Catalog of selected heavy duty transport energy management models

A catalog of energy management models for heavy duty transport systems powered by diesel engines is presented. The catalog results from a literature survey, supplemented by telephone interviews and mailed questionnaires to discover the major computer models currently used in the transportation industry in the following categories: heavy duty transport systems, which consist of highway (vehicle simulation), marine (ship simulation), rail (locomotive simulation), and pipeline (pumping station simulation); and heavy duty diesel engines, which involve models that match the intake/exhaust system to the engine, fuel efficiency, emissions, combustion chamber shape, fuel injection system, heat transfer, intake/exhaust system, operating performance, and waste heat utilization devices, i.e., turbocharger, bottoming cycle

Supervisory control for emission compliance of heavy-duty vehicles

Heavy freight trucks globally contribute to a significant proportion of transport-related air pollution. The dominant air pollutants from heavy freight trucks with diesel engine and exhaust aftertreatment system (EATS) are CO2, hydrocarbons (HC), CO, particulate matter (PM), NOX (NO and NO2), and NH3. The greenhouse gas emission legislation limits the amount of CO2 emission, and Euro VI emission legislation limits the other dominant air pollutants. Emission legislation is gradually becoming more and more stringent to reach the long term goal of near-zero-emission. Several parties are working together to reduce the emissions, keeping both the short and long term goal in mind. Any step which results in ICE downsizing contributes to the reduction of all dominant emissions. But, with size and type of the ICE decided, there is a trade-off between NOX emission and other emissions: reduced NOX emission means reduced fuel efficiency (i.e. increased CO2, PM, and HC emissions). It is a challenge to fulfil the current emission legislation—especially real-driving NOX emission legislation—with existing control functionalities in the engine management system (EMS). However, better control of NOX emission is possible by exploiting predictive driving information and considering the coupling between the engine system and EATS. This work pursues this idea and concludes that fulfilling real-driving NOX emission legislation is possible, considering the coupling between the engine system and EATS while using predictive information. The work provides a mathematical formulation of the concept and then develops, evaluates, and implements an engine-EATS supervisor which optimizes total fuel consumption and fulfils both the world harmonized transient cycle (WHTC) based and real-driving NOX emission legislation. The developed supervisor is a distributed economic nonlinear model predictive controller (E-NMPC). This work develops and analyzes two different versions of the distributed E-NMPC based supervisory control algorithm. The more efficient one of the two is again compared for three variants. Considering the computation time of the three algorithms and processing speed of the existing EMS, one algorithm is selected for implementation. The supervisor performs much better compared to a baseline controller (optimized offline). Simulation results show that the supervisory controller has 1.7% less total fuel consumption and 88.4% less NH3 slip, compared to the baseline controller, to achieve the same real-driving NOX emission

Development of Machine Learning based approach to predict fuel consumption and maintenance cost of Heavy-Duty Vehicles using diesel and alternative fuels

One of the major contributors of human-made greenhouse gases (GHG) namely carbon dioxide (CO2), methane (CH4), and nitrous oxide (NOX) in the transportation sector and heavy-duty vehicles (HDV) contributing to about 27% of the overall fraction. In addition to the rapid increase in global temperature, airborne pollutants from diesel vehicles also present a risk to human health. Even a small improvement that could potentially drive energy savings to the century-old mature diesel technology could yield a significant impact on minimizing greenhouse gas emissions. With the increasing focus on reducing emissions and operating costs, there is a need for efficient and effective methods to predict fuel consumption, maintenance costs, and total cost of ownership for heavy-duty vehicles. Every improvement so achieved in this direction is a direct contributor to driving the reduction in the total cost of ownership for a fleet owner, thereby bringing economic prosperity and reducing oil imports for the economy. Motivated by these crucial goals, the present research considers integrating data-driven techniques using machine learning algorithms on the historical data collected from medium- and heavy-duty vehicles. The primary motivation for this research is to address the challenges faced by the medium- and heavy-duty transportation industry in reducing emissions and operating costs. The development of a machine learning-based approach can provide a more accurate and reliable prediction of fuel consumption and maintenance costs for medium- and heavy-duty vehicles. This, in turn, can help fleet owners and operators to make informed decisions related to fuel type, route planning, and vehicle maintenance, leading to reduced emissions and lower operating costs. Artificial Intelligence (AI) in the automotive industry has witnessed massive growth in the last few years. Heavy-duty transportation research and commercial fleets are adopting machine learning (ML) techniques for applications such as autonomous driving, fuel economy/emissions, predictive maintenance, etc. However, to perform well, modern AI methods require a large amount of high-quality, diverse, and well-balanced data, something which is still not widely available in the automotive industry, especially in the division of medium- and heavy-duty trucks. The research methodology involves the collection of data at the West Virginia University (WVU) Center for Alternative Fuels, Engines, and Emissions (CAFEE) lab in collaboration with fleet management companies operating medium- and heavy-duty vehicles on diesel and alternative fuels, including compressed natural gas, liquefied propane gas, hydrogen fuel cells, and electric vehicles. The data collected is used to develop machine learning models that can accurately predict fuel consumption and maintenance costs based on various parameters such as vehicle weight, speed, route, fuel type, and engine type. The expected outcomes of this research include 1) the development of a neural network model 3 that can accurately predict the fuel consumed by a vehicle per trip given the parameters such as vehicle speed, engine speed, and engine load, and 2) the development of machine learning models for estimating the average cost-per-mile based on the historical maintenance data of goods movement trucks, delivery trucks, school buses, transit buses, refuse trucks, and vocational trucks using fuels such as diesel, natural gas, and propane. Due to large variations in maintenance data for vehicles performing various activities and using different fuel types, the regular machine learning or ensemble models do not generalize well. Hence, a mixed-effect random forest (MERF) is developed to capture the fixed and random effects that occur due to varying duty-cycle of vocational heavy-duty trucks that perform different tasks. The developed model helps in predicting the average maintenance cost given the vocation, fuel type, and region of operation, making it easy for fleet companies to make procurement decisions based on their requirement and total cost of ownership. Both the models can provide insights into the impact of various parameters and route planning on the total cost of ownership affected by the fuel cost and the maintenance and repairs cost. In conclusion, the development of a machine learning-based approach can provide a reliable and efficient solution to predict fuel consumption and maintenance costs impacting the total cost of ownership for heavy-duty vehicles. This, in turn, can help the transportation industry reduce emissions and operating costs, contributing to a more sustainable and efficient transportation system. These models can be optimized with more training data and deployed in a real-time environment such as cloud service or an onboard vehicle system as per the requirement of companies

Effects of artificial neural network speed-based inputs on heavy-duty vehicle emissions prediction

The PM split study was performed in Southern California on thirty-four heavy-duty diesel vehicles using the West Virginia University Transportable Heavy-Duty Vehicle Emissions Testing Laboratories to gather emissions data of these vehicles. The data obtained from six vehicles in the 1985--2001 model year and 33,000--80,000 lb weight range exercised through three different cycles were selected in this thesis. To predict the instantaneous levels of oxides of nitrogen (NOx), carbon dioxide (CO2), hydrocarbons (HC) and carbon monoxide (CO), an Artificial Neural Network (ANN) was used. Axle speed, torque, their rates of change over different time periods and two other variables as a function of axle speed were defined as the inputs for the neural network. Also, each emissions species was considered individually as the output of the ANN. The ANN was trained on the Highway cycle and applied to the City/Suburban Heavy Vehicle Route (CSHVR) and Urban Dynamometer Driving Schedule (UDDS) with four different sets of inputs to predict the emissions for these vehicles. The research showed an excellent emissions prediction for the neural networks that were trained with only eight inputs (speed, torque, their first and second derivatives, and two variables of Diff. and Spd related to the speed pattern over the last 150 seconds). The Diff variable provided a measure of the variability of speed over the last 150 seconds of operation. This variable was able to create a moving speed-dependant window, which was used as an input for the neural networks. The results showed an average accuracy of 0.97 percent for CO2, 0.89 percent for NOx, 0.70 for CO and 0.48 percent for HC over the course of the CSHVR, Highway and UDDS

Spatial and Temporal Investigation of Real World Crosswind Effects on Transient Aerodynamic Drag Losses in Heavy Duty Truck Trailers in the US

Decreasing truck fuel usage and climate change gas production is of national and global importance. This study focuses on large, heavy-duty on-road tractor trailer combinations because of their impact in terms of fuel consumption levels, emissions, and their dominance in freight transportation in the United States, which offers substantial potential to improve efficiency of the transportation sector and reduce emissions. The US Department of Energy completed a study of this topic in 2009, and the EPA and NHTSA are both engaged in regulating truck efficiency. The Energy Information Administration (EIA) reported that more than 50 percent of the total diesel consumed was for transportation and this percentage will increase. With about 65 percent of the total engine-out energy consumed by a typical heavy-duty tractor trailer being spent on overcoming aerodynamic drag at highway speeds (55mph in the USA), improvements to aerodynamic performance offers a substantial avenue for reduction in fuel usage and emissions. Besides being directly related to fuel consumption, emissions, maximum speed and acceleration, aerodynamic phenomena also influence the stability characteristics of road vehicles, and their response to crosswinds. Crosswinds from any directions will affect the drag losses and will cause a significant change in pressure distribution along the truck body. The main objective of this research is to provide a better understanding of the influence of crosswinds on the aerodynamic performance of heavy-duty tractor trailers in the United States.;A model to calculate on-road crosswinds for any temporal and spatial conditions from time-varying weather data, vehicle position and road data was developed. This transient model combined with drag data obtained from experimental, steady-state wind tunnel testing and numerical simulations for various tractor trailer configurations, the transient nature of coefficient of drag due to on-road crosswind conditions (from the model) was analyzed. Variations in yaw angle of up to 17 degrees were observed in some cases where the average yaw angle was recorded at only 3 degrees. Relationships between wind speed, yaw angle, drag and overall truck efficiency were clearly established. The research statistically measured the interaction between aerodynamic add-on devices, on-road crosswinds, and drag reduction efficiency. A region-based and time-based analysis was conducted to provide a better understanding of the aerodynamic performance of a baseline tractor-trailer configuration and aerodynamic add on devices. In several cases, the coefficient of drag varied as much as 60% on the routes analyzed and reductions in aerodynamic drag force up to 25% could realized by using the appropriate aerodynamic configurations. The application of these results will improve the estimation accuracy in fuel, emissions prediction models by allowing temporally and spatially disaggregated data input parameters. Finally, the study presented the different methods in which coefficient of drag is estimated and how these differences could play a role in misleading information about the aerodynamic characteristics of a tractor trailer

Reduced Fuel Emissions through Connected Vehicles and Truck Platooning

Vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication enable the sharing, in real time, of vehicular locations and speeds with other vehicles, traffic signals, and traffic control centers. This shared information can help traffic to better traverse intersections, road segments, and congested neighborhoods, thereby reducing travel times, increasing driver safety, generating data for traffic planning, and reducing vehicular pollution. This study, which focuses on vehicular pollution, used an analysis of data from NREL, BTS, and the EPA to determine that the widespread use of V2V-based truck platooning—the convoying of trucks in close proximity to one another so as to reduce air drag across the convoy—could eliminate 37.9 million metric tons of CO2 emissions between 2022 and 2026

Fuel Efficient Connected Cruise Control for Heavy-Duty Trucks in Real Traffic

In this paper, we present a systematic approach for fuel-economy optimization of a connected automated truck that utilizes motion information from multiple vehicles ahead via vehicle-to-vehicle (V2V) communication. Position and velocity data collected from a chain of human-driven vehicles are utilized to design a connected cruise controller that smoothly responds to traffic perturbations while maximizing energy efficiency. The proposed design is evaluated using a high-fidelity truck model and the robustness of the design is validated on real traffic data sets. It is shown that optimally utilizing V2V connectivity leads to around 10% fuel economy improvements compared to the best nonconnected design

Fuel Efficient Connected Cruise Control for Heavy-Duty Trucks in Real Traffic

In this paper, we present a systematic approach for fuel-economy optimization of a connected automated truck that utilizes motion information from multiple vehicles ahead via vehicle-to-vehicle (V2V) communication. Position and velocity data collected from a chain of human-driven vehicles are utilized to design a connected cruise controller that smoothly responds to traffic perturbations while maximizing energy efficiency. The proposed design is evaluated using a high-fidelity truck model and the robustness of the design is validated on real traffic data sets. It is shown that optimally utilizing V2V connectivity leads to around 10% fuel economy improvements compared to the best nonconnected design

Topological analysis of powertrains for refusecollecting vehicles based on real routes – Part I: Hybrid hydraulic powertrain

In this two-part paper, a topological analysis of powertrains for refuse-collecting vehicles (RCVs) based on the simulation of different architectures (internal combustion engine, hybrid electric, and hybrid hydraulic) on real routes is proposed. In this first part, a characterization of a standard route is performed, analyzing the average power consumption and the most frequent working points of an internal combustion engine (ICE) in real routes. This information is used to define alternative powertrain architectures. A hybrid hydraulic powertrain architecture is proposed and modelled. The proposed powertrain model is executed using two different control algorithms, with and without predictive strategies, with data obtained from real routes. A calculation engine (an algorithm which runs the vehicle models on real routes), is presented and used for simulations. This calculation engine has been specifically designed to analyze if the different alternative powertrain delivers the same performance of the original ICE. Finally, the overall performance of the different architectures and control strategies are summarized into a fuel and energy consumption table, which will be used in the second part of this paper to compare with the different architectures based on hybrid electric powertrain. The overall performance of the different architectures indicates that the use of a hybrid hydraulic powertrain with simple control laws can reduce the fuel consumption up to a 14 %.Postprint (author's final draft

Advanced propulsion system for hybrid vehicles

A number of hybrid propulsion systems were evaluated for application in several different vehicle sizes. A conceptual design was prepared for the most promising configuration. Various system configurations were parametrically evaluated and compared, design tradeoffs performed, and a conceptual design produced. Fifteen vehicle/propulsion systems concepts were parametrically evaluated to select two systems and one vehicle for detailed design tradeoff studies. A single hybrid propulsion system concept and vehicle (five passenger family sedan)were selected for optimization based on the results of the tradeoff studies. The final propulsion system consists of a 65 kW spark-ignition heat engine, a mechanical continuously variable traction transmission, a 20 kW permanent magnet axial-gap traction motor, a variable frequency inverter, a 386 kg lead-acid improved state-of-the-art battery, and a transaxle. The system was configured with a parallel power path between the heat engine and battery. It has two automatic operational modes: electric mode and heat engine mode. Power is always shared between the heat engine and battery during acceleration periods. In both modes, regenerative braking energy is absorbed by the battery