEs were found to be promising. We also demonstrated that estimating MOEs in real-time is achievable using drone data. Such models can track individual vehicle movements within street networks and thus allow the modeler to consider any traffic conditions, ranging from highly under-saturated to highly over-saturated conditions.
... Additionally, because the function for fuel consumption is a second degree polynomial with respect to vehicle specific power (VSP), the partial derivative with respect to torque is a function of torque and the bang-bang control is not produced [20]. The model also can be used for different vehicle classes including light-duty vehicles [20], heavy-duty vehicles [21], and buses [22]. ...
... VT-CPFM function for fuel consumption is a second degree polynomial with respect to vehicle specific power (VSP) using Equation (6) [20]. We used VT-CPFM in this study for the different vehicle classes including light-duty vehicles [20], heavy-duty vehicles [21], and buses [22] that passed through the study area. We found that the total fuel consumption resulted from all the 750 vehicles passing through the approach is about 207L during the 14-min study period; about 0.28L per vehicle; or 0.25 L/s. ...
Driving behavior is considered as a unique driving habit of each driver and has a significant impact on road safety. This study proposed a novel data-driven Machine Learning framework that can classify driving behavior at signalized intersections considering two different signal conditions. To the best of our knowledge, this is the first study that investigates driving behavior at signalized intersections with two different conditions that are mostly used in practice, i.e., the control setting with the signal order of green-yellow-red and a flashing green setting with the signal order of green-flashing green-yellow-red. A driving simulator dataset collected from participants at Qatar University's Qatar Transportation and Traffic Safety Center, driving through multiple signalized intersections, was used. The proposed framework extracts volatility measures from vehicle kinematic parameters including longitudinal speed and acceleration. K-means clustering algorithm with elbow method was used as an unsupervised machine learning to cluster driving behavior into three classes (i.e., conservative, normal, and aggressive) and investigate the impact of signal conditions. The framework confirmed that in general driving behavior at a signalized intersection reflects drivers' habits and personality rather than the signal condition, still, it manifests the intersection nature that usually requires drivers to be more vigilant and cautious. Nonetheless, the results suggested that flashing green condition could make drivers more conservative, which could be due to the limited capabilities of human to estimate the remaining distance and the prolonged duration of the additional flashing green interval. The proposed framework and findings of the study were promising that can be used for clustering drivers into different styles for different conditions and might be beneficial for policymakers, researchers, and engineers.
... Additionally, because the function for fuel consumption is a second degree polynomial with respect to vehicle specific power (VSP), the partial derivative with respect to torque is a function of torque and the bang-bang control is not produced [20]. The model also can be used for different vehicle classes including light-duty vehicles [20], heavy-duty vehicles [21], and buses [22]. ...
... VT-CPFM function for fuel consumption is a second degree polynomial with respect to vehicle specific power (VSP) using Equation (6) [20]. We used VT-CPFM in this study for the different vehicle classes including light-duty vehicles [20], heavy-duty vehicles [21], and buses [22] that passed through the study area. We found that the total fuel consumption resulted from all the 750 vehicles passing through the approach is about 207L during the 14-min study period; about 0.28L per vehicle; or 0.25 L/s. ...
This paper presents a novel method to compute various measures of effectiveness (MOEs) at a signalized intersection using vehicle trajectory data collected by flying drones. MOEs are key parameters in determining the quality of service at signalized intersections. Specifically, this study investigates the use of drone raw data at a busy three-way signalized intersection in Athens, Greece, and builds on the open data initiative of the pNEUMA experiment. Using a microscopic approach and shockwave analysis on data extracted from real-time videos, we estimated the maximum queue length, whether, when, and where a spillback occurred, vehicle stops, vehicle travel time and delay, crash rates, fuel consumption, CO2 emissions, and fundamental diagrams. Results of the various MOEs were found to be promising, which confirms that the use of traffic data collected by drones has many applications. We also demonstrate that estimating MOEs in real-time is achievable using drone data. Such models have the ability to track individual vehicle movements within street networks and thus allow the modeler to consider any traffic conditions, ranging from highly under-saturated to highly over-saturated conditions. These microscopic models have the advantage of capturing the impact of transient vehicle behavior on various MOEs.
... Additionally, because the function for fuel consumption is a second degree polynomial with respect to vehicle specific power (VSP), the partial derivative with respect to torque is a function of torque and the bang-bang control is not produced [20]. The model also can be used for different vehicle classes including light-duty vehicles [20], heavy-duty vehicles [21], and buses [22]. ...
... VT-CPFM function for fuel consumption is a second degree polynomial with respect to vehicle specific power (VSP) using Equation (6) [20]. We used VT-CPFM in this study for the different vehicle classes including light-duty vehicles [20], heavy-duty vehicles [21], and buses [22] that passed through the study area. We found that the total fuel consumption resulted from all the 750 vehicles passing through the approach is about 207L during the 14-min study period; about 0.28L per vehicle; or 0.25 L/s. ...
This paper presents a novel method to compute various measures of effectiveness (MOEs) at a signalized intersection using vehicle trajectory data collected by flying drones. MOEs are key parameters in determining the quality of service at signalized intersections. Specifically, this study investigates the use of drone raw data at a busy three-way signalized intersection in Athens, Greece, and builds on the open data initiative of the pNEUMA experiment. Using a microscopic approach and shockwave analysis on data extracted from realtime videos, we estimated the maximum queue length, whether, when, and where a spillback occurred, vehicle stops, vehicle travel time and delay, crash rates, fuel consumption, CO2 emissions, and fundamental diagrams. Results of the various MOEs were found to be promising, which confirms that the use of traffic data collected by drones has many applications. We also demonstrate that estimating MOEs in real-time is achievable using drone data. Such models have the ability to track individual vehicle movements within street networks and thus allow the modeler to consider any traffic conditions, ranging from highly under-saturated to highly over-saturated conditions. These microscopic models have the advantage of capturing the impact of transient vehicle behavior on various MOEs.
... The differences in road transport conditions present difficulties in calculating fuel consumption and costs. Therefore, Figure 4 is used to calculate the fuel consumption for a heavy-duty truck considering EU and US transport conditions [28]. A literature review shows that fuel consumption varies with road conditions and engine and equipment characteristics. ...
Deciding on appropriate transport modes is critical in terms of emissions, time, and cost. However, transport routes do not always allow for the selection of the most cost-effective and environmentally friendly modes of transport. Therefore, various modes of transportation must be used together to overcome these constraints. This study investigates the use of a combination of different transportation modes in container transport from Filyos in Turkey to Vienna. Constanța has been selected as the trans-shipment port on the transport route, and three distinct modes of transport have been used from Constanța to Vienna, including road, rail, and riverway. As a result of this study, the fuel consumption, CO2 emissions, time, and cost for each intermodal transport type were evaluated comparatively. Although seaway transportation is advantageous in terms of emissions, cost, and fuel consumption, it is determined that road transport is more beneficial in terms of time. The maximum and minimum CO2 emissions were calculated to be 2,107,124 tonnes and 365.6 tonnes for roadway and seaway transportation, respectively.
... The first type of model primarily employs mathematical formulas based on the internal structure of vehicles and the operating principles of their components, such as the physical or chemical processes within the engine, to provide accurate predictions [10]. For instance, Chang et al. [11] utilized sensors installed along specific road sections to capture vehicle state parameters at designated locations as inputs for their model. ...
With the increasing number of heavy-duty trucks and their high fuel consumption characteristics, reducing fuel costs has become a primary challenge for the freight industry. Consequently, accurately predicting fuel consumption for heavy-duty trucks is crucial. However, existing fuel consumption prediction models still face challenges in terms of prediction accuracy. To address this issue, a model named Cross-LSTM Multi-Feature Distillation (CLMFD) is proposed. The CLMFD model employs the Crossformer model and the LSTM model as teacher and student models, respectively, utilizing multi-layer intermediate features for distillation. Fuel consumption data from a vehicular networking system was used in this study. Initially, the raw data were preprocessed by segmenting it into two-kilometer intervals, calculating sample features, and handling outliers using box plots. Feature selection was then performed using XGBoost. Subsequently, the CLMFD model was applied to predict fuel consumption. Experimental results demonstrate that the CLMFD model significantly outperforms baseline models in prediction performance. Ablation studies further indicate that the CLMFD model effectively integrates the strengths of both the Crossformer and LSTM, exhibiting superior predictive performance. Finally, predictions on data with varying masking rates show that the CLMFD model demonstrates robust performance. These findings validate the reliability and practicality of the CLMFD model, providing strong support for future research in fuel consumption prediction.
... This model reduces the required parameters and improves applicability of the model. Wang and Rakha (Wang & Rakha, 2017) established a fuel consumption model for heavy-duty vehicles based on the VT-CPFM model. Verification shows that the model has high accuracy and can provide better predictions than the CMEM model, but the model still requires accurate vehicle parameters for further validation. ...
In order to predict the fuel consumption and carbon emission of vehicle driving on mountain city road, this research constructs energy conversion, fuel consumption and carbon emission model for N2 class heavy-duty diesel vehicle. The model is constructed based on the first law of engineering thermodynamics and the driving dynamic theory. The constructed model fully considers the impact of road slope characteristics on fuel consumption of mountain city roads and requires fewer parameters. The accuracy of the model is verified by actual road driving test data. Then, the prediction model is improved by adopting actual acceleration characteristics. Next, this research discusses the effects of speed, acceleration and slope on fuel consumption and carbon emission characteristics. Result indicates that when assuming the vehicle travels at a constant speed, the errors are large between measurement value and prediction value, the average errors are approximately 13% for fuel consumption and 14% for carbon emission. After considering the acceleration factor, the accuracy of the prediction model is significantly improved. Result shows that the correlation coefficient R² between predicted value and tested value increased by 0.154 for fuel consumption and 0.183 for instantaneous work done, indicating an enhanced correlation between these values. This article constructs a vehicle fuel consumption and carbon emission model for mountain city roads. The predicted results of the model can reflect the actual fuel consumption and carbon emission levels during driving. Model developed in this paper has a typical physical meaning and can be applied to other roads and other vehicles.
... The significance of this issue becomes evident when considering that transportation activities account for 28% of total U.S. energy use and 33.4% of CO2 emissions. Notably, heavy-duty diesel trucks, despite being a minority in the vehicle population, are major contributors, responsible for 22.8% of CO2 emissions in the transportation sector [1]. With the projected growth of the global truck fleet to 64 million by 2050, coupled with the price inelasticity of fuel and its consistent rise, it underscores the urgency of addressing these issues, as they have the potential to create both environmental and economic concerns in the future [2] [3]. ...
In recent years, the drive to improve aerodynamic efficiency in heavy-duty trucks has increased due to its substantial influence on CO2 emissions in the transportation sector. Research on the dermal denticles of fast-swimming marine fauna, such as sharks, has inspired the concept of employing riblet-covered surfaces. Riblets offer a straightforward approach to skin friction drag reduction compared to other methods, sparking a growing interest in their construction and optimization. This study focuses on examining a continuous sawtooth riblet. For the first time, micro-riblets are applied to a heavy-duty truck in ANSYS FLUENT using computational fluid dynamics (CFD) to quantify the reduction in viscous drag force experienced by heavy-duty trucks. It was found that the implementation of riblets led to a significant reduction of 3.38% in viscous drag.
... Over the past decades, several fuel consumption and emissions models have been developed [27,28]. The variations in fuel consumption and emissions from different models are mainly due to the type and properties of the vehicle being modeled. ...
Energy consumption and emissions of a vehicle are highly influenced by road contexts and driving behavior. Especially, driving on horizontal curves often necessitates a driver to brake and accelerate, which causes additional fuel consumption and emissions. This paper proposes a novel optimal ecological (eco) driving scheme (EDS) using nonlinear model predictive control (MPC) considering various road contexts, i.e., curvatures and surface conditions. Firstly, a nonlinear optimization problem is formulated considering a suitable prediction horizon and an objective function based on factors affecting fuel consumption, emissions, and driving safety. Secondly, the EDS dynamically computes the optimal velocity trajectory for the host vehicle considering its dynamics model, the state of the preceding vehicle, and information of road contexts that reduce fuel consumption and carbon emissions. Finally, we analyze the effect of different penetration rates of the EDS on overall traffic performance. The effectiveness of the proposed scheme is demonstrated using microscopic traffic simulations under dense and mixed traffic environment, and it is found that the proposed EDS substantially reduces the fuel consumption and carbon emissions of the host vehicle compared to the traditional (human-based) driving system (TDS), while ensuring driving safety. The proposed scheme can be employed as an advanced driver assistance system (ADAS) for semi-autonomous vehicles.
... EUA involves pricing 1 tone of CO2 emission in 2030 at 47 €/t and increasing it linearly every five years reaching 386 €/t in 2050. Provided that large tracks consume roughly 0.4 liters of diesel per kilometer [13] and each liter of diesel produces 2.7 kg of CO2 [14], then the time-variant element of the transportation cost starting from 2030 can be calculated by the following formula: ...
Forest biomass is one of the most significant renewable energy sources in the Czech Republic. Recently, Czech forests have been under attack by spruce bark beetles, threatening the stability of biomass supply also for energy purposes. The goal of this paper is to uncover the impact of four different biomass development scenarios and three policies on the energy system and to evaluate the contribution of biomass to decarbonization efforts. We use the TIMES-CZ energy system optimization model to perform the analyses. We provide a crucial extension of this model by regionalizing it into NUTS3 +1 regions. Our main findings are that under the expected price of various types of biomass and their transportation cost, energy system would still exploit available biomass almost entirely throughout the period under review; subsidizing the production cost of the most expensive type of biomass for households would help utilize the full potential of biomass; and power and heat sector and industry would compete for biomass consumption against the residential sector.
... However, the influence of ship power models on marine emissions inventories has not received much focus while significant differences in the results of different power prediction models have been reported (Brown and Aldridge 2019). A power prediction model is applied for a variety of applications, including ship and propeller design (Esmailian et al. 2019(Esmailian et al. , 2017, weather routing (Kim and Kim 2017;Shao et al. 2012), fleet performance analysis (Vernengo et al. 2016;Kim et al. 2023), modelling and analysis of the ship propulsion system (Saettone et al. 2020;Tadros et al. 2021), energy management of the ship power system (Planakis et al. 2022), modelling the ship emission and the fuel consumption (Wang and Rakha 2017;Kim et al. 2021), hull condition monitoring (Koboević et al. 2019), and other operational optimization purposes (Sun et al. 2013;Tillig et al. 2020). Over the past few decades, a large number of research have been published suggesting methods to compute different components of a power prediction model, including calm water resistance (Guldhammer and Harvald 1974;Holtrop 1984;Hollenbach 1998;Kristensen and Lützen 2012), added resistance due to waves (Faltinsen 1980;ISO 2015;Grin 2015), added resistance due to winds (Blendermann 1995;Watson 1998;ISO 2015;Kitamura et al. 2017), propulsion system efficiency (Oosterveld and van Oossanen 1975;Schneekluth and Bertram 1998;ITTC 2014), and so on. ...
... Prediction of engine performance for heavy-duty vehicle Demir et al. [36] Review and numerical comparison of six emissions models that reflect driving conditions Wang et al. [37] Fuel consumption model dependent on the driving conditions while indicating the optimum speeds and 'eco-freight strategies' Perugu et al. [38] Approximation of particulate matter emissions and resulting local air quality using fleet spatial activity data Ligterink et al. [39] The estimation method of emissions dependent on velocity and payload with improved predictions for nitrogen oxides Zamboni et al. [40] Statistical investigation of the speed patterns to estimate fuel consumption and related emissions Seo et al. [41] Estimation of the fleet CO 2 emissions by the bottom-up approach and validation by chassis dynamometer tests Zacharof et al. [42] Estimation of fleet representative CO 2 emissions utilizing different sampling methods Prussi et al. [43] Comparison of different fuel options in terms of CO 2 emissions and expanded energy over the well-to-wheel perspective Zhang et al. [44] Comprehensive statistical analysis while investigating the influence of test cycle and fuel property on HD diesel engine Tucki [45] Tool for modeling CO 2 emissions in vehicle's driving tests for neat renewable fuels including FAME, rapeseed oil and butanol Vijayagopal et al. [46] and Gao et al. [47] (Autonomie) Autonomie tool -driving cycles' simulations that can be applied to the evaluation of fuel consumption for various engine technologies Wang et al. [48] (GREET) Greenhouse gases, Regulated Emissions, and Energy use in Technologies model (GREET) -analytical tool for GHG emission analysis over the lifecycle Vallamsundar and Lin [49] (MOVES) Motor Vehicle Emission Simulator (MOVES) model used in the US to estimate emissions at national, regional or project scale Fontaras et al. [50] (VECTO) Vehicle Energy Consumption calculation Tool (VECTO) used in the EU for HD vehicle legislation purposes ...
... The first type of model is mainly built through mathematical formulas based on the internal structure of the vehicle and the working principles of components, such as the engine. The model's transparency is high and can provide more accurate prediction results [2]. However, the research of this type of model is mainly focused on the fixed path in some specific areas, and the influence of different road types and weather conditions on fuel consumption is often ignored, resulting in a single data dimension and the poor applicability of models. ...
Accurately and efficiently predicting the fuel consumption of vehicles is the key to improving their fuel economy. This paper provides a comprehensive review of data-driven fuel consumption prediction models. Firstly, by classifying and summarizing relevant data that affect fuel consumption, it was pointed out that commonly used data currently involve three aspects: vehicle performance, driving behavior, and driving environment. Then, from the model structure, the predictive energy and the characteristics of the traditional machine learning model (support vector machine, random forest), the neural network model (artificial neural network and deep neural network), and this paper point out that: (1) the prediction model of fuel consumption based on neural networks has a higher data processing ability, higher training speed, and stable prediction ability; (2) by combining the advantages of different models to build a hybrid model for fuel consumption prediction, the prediction accuracy of fuel consumption can be greatly improved; (3) when comparing the relevant indicts, both the neural network method and the hybrid model consistently exhibit a coefficient of determination above 0.90 and a root mean square error below 0.40. Finally, the summary and prospect analysis are given based on various models’ predictive performance and application status.
... Authors such as Madhusudhanan, Ainalis, Na, Garcia, Sutcliffe and Cebon (2021) investigated the effect of lightweight trailers and aerodynamics on the fuel consumption of HGVs. Others, such as Wang and Rakha (2017), developed a model to estimate a vehicle's fuel consumption and emissions based on parameters such as vehicle power, acceleration and braking, speed, gross vehicle weight and road gradient. Sharpe and Muncrief (2015) attempted to determine the fuel consumption of HGVs on a country level through a literature review, neglecting the impact of operational conditions. ...
Road freight transportation is and will become increasingly important in all distribution chains. However, little research has analysed actual logistics service provider (LSP) data on a transport service level to determine fuel use and emissions in real-world scenarios. Subsequently, this article analyses 147 long-distance trips during which nearly 200 000 km were travelled, 3 693 tonnes of cargo were moved, and 84 588 litres of diesel fuel were burnt. In addition, 23 250 hours of refrigeration data were assessed. Based on the assessed data, a novel formula was developed that estimates the total fuel use (ℓ) of a transport service by incorporating the trailer type, route, load weight, empty distance, loaded distance and use and time duration of refrigeration with an average error of 6.7 %. This formula enables estimation of the total fuel use (ℓ), GHG emissions (kg CO 2 e), carbon footprint (kg CO 2 e/t cargo) and emission intensity factor (g CO 2 e/t-km).
... In the past decade, numerous effective formulas have been proposed to estimate fuel consumption, most of which have used the vehicle engine power concept (Aerde, 2002;Rakha et al., 2004Rakha et al., , 2011Int Panis et al., 2006;Wang and Rakha, 2017;Caspari et al., 2022). As shown in Fig. 1, the applied forces acting on a moving vehicle consists of four components: the driving force , aerodynamic drag force , rolling resistance force , and gravitational resistance force . ...
Eco-routing aims to provide fuel-efficient paths to help travelers make route choice decisions in the road network. Most eco-routing algorithms build on a deterministic assumption about link fuel consumption. However, link fuel consumption in real road networks is highly stochastic caused by travel speed variations. This study investigates the eco-routing problem by explicitly considering uncertainties of travel time and fuel consumption. A stochastic fuel consumption formula to estimate fuel consumption distributions caused by travel speed variations is proposed. A bi-objective reliable eco-routing model is developed by minimizing the travel time budget and fuel consumption budget simultaneously while satisfying given constraints on travel time reliability and fuel consumption reliability. An efficient reliable path ranking algorithm is developed to solve the formulated model exactly. A comprehensive case study using real data from Hong Kong is presented to demonstrate the applicability of the proposed model and algorithm.
... (1) remove all records that are not in the test route; (2) identify the start and end points for each test trip according to the route direction; (3) verify, for every trip, that the bus has covered the entire route; (4) remove trips that are either incomplete or have variations; (5) standardise idle times (zero speed) at both the beginning and end of each test trip; (6) smooth the speed profile for each experimental test according to a moving average filter (Wang and Rakha, 2017); and (7) correct and smooth the altitude profile for each experimental test according to EU Regulation 2016/646 to obtain the second-by second road grade. At the end, a total of approximately 190 valid trips (including outbound and inbound trips) were obtained, representing approximately 530,000 second-by-second GPS position data points for the buses. ...
In recent years, the integration of traffic simulators and emission models has become the most preferred option for evaluating vehicle emissions in different traffic states. However, the definition of a 'traffic condition' is often subjective, as driving patterns can vary significantly with the spatial domain of study. Alternatively, the implementation of 'Cooperative Intelligent Transport Systems' has led to a growing variety of devices being installed, both on the road and in public transport vehicles for monitoring the traffic-flow conditions and vehicle speeds in cities. This study purposed an original approach for integrating real-world emissions (as an micro-emission model), real-world driving profiles, and city traffic sensor data to assess the effects of traffic congestion at the route level on emissions from urban buses in Madrid (Spain). The definition of the traffic scenarios was based on a K-means clustering analysis by linking stationary (from city sensors) and dynamic (from bus driving profiles) congestion indicators. In parallel, a micro-emissions model based on vehicle-specific power (VSP) methodology was used to model the second-by-second CO2 and NOx emissions from individual trips of the diesel and compressed natural gas (CNG) buses. Finally, the clustering and modelled emissions data were combined. A comparison of the free flow and the severe congestion scenarios showed that the average speed of the route decreased by approximately 50 %, and the number of stops per kilometre increased by a multiple of 1.5; furthermore, the CO2 and NOx emissions from buses increased by approximately 50 % and 85 %, respectively. The diesel bus showed a lower sensitivity to variations in the congestion level at the route level, although the low-NOx emissions from the CNG buses were evident for all traffic scenarios. The results of this study, based on extensive real-world data, can be used to develop high-resolution vehicle emissions inventories.
... Based on this assumption, retrieving generic aerodynamic coefficients from the literature [24,25] and obtaining a CdA value was possible. On the other hand, the rolling resistance coefficient (RRC) value was retrieved based on the tyre's energy efficiency class, where a representative value was selected and also confirmed by Ref. [26]. The vehicle had tyres with 275/70 R22.5 dimensions, which were used for calculating the dynamic rolling radius (R dynamic). ...
The efforts to reduce CO2 emissions from road transport have focused mainly on passenger cars, but the attention shifts to heavy-duty vehicles, which contribute significantly to the sector’s greenhouse gas emissions despite their low fleet share. The current study focused on a bus and investigated the effect of environmental conditions and passenger occupancy on auxiliary usage and CO2 emissions. It utilised measurements conducted over the Zincirlikuyu – Avcilar route in Istanbul, Turkey, to develop vehicle simulation models. The vehicle was simulated under different environmental conditions and passenger occupancy, and it was found that the auxiliary use could differ between -1.05 and 2.67 kW compared to the European reference conditions. Simulations with variable passenger capacity showed no difference with runs that utilised a standard average passenger capacity value. In addition, a factorial analysis was performed to cover a broader range of environmental conditions. The findings were used to develop a methodology to calculate vehicle-specific CO2 emissions under different on-road conditions by utilising the vehicle’s reference emissions. The study results are expected to contribute to the field as HVAC energy demands could increase on-road CO2 emissions and limit the range of electrified powertrains.
... where m is the vehicle mass in kg, a(t) is the value of acceleration in m/s 2 at t, g is the gravitational acceleration (9.8066m/s 2 ), θ is the road inclination angle (assumed to be zero if unavailable), C r , c 1 , c 2 are the rolling coefficients (unitless), v(t) is the instantaneous vehicle speed in m/s at t, ρ a is the air density at sea level (1.2256kg/m 3 ), A f is the frontal area (m 2 ) of the vehicle, C D is the drag coefficient (unit-less), η d is the driveline efficiency. For details of the VT-CPFM model and how the coefficients are calculated, please see [18,22]. ...
Traditional vehicle routing algorithms aim to find the fastest or shortest route, whereas eco-friendly routing algorithms aim to find the route that minimizes vehicle fuel consumption or greenhouse gas (GHG) emissions. To accurately estimate fuel consumption and emissions along a route, a detailed mobility profile of the vehicle traveling on the route is needed including acceleration/deceleration and idling time. However, the existing techniques that aim to find eco-friendly routes make a simplistic assumption by assigning each road segment of the road network an average speed or average fuel consumption along the road, ignoring detailed mobility profiles and driving behavior (e.g., aggressive or moderate) altogether. This simplistic treatment leads to sub-optimal route choices because such representation fails to capture driving behaviors and detailed mobility profiles of the candidate routes resulting in poor quality estimates. Furthermore, many of the existing techniques employ a one-size-fits-all approach ignoring that different vehicles (e.g., truck vs car) and drivers exhibit different behaviors, thus, the most eco-friendly route may be significantly different for different types of vehicles and/or drivers. This paper addresses these limitations and presents an eco-routing algorithm that computes the most fuel economical route considering the detailed mobility profiles, driving behavior, and vehicle type. We conduct an extensive experimental study on a real road network considering different vehicles and driving behaviors and show that our algorithm generates routes that reduce fuel consumption by up to 35%.KeywordsEco-routingNavigation systemsMobility profileMicroscopic simulationIntelligent transportation systems
Fuel efficiency in heavy-duty trucks in Indonesia faces significant challenges, while the current HDM-4 fuel consumption model has limitations in reflecting local conditions. This study calibrates the HDM-4 model using telematics data, engine speed modeling, aerodynamic simulations, and calibration factors. The novelty lies in updating parameters such as engine speed, vehicle frontal area, and calibration factors for engine power efficiency (Kpea) and rolling resistance (Kcr2) to account for tire-road interaction in Indonesian conditions. Data were collected from 5-axle trucks on the Tanjung Priok–Bandung toll road, analyzed using regression, Computational Fluid Dynamics (CFD) simulations, and non-parametric paired tests. Results show updated engine speed parameters (RPM_a0 = 680.11, RPM_a1 = -4.9031, RPM_a2 = 0.3858, RPM_a3 = -0.0028), a drag coefficient of 1.0556, and a frontal area of 8.2 m². Calibrating Kpea and Kcr2 (both 0.6) improved prediction accuracy, with no significant difference between predicted and observed data (p = 0.186). The enhanced HDM-4 model supports operational decisions, infrastructure planning, and sustainable transport policies, improving energy efficiency, reducing emissions, and boosting national logistics competitiveness.
We consider the problem of minimizing emission of a heavy-duty truck transporting freight between two locations subject to a hard deadline constraint. The truck is equipped with a multi-speed transmission and a modern combustion engine that intelligently switches among multiple fuel injection strategies at certain engine speeds (called
switching speeds
) to achieve lower emission profiles. Our objective is to minimize the emission by optimizing both path and speed planning for heavy-duty trucks with multi-speed transmission and multiple injection strategies in the engine. This emission minimization problem, while pervasive in practice, has two challenges: i) the emission rate function is discontinuous and non-convex due to switching of the fuel injections and gear ratios, which makes the common practice of driving at a constant speed on a road segment not eco-friendly; ii) the problem is NP-hard due to the combinatorial nature of the simultaneous path and speed planning. We tackle the first challenge by considering the case where the truck can travel at a heterogeneous speed profile over a road segment and then formulate the speed planning problem as a convex problem. We further identify special structures in this problem and provide an efficient method for computing the optimal speed profile. We then tackle the second challenge by developing an efficient heuristic for both path planning and speed planning to solve the emission minimization problem on the scale of national highway systems. Our extensive simulations on the US highway system show that our solution reduces up to 46% NOx emission as compared to the commonly-adopted fastest path approach. We also find that optimizing heterogeneous speed profiles reduce up to 32% emission as compared to their homogeneous counterpart, thus are necessary to be considered in eco-friendly truck operations.
Last-mile logistics poses a pivotal challenge for the sustainable prosperity of fresh food e-commerce. However, there has been insufficient research attention towards optimizing the last-mile vehicle routing from three sustainable dimensions simultaneously based on triple-bottom-line. To narrow this gap, this paper proposes a multi-objective last-mile vehicle routing model for fresh food e-commerce, which takes the three sustainable dimensions as independent objectives. The economic and environment objectives are minimizing the total cost and carbon emission, respectively. For social dimension, a nonlinear function is proposed based on the Prospect theory, which quantifies customers’ satisfaction when they perceive delivery time. Then the social objective is described as maximizing customers’ satisfaction. Subsequently, the model is solved by the nondominated sorting genetic algorithm II and validated by a numerical experiment. Based on the experiment results, this paper recommends that logistics enterprises should prioritize the trade-off among the three sustainable dimensions.
Insufficient consideration of vehicle weight dynamics during real-world driving could lead to inaccurate fuel consumption estimates. This study examined the impact of vehicle weight on fuel consumption rate (FCR) by analyzing extensive, high-resolution operating data obtained from 162 heavy-duty trucks (HDTs). An engine output power-based (EOP) model, an artificial neural network (ANN) model, and a long short-term memory-convolutional (LSTM-Conv) model were developed and contrasted with conventional vehicle specific power (VSP) and Virginia Tech Microscopic (VT-Micro) models. The results indicated a significant, non-linear relationship between weight and FCR. Compared to 5-ton trucks, FCR for trucks weighing 15–25 tons and 45–55 tons increased by 290% and 755%, respectively, under low-speed and positive acceleration conditions. The LSTM-Conv model outperformed the VSP, VT-Micro, and EOP models, achieving MAPEs of 9.81% for FCR and 1.49% for trip fuel economy estimation. The deep-learning models exhibited enhanced stability across varying speeds, accelerations, and vehicle weights.
With the increasing emphasis on sustainable transportation solutions, there is an urgent need for a system that can accurately assess vehicle-specific energy consumption in real-time. However, the current modeling frameworks have a significant gap in that they are not demand- and congestion-responsive, nor are they capable of modeling the system-wide energy consumption of bus fleets at the vehicle-level, encompassing all three types of powertrains—conventional, hybrid, and electric. To address this gap, we present a comprehensive modeling framework that takes into account passenger loading, considers the dynamics of the system including acceleration, and is capable of predicting the real-time energy usage of each bus in a transit network. Using the Pioneer Valley Transit Authority system as a case study, we calibrated existing powertrain-specific models for conventional diesel, hybrid, and battery electric buses. Our demand- and congestion-responsive energy consumption modeling framework fills this critical gap and offers significant potential for future sustainable transportation planning. Relying on detailed trajectories computed from one month of bus location data, along with passenger counts and road grade, our framework is a novel contribution in that it provides spatiotemporal energy predictions for an entire bus system. As it is readily calibrated with potential improvements in accuracy given more data, this framework can be applied by transit agencies for the purposes of real-time energy consumption monitoring, fleet electrification planning, equity analyses, and carbon emission reduction strategies.
Over the years, in most countries, more strict regulations are being implemented on the emissions resulting from fossil fuels combustion. Among all the conventional fuels used, diesel is considered the one being highly polluting in terms of soot emissions. Therefore many researchers and organizations are continuously looking for technologies to reduce the concentration of particulates produced from diesel burning activities. One of the promising solutions is the hydrocarbon-based additives for Diesel fuel (DF) that can reduce the soot emissions and other harmful pollutants from diesel engine.
Diesel as a fuel and its applications
The fossil fuel mainly diesel and gasoline, are blends of hydrocarbons produced by fractional distillation of crude oil in petroleum refineries [1-3]. Based on the fuel used, the engines were classified into gasoline and diesel engines, which use gasoline and diesel fuels, respectively. Both are internal combustion engines, where chemical energy from the fuel is converted to mechanical energy. The main difference between the two engines is their mechanism of operation. In the gasoline engine, fuel and air are mixed first, and then, the mixture is compressed by pistons before ignition take place by spark generated from the spark plugs. On the other hand, the air is initially compressed in the diesel engine, before the fuel is injected, and then the autoignition of the fuel takes place in hot compressed air [4].
Despite of the fuel type, fossil fuels are generally witnessing an increase in their demand all over the world because of their high energy content and combustion efficiency, consequently, the level of pollutants in the atmosphere is rising as well [5, 6]. DF is mainly used due to their excellent drivability and high durability for many applications such as electric power generation, transportation (including road trucks, military, marine shipping and rail transportation) and off-road uses (e.g., mining, farming and construction) [7]. However, the main concern with the using DF is the pollution caused and its impact on environment [8]. Pollutants are formed as a result of the incomplete combustion in internal engines. Such as soot which known as particulate matter as well, carbon monoxide (CO), nitrogen oxides (NOx), sulfur oxides (SOx) and volatile organic compounds (VOC) [4, 9].
Fuel cost contributes significantly to the high operation cost of heavy-duty trucks. Developing fuel rate prediction models is the cornerstone of fuel consumption optimization approaches for heavy-duty trucks. However, limited by accurate features directly related to the truck’s fuel consumption, state-of-the-art models show poor performance and are rarely deployed in practice. In this paper, we use the truck’s engine management system (EMS) and Instant Fuel Meter (IFM) to collect a three-month dataset during the period of December 2019 to June 2020. Seven prediction models, including linear regression, polynomial regression, MLP, CNN, LSTM, CNN-LSTM, and AutoML, are investigated and evaluated to predict real-time fuel rate. The evaluation results show that the EMS and IFM dataset help to improve the coefficient of determination of traditional linear/polynomial models from 0.87 to 0.96, while learning-based approach AutoML improves the coefficient of determination to attain 0.99. Besides, we explore the actual deployment of fuel rate prediction with transfer learning and path planning for autonomous driving.
Şanzıman, motordan aldığı torku aracın tekerleklerine ileten güç aktarım organlarından kritik öneme sahip olan bir mekanizmadır. Araçlarda ise şanzıman tipine göre yakıt tüketimi farklılık göstermektedir. Yakıt ekonomisini iyileştirmek, emisyonu azaltmak ve sürüş performansını artırmak için son yıllarda şanzıman alanında birçok çalışmalar yapılmış olup araçlar üzerinde uygulanmıştır. Günümüzde birçok şanzıman çeşidi olmasına rağmen yaygın olarak kullanılan manuel ve otomatik şanzımanlardır. Bu şanzıman tiplerinin birbirine göre avantaj ve dezavantajları bulunmakta olup farklılıklar göz önünde bulundurularak yeni bir şanzıman arayışı ortaya çıkmıştır. Çalışmada manuel ve otomatik şanzımanın özellikleri birleştirilerek vites değiştirme bileşenleri ile robotize(otomatikleştirilmiş manuel) şanzıman geliştirilmiştir. Manuel şanzıman sistemine entegre edilen vites değiştirme bileşenleri şanzımanın otomatikleştirilmesinde kullanılmıştır. Robotize hale getirilen otomatikleştirilmiş manuel şanzıman için kontrolü sağlanacak vites değişim parametreleri belirlenmiştir. Vites değiştirme parametrelerine ait optimum değerlerin bulunması için araç üzeri test çalışmaları yapılarak dinamik test sürüşleri gerçekleştirilmiştir. Bilindiği üzere manuel şanzımanlar otomatik şanzımanlardan daha verimli olmasına rağmen otomatik şanzımanda vites değişimi sürücüye bırakılmaksızın vites değişim parametrelerine göre otomatik olarak değişmektedir. Yapılan çalışmada ise vites değişimi sürücüden bağımsız halde bir vites değiştirme algoritmasına bağlı olarak kontrol edilmiştir. Sürücü ve vites değiştirme algoritması kontrolüne bağlı olarak yakıt tüketimleri dinamik test sürüşü ile ölçülmüştür. K-means algoritması kullanılarak optimum vites değiştirme parametrelerine ait değer aralıkları belirlenmiştir. Optimum vites değiştirme parametrelerine göre araç ortalama 6,79 L/100km yakıt tüketmiştir. Sürücü algoritmadan bağımsız şekilde aracı kullanması sonucu ortalama 8,62 L/100km yakıt tükettiği gözlemlenmiştir. Vites değiştirme parametrelerinin optimize edilmesi ile %27 oranında yakıt tasarrufu sağlanmıştır.
In this paper, an estimation model based on bilinear interpolation of data from the EMEP/EEA air pollutant emission inventory guidebook database regarding fuel consumption and exhaust emissions of commercial vehicles in different operating conditions is developed. Depending on vehicle's average speed, road slope and load factor, the model determines the fuel consumption and emissions for the observed vehicle type, category and emission standard. In order to validate the model, recordings on actual commercial vehicles were made in real operating conditions on two urban public transport bus lines in Belgrade. Comparison of the estimated and actual results from vehicle operation substantiate satisfactory validation of the model, making it suitable for logistics managers in operational and tactical planning as easy to use decision making tool allowing better sustainability and lower environmental footprint.
The fuel efficiency of the transportation sector has become a key factor to reduce greenhouse gas emissions and fuel consumption in response to the negative impacts of global warming. As an approach to energy saving and environmental sustainability, eco-driving has attracted considerable research interest in the past decades. This review aims to provide a comprehensive review of the research on eco-driving using methodologies of literature bibliometrics and content analysis through VOSviewer software. The following keywords “ecological-driving”“, ecological-routing”“, ecological-bus”“, ecological-car”“, ecological-vehicle”“, eco-driving”“, eco-routing”“, eco-driver”“, eco-bus”“, eco-car” and “eco-vehicle” are used for paper retrieval. The query was conducted on January 20, 2021. The results take account of all journal articles, proceedings papers, and reviews without time limitation. Finally, a total of 767 documents were retrieved as total publications, which were viewed over the period 2001–2020 based on the Web of Science (WoS) Core Collection database. The publication year, leading countries, leading sources, leading institutions, leading authors, document citation, and document co-citation were analyzed to explore the primary trends. The In-depth analysis reveals five clusters of keywords, and the review of relevant studies on eco-driving from five different perspectives is carried out to identify potential trends and future research hot spots of eco-driving.
It is important to understand real-world performance of fuel-saving technologies for heavy goods vehicles (HGVs). This paper concerns applying data mining to quantify the fuel-saving benefit of a low-rolling-resistance (LRR) tyre using in-service operational data. Two HGVs of the same specification were employed, with one using LRR tyres and the other conventional tyres. A smartphone-based data logger was developed to collect data from the HGVs’ operations on publics roads for three months. Data from vehicle Controller Area Network, smartphone sensors, weather and elevation databases were collected. A data mining methodology was developed to mine data segments representing dynamically similar driving conditions between the two HGVs, and compare their fuel consumption rates through fitting data to a theoretical vehicle fuel consumption model. It was found that at a 95% confidence level the LRR tyres exclusively brought about a fuel-saving benefit between 6.89% and 8.37% in typical UK motorway driving conditions.
To confront the lack of robust and accessible models predicting diesel truck fuel consumption, this study develops an Engine-based Correction Model (ECM) using a sample containing vehicle operation and road data from heavy-duty diesel trucks (HDTs) along an urban arterial in Sichuan Province, China. This model is compared with two popular methods, the VT-Micro model and the CMEM model, in terms of goodness-of-fit and out-of-sample prediction performance. The results show that the proposed ECM models explain on average 89.7% of the variation in fuel consumption across the four modes and produce the smallest error, measured by MAPE and RMSE, for predicting each mode. Specifically, the MAPE values of the new ECM models are 7.95% to 25.59% lower than those of the VT-Micro models and 5.74% to 13.27% lower than the CMEM models. The improvement of RMSE values ranges from 0.1780 to 2.0940 cc/s and from 0.0171 to 0.7259 cc/s, compared to the VT-Micro model and CMEM model, respectively. In sum, the new ECM outperforms the other two existing models in both goodness-of-fit and predictive power across all modes. The enhanced performance of ECM can be attributed to the correction component and a model-building process powered by the simulated annealing (SA) algorithm that reliably and quickly weed out ineffective model forms. The results can inform driver training to foster energy-efficient driving behavior and assess the impact on fuel consumption of roadway design policies.
This paper presents a coordination strategy to optimally form and dissolve N-truck platoons on a highway stretch. Truck platooning, a set of trucks driving with small inter-vehicle distances, can benefit the transportation sector by reducing the overall fuel consumption and greenhouse gas emissions while increasing traffic throughput. However, different itineraries and delivery time restrictions may limit the opportunities to from platoons on a large scale. Therefore, a coordination strategy must be capable of merging scattered trucks and splitting the platoon considering the constraints from each participant to avoid penalties. To address this issue, an optimization problem is formulated to provide optimal speed profiles for an unlimited number of trucks during the merging, platooning and splitting phases of the coordination. An equivalent single stretch representation is presented to simplify complex road networks using appropriate merging and splitting constraints. The resulting optimal speed profiles are presented for 2, 3 and 10 trucks highlighting the capability to handle different desired traveling speeds without compromising the itinerary of each truck and allowing the overtake of trucks directly in the optimization problem. Sensitivity analyses are used to investigate the savings potential according to the main parameters of the coordination. Finally, the proposed algorithm is evaluated in a simulation study using validated vehicle and consumption models with real road topography data. In a 100 km Brazilian highway stretch, scenarios with two and three scattered trucks with substantial initial separation distances are evaluated and present energy efficient maneuvers under the proposed coordination strategy.
Electric hybridization technologies appear to be one of the most promising approaches to improving the energy efficiency of buses; however, this improvement has not been systematically quantified. A fuel consumption model is essential for capturing fuel consumption behavior accurately and quantifying the fuel benefits of hybrid buses. Consequently, the objective of this study was to develop a fuel consumption model for hybrid buses on the basis of the framework of the Virginia Tech Transportation Institute's comprehensive power-based fuel consumption model and then to quantify the benefits associated with hybridization technologies relative to conventional diesel bus operations. The model estimates were demonstrated to be consistent with in-field measurements, and the optimum fuel economy cruise speed was demonstrated to be approximately 50 km/h. The results demonstrate that hybrid buses consumed less fuel overall, while heavier buses and higher passenger loads may have reduced the fuel savings. The results also reveal that more fuel savings could be achieved for cruise and stop-and-go activity compared with idling behavior and that stop-and-go operation generated the highest level of fuel efficiency benefits. The conclusions of this paper can support bus planning applications to achieve fleet fuel savings.
Electric vehicles (EVs), plug-in hybrid electric vehicles (PHEVs), and hybrid electric vehicles (HEVs) are often considered as better options in terms of greenhouse gas emissions and energy consumption compared to internal combustion vehicles. However, making any decision among these vehicle options is not a straightforward process due to temporal and spatial variations, such as the sources of the electricity used and regional driving patterns. In this study, we compared these vehicle options across 50 states, taking into account state-specific average and marginal electricity generation mixes, regional driving patterns , and vehicle and battery manufacturing impacts. Furthermore, a policy scenario proposing the widespread use of solar energy to charge EVs and PHEVs is evaluated. Based on the average electricity generation mix scenario, EVs are found to be least carbon-intensive vehicle option in 24 states, while HEVs are found to be the most energy-efficient option in 45 states. In the marginal electricity mix scenario , widespread adoption of EVs is found to be an unwise strategy given the existing and near-future marginal electricity generation mix. On the other hand, EVs can be superior to other alternatives in terms of energy-consumption, if the required energy to generate 1 kW h of electricity is below 1.25 kW h.
There have been significant improvements in recent years in transportation and emissions modeling to allow better evaluations of transportation operational effects and associated vehicle emissions. In particular, instantaneous or modal emissions models have been developed for a variety of light-duty vehicles. To date, most of the effort has focused primarily on developing these models for light-duty vehicles with less effort devoted to heavy-duty diesel (HDD) vehicles. Although HDD vehicles currently make up only a fraction of the total vehicle population, they are major contributors to the emissions inventory. A description is provided of an HDD truck model that is part of a larger comprehensive modal emissions modeling (CMEM) program developed at the University of California (UC), Riverside. Several HDD truck submodels have been developed in the CMEM framework, each corresponding to a distinctive vehicle-technology category. The developed models use a parameterized physical approach in which the entire emission process is broken down into different components that correspond to physical phenomena associated with vehicle operation and emission production. A variety of trucks were extensively tested under a wide range of operating conditions at UC Riverside's Mobile Emissions Research Laboratory. The collected data were then used to calibrate the HDD models. Particular care was taken to investigate and implement the effects of varying grade and the use of variable fuel injection strategies. Results show good estimates for fuel use and the regulated emission species including nitrogen oxides, one of the key targets for HDD vehicles.
The paper presents the INTEGRATION microscopic traffic assignment and simulation framework for modeling eco-routing strategies. Two eco-routing algorithms are developed: one based on vehicle sub-populations (ECO-Subpopulation Feedback Assignment or ECO-SFA) and another based on individual agents (ECO-Agent Feedback Assignment or ECO-AFA). Both approaches initially assign vehicles based on fuel consumption levels for travel at the facility free-flow speed. Subsequently, fuel consumption estimates are refined based on experiences of other vehicles within the same class. The proposed framework is intended to evaluate the network-wide impacts of eco-routing strategies. This stochastic, multi-class, dynamic traffic assignment framework was demonstrated to work for two scenarios. Savings in fuel consumption levels in the range of 15 percent were observed and potential implementation challenges were identified.
Existing bus fuel consumption models produce a bang–bang type of control, implying that drivers would have to either accelerate at full throttle or brake at full braking in order to minimize their fuel consumption levels. This is obviously not correct. The paper is intended to enhance bus fuel consumption modeling by circumventing the bang–bang control problem using the Virginia Tech Comprehensive Power-based Fuel consumption Model (VT-CPFM) framework. The model is calibrated for a series of diesel-powered buses using in-field second-by-second data because of a lack of publicly available bus fuel economy data. The results reveal that the bus fuel consumption rate is concave as a function of vehicle power instead of convex, as was the case with light duty vehicles. The model is calibrated for an entire bus series and demonstrated to accurately capture the fuel consumption behavior of each individual bus within its series. Furthermore, the model estimates are demonstrated to be consistent with in-field measurements. The optimum fuel economy cruising speeds range between 40 and 50 km/h, which is slightly lower than that for gasoline-powered light duty vehicles (60–80 km/h). Finally, the model is demonstrated to capture transient fuel consumption behavior better than the Motor Vehicle Emission Simulator (MOVES) and produces a better fit to field measurements compared to the Comprehensive Modal Emission Model (CMEM).
The goal of this paper was to develop a calibration procedure and use it to estimate diesel bus fuel consumption and carbon dioxide emission levels. There are few models for estimating those values. Available models require dynamometer data to calibrate model parameters and produce a bang-bang control system (optimum control entails maximum throttle and braking input). The only diesel fuel consumption model that does not suffer from these deficiencies is the Virginia Tech comprehensive power-based fuel consumption model (VT-CPFM). VT-CPFM can be calibrated with publicly available data from the Altoona Bus Research and Testing Center. However, each bus is slightly different because it is built and tuned for the specific transit agency. Consequently, research presented in this paper enhanced the VT-CPFM for modeling diesel buses and developed a procedure for calibrating bus fuel consumption models by using in-field data. All models produced a good fit to the in-field data with a coefficient of determination (R2) greater than .936, and the sum of the mean squared error for each quarter of a second was less than 0.002. Validation found an average error of 17.55% in total fuel consumed during the validation portion of the test. However, for tests with air-conditioning on, the average error was 10.82%.
Reducing greenhouse gas emissions is a highly prevalent goal of public policy in many countries around the world. Convincing people to drive more fuel-efficiently ("eco-driving") can contribute substantially to this goal and is often an integral part of policy initiatives. However, there is a lack of scientific studies on the effects of individual monetary and non-monetary incentives for eco-driving, especially in organizational settings and with regards to demonstrating causality, e.g., by using controlled experiments. We address this gap with a six months long controlled natural field experiment and introduce a monetary and a non-monetary reward for eco-driving to drivers of light commercial vehicles in different branches of a logistics company. Our results show an average reduction of fuel consumption of 5% due to a tangible non-monetary reward and suggest only a small reduction of the average fuel consumption in the equivalent monetary reward treatment. We find indications that more emphasis on the fun of achieving a higher fuel efficiency, a more emotional response to non-monetary incentives, and a higher frequency of thinking and talking about non-monetary incentives might play a role in the stronger effect of the tangible non-monetary reward. Policy implications for private and public actors are discussed.
In this research, two parallel hybrid- and two diesel-buses fitted with selective catalytic reduction systems were tested in real world conditions using portable emission measurement systems. The hybrid buses were chosen to operate in either hybrid mode or diesel mode. In hybrid mode, the buses consumed less fuel, but the brake specific fuel consumptions were higher. The hybrid buses produced less engine-out NOX emissions than diesel buses, but as a result of lower exhaust temperature and lower efficiencies of SCR systems, the tailpipe NOX emissions from hybrid buses were a little higher. The brake specific NOX emissions from hybrid buses were very high and beyond the limit value of Euro-IV standard. History effect was also important for the efficiency of SCR system. The CO emissions from hybrid buses were lower in the unit of g/s, but the fuel-based CO emissions were higher than diesel buses.
Few models estimate fuel consumption and carbon dioxide emission levels of diesel and hybrid buses. Models that are available either require the gathering of significant dynamometer data to calibrate the model parameters or produce a bang-bang control system, which results when optimal control entails use of maximum throttle and breaking use. This paper extends the Virginia Tech Comprehensive Power-Based Fuel Consumption Model (VT-CPFM) to model diesel and hybrid buses. The bus parameters are calibrated with publicly available data from the Altoona Bus Research and Testing Center in Altoona, Pennsylvania. The research presented in this paper analyzed 10 standard diesel buses and three hybrid buses. The VT-CPFM estimated fuel consumption levels on the Orange County bus cycle dynamometer test with an average error of 4.7%. The estimation error was less than 6% for all except two buses, with a maximum error of 10.66% for one hybrid bus. The VT-CPFM was also validated with on-road fuel consumption measurements that were derived by creating drive cycles from acceleration information; these measurements produced an average estimation error of 22%. These higher errors are probably attributable to the in-field drive cycles, which had to be constructed because none were available.
A power-based vehicle fuel consumption model, entitled the Virginia Tech Comprehensive Power-based Fuel Consumption Model (VT-CPFM) that was developed in an earlier publication is validated against in-field fuel consumption measurements. The study demonstrates that the VT-CPFMs calibrated using the EPA city and highway fuel economy ratings generally provide reliable fuel consumption estimates with a coefficient of determination in the range of 0.96. More importantly, both estimates and measurements produce very similar behavioral changes depending on engine load conditions. The VT-CPFMs are demonstrated to be easily calibrated using publically available data without the need to gather in-field instantaneous data.
The present work evaluates the impact of properties of four very common bio-fuels, viz. vegetable oil (cottonseed), or its derived (methyl ester) bio-diesel, or ethanol, or n-butanol, in blends of various proportions with diesel fuel, on the combustion and exhaust emissions of a fully instrumented, six-cylinder, four-stroke, heavy-duty direct injection (HDDI), ‘Mercedes-Benz’ bus diesel engine, bearing a waste-gate turbocharger with after-cooler, running under steady and transient conditions. Under steady-state operation, exhaust smoke, nitrogen oxides (NOx), carbon monoxide (CO), and total unburned hydrocarbons (HC) were measured and compared with those of the baseline operation (with neat diesel fuel) and among themselves. Fuel injection, combustion chamber pressure, and heat release rate (HRR) diagrams revealed interesting features of the combustion mechanisms. These results and the different physical and chemical properties of those bio-fuels are used to aid the interpretation of the observed engine behavior. As regards the transient engine operation, measurements for three accelerations tests were examined with the engine fueled on bio-diesel or n-butanol diesel fuel blends. The test bed was complemented with fast response instruments to capture the development of key engine and turbocharger variables, depicted in analytical diagrams, using ultra-fast response instrumentation for the instantaneous measurements of the exhaust NO and smoke opacity. Again, these results and the different physical and chemical properties of bio-fuels are used to aid the interpretation of the engine behavior. Finally, a comparison is made for the influence of bio-fuels properties (bio-diesel and n-butanol) on the NOx and smoke emissions between steady-state and transient operating conditions, under the recognition of the different non-fuel factors affecting the transient operating schedules.
Operating characteristics of conventional and hybrid electric buses were examined.
� Recovery of braking energy offers an excellent opportunity to improve fuel economy.
� Speed and altitude profiles of routes have dramatic impacts on the energy recovery.
� Capacity of the auxiliary power source has a dramatic impact on the energy recovery.
� Round-trip efficiency of the regenerative braking system was calculated to be 27%.
Due to increased public awareness on global climate change and other energy and environmental problems, a variety of strategies are being developed and used to reduce the energy consumption and environmental impact of roadway travel. In advanced traveler information systems, recent efforts have been made in developing a new navigation concept called “eco-routing,” which finds a route that requires the least amount of fuel and/or produces the least amount of emissions. This paper presents an eco-routing navigation system that determines the most eco-friendly route between a trip origin and a destination. It consists of the following four components: 1) a Dynamic Roadway Network database, which is a digital map of a roadway network that integrates historical and real-time traffic information from multiple data sources through an embedded data fusion algorithm; 2) an energy/emissions operational parameter set, which is a compilation of energy/emission factors for a variety of vehicle types under various roadway characteristics and traffic conditions; 3) a routing engine, which contains shortest path algorithms used for optimal route calculation; and 4) user interfaces that receive origin-destination inputs from users and display route maps to the users. Each of the system components and the system architecture are described. Example results are also presented to prove the validity of the eco-routing concept and to demonstrate the operability of the developed eco-routing navigation system. In addition, current limitations of the system and areas for future improvements are discussed.
This paper quantifies the system-wide impacts of implementing a dynamic eco-routing system, considering various levels of market penetration and levels of congestion in downtown Cleveland and Columbus, Ohio, USA. The study concludes that eco-routing systems can reduce network-wide fuel consumption and emission levels in most cases; the fuel savings over the networks range between 3.3% and 9.3% when compared to typical travel time minimization routing strategies. We demonstrate that the fuel savings achieved through eco-routing systems are sensitive to the network configuration and level of market penetration of the eco-routing system. The results also demonstrate that an eco-routing system typically reduces vehicle travel distance but not necessarily travel time. We also demonstrate that the configuration of the transportation network is a significant factor in defining the benefits of eco-routing systems. Specifically, eco-routing systems appear to produce larger fuel savings on grid networks compared to freeway corridor networks. The study also demonstrates that different vehicle types produce similar trends with regard to eco-routing strategies. Finally, the system-wide benefits of eco-routing generally increase with an increase in the level of the market penetration of the system.
In September 2011, the National Highway Traffic Safety Administration and U.S. Environmental Protection Agency promulgated the first-ever federal regulations mandating fuel economy improvements for heavy-duty commercial vehicles. While the performance-based approach to these rules offers familiarity and assurances of fuel economy improvements, it also has some well-known weaknesses. In this paper, we describe fuel economy technologies for the trucking sector, its economic structure, the details of the new fuel economy regulations, and the controversies they sparked. We then address issues raised in reviewing the accompanying regulatory impact analysis. Next, we highlight some flaws of this form of regulation and suggests a variety of alternative, more market-oriented approaches that might work better.
A vehicle predictive eco-cruise control system is developed that minimizes vehicle fuel consumption levels utilizing roadway topographic information. The predictive eco-cruise control system consists of three components: a fuel consumption model, a powertrain model, and an optimization algorithm. The developed system generates an optimal vehicle control plan using anticipated roadway grade information so that the vehicle can vary its speed within a preset speed window in a fuel-saving manner. The developed system is tested by simulating a vehicle trip on synthetic roadway profiles and compared to a conventional cruise control system performance. Finally, the potential benefits of the predictive eco-cruise control system are quantified for the entire United States. I. INTRODUCTION
Predictive Cruise Control (PCC) is a system that enhances and works in combination with the existing Conventional Cruise Control. Based on elevation information captured in a 3D map and a predictive algorithm, PCC allows the vehicle speed to vary around the cruise control set speed within a defined speed band in an effort to reduce fuel consumption. As fuel consumption is a major portion of a truck's life cycle costs (LCC) and cruise control is used extensively in the United States and Canada, PCC can significantly reduce the truck's LCC.
Existing automobile fuel consumption and emission models suffer from two major drawbacks; they produce a bang–bang control through the use of a linear power model and the calibration of model parameters is not possible using publicly available data thus necessitating in-laboratory or field data collection. This paper develops two fuel consumption models that overcome these two limitations. Specifically, the models do not produce a bang–bang control and are calibrated using US Environmental Protection Agency city and highway fuel economy ratings in addition to publicly available vehicle and roadway pavement parameters. The models are demonstrated to estimate vehicle fuel consumption rates consistent with in-field measurements. In addition the models estimate CO2 emissions that are highly correlated with field measurements.Highlights► The research develops two simple vehicle fuel consumption models. ► The models are calibrated using the city and highway fuel economy ratings. ► The models estimate fuel consumption rates consistent with in-field measurements. ► The proposed model estimates CO2 emissions within a 2% error range. ► The proposed model can be easily integrated within a traffic simulation framework.
Hybrid-electric transit buses offer potential benefits over conventional transit buses of comparable capacity, including reduced fuel consumption, reduced emissions, and the utilization of smaller engines. Emissions measurements were performed on a 1998 New Flyer 40-foot transit bus equipped with a Cummins ISB 5.9-L diesel engine, an Engelhard DPX catalyzed particulate filter, and an Allison series-drive system. Results were compared to a conventional-drive, diesel-powered bus that was equipped with an oxidation catalyst, and to a liquefied natural gas (LNG)-powered bus. Tests were performed according to the guidelines of SAE Recommended Practice J2711. On average, the oxides of nitrogen (NOx) emissions from the hybrid bus were reduced by 50%, compared to the conventional-drive diesel bus, and 10%, compared to the LNG bus. Particulate matter (PM) emissions from the catalyzed filter-equipped hybrid bus were reduced by 90%, relative to those of the conventional diesel bus, and were comparable to those of the LNG bus.
The Transportation Energy Data Book: Edition 18 is a statistical compendium prepared and;
published by Oak Ridge National Laboratory (ORNL) under contract with the Office of;
Transportation Technologies in the Department of Energy (DOE). Designed for use as a desk-top;
reference, the data book represents an assembly and display of statistics and information that;
characterize transportation activity, and presents data on other factors that influence transportation;
energy use. The purpose of this document is to present relevant statistical data in the form of tables;
and graphs.;
This edition of the Data Book has 11 chapters which focus on various aspects of the;
transportation industry. Chapter 1 focuses on petroleum; Chapter 2 - energy Chapter 3 - emissions;;
Chapter 4 - transportation and the economy; Chapter 5 - highway vehicles; Chapter 6 - Light;
vehicles; Chapter 7 - heavy vehicles; Chapter 8 - alternative fuel vehicles; Chapter 9 - fleet;
vehicles; Chapter 10 - household vehicles; and Chapter 11 - nonhighway modes. The sources used;
represent the latest available data.
The paper presents a simple vehicle dynamics model for estimating maximum vehicle acceleration levels based on a vehicle's tractive effort and aerodynamic, rolling, and grade resistance forces. In addition, typical model input parameters for different vehicle, pavement, and tire characteristics are presented. The model parameters are calibrated/validated against field data that were collected along the Smart Road test facility at Virginia Tech utilizing a truck and trailer for 10 weight-to-power configurations, ranging from 85 kg/kW to 169 kg/kW (140 lb/hp to 280 lb/hp). The model was found to predict vehicle speeds at the conclusion of the travel along the section to within 5 km/h (3.1 mi/hr) of field measurements, thus demonstrating the validity and applicability of the model.
Additional consumption of fuel in an intense traffic condition is inevitable. Excess fuel consumption may be avoided, if an optimal driving strategy is implemented subject to the surrounding condition of a vehicle and existing constraints. Development of an optimal driving strategy has been the subject of eco-driving. A model of optimal driving strategy has been developed and it has been applied for assessment of eco-driving rules. The model may be categorized as an optimal control and the objective function is minimization of fuel consumption in a given route. Vehicle speed and gear ratio are identified as control variables. The effect of working load has been considered according three engine running processes of Idle, part-load and wide open throttle. The model has then been applied to identify the optimal driving strategy of a vehicle in different traffic congestion based on eco-driving rules.
This chapter describes the application of Pontryagin’s Maximum Principle and Dynamic Programming for vehicle drivingwith minimum
fuel consumption. The focus is on minimum-fuel accelerations. For the fuel consumption modeling, a six-parameter polynomial
approximation is proposed. With the Maximum Principle, this consumption model yields optimal accelerations with a linearly
decreasing acceleration as a function of the velocity. This linear acceleration behavior is also observed in real traffic
situations by other researchers. Dynamic Programming is implemented with a backward recursion on a specially chosen distance
grid. This grid enables the calculation of realistic gear shifting behaviour during vehicle accelerations. Gear shifting dynamics
are taken into account.
The scarcity of known petroleum reserves will make renewable energy resources more attractive. The most feasible way to meet this growing demand is by utilizing alternative fuels. Biodiesel is defined as the monoalkyl esters of vegetable oils or animal fats. Biodiesel is the best candidate for diesel fuels in diesel engines. The biggest advantage that biodiesel has over gasoline and petroleum diesel is its environmental friendliness. Biodiesel burns similar to petroleum diesel as it concerns regulated pollutants. On the other hand, biodiesel probably has better efficiency than gasoline. One such fuel for compression-ignition engines that exhibit great potential is biodiesel. Diesel fuel can also be replaced by biodiesel made from vegetable oils. Biodiesel is now mainly being produced from soybean, rapeseed and palm oils. The higher heating values (HHVs) of biodiesels are relatively high. The HHVs of biodiesels (39–41 MJ/kg) are slightly lower than that of gasoline (46 MJ/kg), petrodiesel (43 MJ/kg) or petroleum (42 MJ/kg), but higher than coal (32–37 MJ/kg). Biodiesel has over double the price of petrodiesel. The major economic factor to consider for input costs of biodiesel production is the feedstock, which is about 80% of the total operating cost. The high price of biodiesel is in large part due to the high price of the feedstock. Economic benefits of a biodiesel industry would include value added to the feedstock, an increased number of rural manufacturing jobs, an increased income taxes and investments in plant and equipment. The production and utilization of biodiesel is facilitated firstly through the agricultural policy of subsidizing the cultivation of non-food crops. Secondly, biodiesel is exempt from the oil tax. The European Union accounted for nearly 89% of all biodiesel production worldwide in 2005. By 2010, the United States is expected to become the world's largest single biodiesel market, accounting for roughly 18% of world biodiesel consumption, followed by Germany.
Motorists typically select routes that minimize their travel time or generalized cost. This may entail traveling on longer but faster routes. This raises questions concerning whether traveling along a longer but faster route results in energy and/or air quality improvements. We investigate the impacts of route choice decisions on vehicle energy consumption and emission rates for different vehicle types using microscopic and macroscopic emission estimation tools. The results demonstrate that the faster highway route choice is not always the best from an environmental and energy consumption perspective. Specifically, significant improvements to energy and air quality can be achieved when motorists utilize a slower arterial route although they incur additional travel time. The study also demonstrates that macroscopic emission estimation tools (e.g., MOBILE6) can produce erroneous conclusions given that they ignore transient vehicle behavior along a route. The findings suggest that an emission- and energy-optimized traffic assignment can significantly improve emissions over the standard user equilibrium and system optimum assignment formulations. Finally, the study demonstrates that a small portion of the entire trip involves high engine-load conditions that produce significant increases in emissions; demonstrating that by minimizing high-emitting driving behavior, air quality can be improved significantly.
The aim of this paper is to carry out a comparative study with regard to energy consumption and greenhouse gas emissions, in respect of two types of engines with three different fuels. The fuels analysed are diesel, biodiesel 30% (B30) and compressed natural gas (CNG). The engines tested were a spark ignition engine (Otto cycle) and two compression ignition engines (Diesel cycle), the first fed with CNG and the last two with B30 and diesel. What is new about this study is its scope of application concerning refuse collection services in the city of Madrid. The tests were carried out on refuse trucks of the FCC Company along actual urban routes in the city of Madrid. Also taken into account were the energy input and the greenhouse gases emitted for each of the paths taken by the fuels analysed, from resource recovery to delivery to the vehicle tank.
The paper applies a framework for developing microscopic emission models (VT-Micro model version 2.0) for assessing the environmental impacts of transportation projects. The original VT-Micro model was developed using chassis dynamometer data on nine light duty vehicles. The VT-Micro model is expanded by including data from 60 light duty vehicles and trucks. Statistical clustering techniques are applied to group vehicles into homogenous categories. Specifically, classification and regression tree algorithms are utilized to classify the 60 vehicles into 5 LDV and 2 LDT categories. In addition, the framework accounts for temporal lags between vehicle operational variables and measured vehicle emissions. The VT-Micro model is validated by comparing against laboratory measurements with prediction errors within 17%.
The objective of VERSIT+ LD is to predict traffic stream emissions for light-duty vehicles in any particular traffic situation. With respect to hot running emissions, VERSIT+ LD consists of a set of statistical models for detailed vehicle categories that have been constructed using multiple linear regression analysis. The aim is to find empirical relationships between mean emission factors, including confidence intervals, and a limited number of speed–time profile and vehicle related variables. VERSIT+ is a versatile model that has already been used in different projects at different geographical levels. Compared to COPERT IV, the VERSIT+ average speed algorithms provide increased accuracy with respect to the prediction of emissions in specific traffic situations.
Bio-fuels are important because they replace petroleum fuels. A number of environmental and economic benefits are claimed for bio-fuels. Bio-ethanol is by far the most widely used bio-fuel for transportation worldwide. Production of bio-ethanol from biomass is one way to reduce both consumption of crude oil and environmental pollution. Using bio-ethanol blended gasoline fuel for automobiles can significantly reduce petroleum use and exhaust greenhouse gas emission. Bio-ethanol can be produced from different kinds of raw materials. These raw materials are classified into three categories of agricultural raw materials: simple sugars, starch and lignocellulose. Bio-ethanol from sugar cane, produced under the proper conditions, is essentially a clean fuel and has several clear advantages over petroleum-derived gasoline in reducing greenhouse gas emissions and improving air quality in metropolitan areas. Conversion technologies for producing bio-ethanol from cellulosic biomass resources such as forest materials, agricultural residues and urban wastes are under development and have not yet been demonstrated commercially.
The actions individuals can take to mitigate climate change are, in the aggregate, significant. Mobilizing individuals to respond personally to climate change, therefore, must be a complementary approach to a nation's climate change strategy. One action item overlooked in the United States has been changing driver behavior or style such that eco-driving becomes the norm rather than the exception. Evidence to date indicates that eco-driving can reduce fuel consumption by 10%, on average and over time, thereby reducing CO2 emissions from driving by an equivalent percentage. A sophisticated, multi-dimensional campaign, going well beyond what has been attempted thus far, will be required to achieve such savings on a large scale, however, involving education (especially involving the use of feedback devices), regulation, fiscal incentives, and social norm reinforcement.
Tumor lysis syndrome is a well-described, serious complication of chemotherapy administered to treat malignancies. However, a very rare event resulting in the spontaneous necrosis of a tumor prior to therapy can also occur, which is termed spontaneous tumor lysis syndrome (STLS). We present a case of a 27-year-old male who presented to the hospital with epistaxis, dyspnea, and cervical lymphadenopathy. Laboratory findings included progressive pancytopenia, hyperuricemia, and acute renal failure. Bone marrow biopsy showed a T cell lymphoid neoplasm that had entirely infiltrated the marrow stroma. The patient was diagnosed with STLS in the setting of a T cell lymphoma with bone marrow infiltration. The patient was immediately treated with a blood transfusion and hemodialysis. After this urgent treatment, the patient's pancytopenia resolved and the lymphadenopathy disappeared spontaneously. One month post-treatment, the patient's cervical lymphadenopathy recurred and peripheral T cell lymphoma, not otherwise specified, was confirmed. STLS has previously been reported, however, most known cases of STLS did not show a decreased tumor burden resulting from massive tumor cell death. We present a rare case of STLS with resolution of pancytopenia and disappearance of lymphadenopathy in a patient with peripheral T cell lymphoma not otherwise specified.
Heavy-duty diesel vehicle (HDDV) operations are a major source of pollutant emissions in major metropolitan areas. Accurate estimation of heavy-duty diesel vehicle emissions is essential in air quality planning efforts because highway and non-road heavy-duty diesel emissions account for a significant fraction of the oxides of nitrogen (NOx) and particulate matter (PM) emissions inventories. Yet, major modeling deficiencies in the current MOBILE6 modeling approach for heavy-duty diesel vehicles have been widely recognized for more than ten years. While the most recent MOBILE6.2 model integrates marginal improvements to various internal conversion and correction factors, fundamental flaws inherent in the modeling approach still remain. The major effort of this research is to develop a new heavy-duty vehicle load-based modal emission rate model that overcomes some of the limitations of existing models and emission rates prediction methods. This model is part of the proposed Heavy-Duty Diesel Vehicle Modal Emission Modeling (HDDV-MEM) which was developed by Georgia Institute of Technology. HDDV-MEM first predicts second-by-second engine power demand as a function of vehicle operating conditions and then applies brake-specific emission rates to these activity predictions. To provide better estimates of microscopic level, this modeling approach is designed to predict second-by-second emissions from onroad vehicle operations. This research statistically analyzes the database provided by EPA and yields a model for prediction emissions at microscopic level based on engine power demand and driving mode. Research results will enhance the explaining ability of engine power demand on emissions and the importance of simulating engine power in real world applications. The modeling approach provides a significant improvement in HDDV emissions modeling compared to the current average speed cycle-based emissions models. Ph.D. Committee Chair: Guensler, Randall; Committee Member: Hunter, Michael; Committee Member: Meyer, Michael; Committee Member: Ogle, Jennifer H.; Committee Member: Rodgers, Michael
Information about in-use emissions from diesel engines remains a critical issue for inventory development and policy design. Toward that end, we have developed and verified the first mobile laboratory that measures on-road or real-world emissions from engines at the quality level specified in the U.S. Congress Code of Federal Regulations. This unique mobile laboratory provides information on integrated and modal regulated gaseous emission rates and integrated emission rates for speciated volatile and semivolatile organic compounds and particulate matter during real-world operation. Total emissions are captured and collected from the HDD vehicle that is pulling the mobile laboratory. While primarily intended to accumulate data from HDD vehicles, it may also be used to measure emission rates from stationary diesel sources such as back-up generators. This paper describes the development of the mobile laboratory, its measurement capabilities, and the verification process and provides the first data on total capture gaseous on-road emission measurements following the California Air Resources Board (ARB) 4-mode driving cycle, the hot urban dynamometer driving schedule (UDDS), the modified 5-mode cycle, and a 53.2-mi highway chase experiment. NOx mass emission rates (g mi(-1)) for the ARB 4-mode driving cycle, the hot UDDS driving cycle, and the chase experimentwerefoundto exceed current emission factor estimates for the engine type tested by approximately 50%. It was determined that congested traffic flow as well as "off-Federal Test Procedure cycle" emissions can lead to significant increases in per mile NOx emission rates for HDD vehicles.
Eco-cruise control: feasibility and initial testing
- K Ahn
- H Rakha
- K Moran
Ahn, K., Rakha, H., Moran, K., 2011. Eco-cruise control: feasibility and initial testing. In: Transportation Research Board 90th Annual Meeting. No. 11-1031.
User's guide to MOBILE 6.1 and MOBILE 6.2. Assessment and Standard Division, Office of Transportation and Air Quality Recent trends in global production and utilization of bio-ethanol fuel
- A Arbor
Arbor, A., 2003. User's guide to MOBILE 6.1 and MOBILE 6.2. Assessment and Standard Division, Office of Transportation and Air Quality, USEPA, Mich.
Balat, M., Balat, H., 2009. Recent trends in global production and utilization of bio-ethanol fuel. Appl. Energy 86 (11), 2273–2282.
Eco-Driving-Changing Truck Driver Behavior to Achieve Long-Term Sustainability Results. Master's thesis
- K Dzenisiuk
Dzenisiuk, K., 2012. Eco-Driving-Changing Truck Driver Behavior to Achieve Long-Term Sustainability Results. Master's thesis, Copenhagen Business School,
2012.
Inventory of US Greenhouse Gas Emissions and Sinks: 1990–2013
EPA, 2015. Inventory of US Greenhouse Gas Emissions and Sinks: 1990–2013. Tech. rep., EPA 430-R-15-004.
Motor truck engineering handbook
- J W Fitch
Fitch, J.W., 1994. Motor truck engineering handbook. Technology 2004, 03–08.
PHEMuser Guide for Version 10
- S Hausberger
- M Rexeis
- M Zallinger
- R Luz
Hausberger, S., Rexeis, M., Zallinger, M., Luz, R., 2010. PHEMuser Guide for Version 10. TUG/FVT Report, 1-57.
Predictive eco-cruise control: algorithm and potential benefits
- S Park
- H Rakha
- K Ahn
- K Moran
Park, S., Rakha, H., Ahn, K., Moran, K., 2011. Predictive eco-cruise control: algorithm and potential benefits. In: 2011 IEEE Forum on Integrated and
Sustainable Transportation System (FISTS). IEEE, pp. 394–399.
Applications for the environment: realtime information synthesis (AERIS) benefit-cost analysis. Prepared by United States Department of Transportation, Federal Highway Administration Office
- E Pindilli
Pindilli, E., 2012. Applications for the environment: realtime information synthesis (AERIS) benefit-cost analysis. Prepared by United States Department of
Transportation, Federal Highway Administration Office.
Final Rules: Greenhouse Gas Emissions Standards and Fuel Efficiency Standards for Medium and Heavy-duty Engines and Vehicles
- U S Epa
- Nhtsa
U.S. EPA, NHTSA, 2011. Final Rules: Greenhouse Gas Emissions Standards and Fuel Efficiency Standards for Medium and Heavy-duty Engines and Vehicles.
Tech. rep., Federal Register 76: 57106.
User's guide to MOBILE 6.1 and MOBILE 6.2. Assessment and Standard Division
- A Arbor
- Mich Usepa
- M Balat
- H Balat
Arbor, A., 2003. User's guide to MOBILE 6.1 and MOBILE 6.2. Assessment and Standard Division, Office of Transportation and Air Quality, USEPA, Mich.
Balat, M., Balat, H., 2009. Recent trends in global production and utilization of bio-ethanol fuel. Appl. Energy 86 (11), 2273-2282.