Large Scale Duty Cycle (LSDC) Project: Tractive Energy Analysis Methodology and Results from Long-Haul Truck Drive Cycle Evaluations

This report addresses the approach that will be used in the Large Scale Duty Cycle (LSDC) project to evaluate the fuel savings potential of various truck efficiency technologies. The methods and equations used for performing the tractive energy evaluations are presented and the calculation approach is described. Several representative results for individual duty cycle segments are presented to demonstrate the approach and the significance of this analysis for the project. The report is divided into four sections, including an initial brief overview of the LSDC project and its current status. In the second section of the report, the concepts that form the basis of the analysis are presented through a discussion of basic principles pertaining to tractive energy and the role of tractive energy in relation to other losses on the vehicle. In the third section, the approach used for the analysis is formalized and the equations used in the analysis are presented. In the fourth section, results from the analysis for a set of individual duty cycle measurements are presented and different types of drive cycles are discussed relative to the fuel savings potential that specific technologies could bring if these drive cycles were representative of the use of a given vehicle or trucking application. Additionally, the calculation of vehicle mass from measured torque and speed data is presented and the accuracy of the approach is demonstrated

Self-powered Heating: Efficiency Analysis

Conventional fuel-fired heating devices such as furnaces, boilers, and water heaters have fuel efficiency less than 100% on the basis of higher heating value. They also require electricity from the electric grid to power parasitic loads such as blowers, pumps, fans, and ignitors. The primary energy efficiency of the device accounts for both fuel used on-site and primary energy used off-site to produce electric power used by the device. This work compares conventional fuel-fired heating devices to two types of self-powered devices. A self-powered device (SPD) integrates a power cycle onboard to eliminate consumption of grid electricity. We assume that all heat rejected by the onboard power cycle is added to the process fluid, so that, compared with a conventional device, the same amount of heat is provided to the process fluid and the same amount of fuel is consumed, but grid electricity consumption is eliminated. The first SPD type is the basic one: exactly the electricity required is generated. The second type considered is the SPD with heat pump (SPD-HP), in which the power cycle generates more electricity than needed for parasitic loads, and the excess electricity is used to power a heat pump. The heat pump extracts additional heat from the ambient to boost efficiency. Both SPD and SPD-HP self-consume all the generated electricity, in contrast to combined heat and power (CHP) systems that export electricity. In this work, equations are derived to express the efficiency of three classes of heating devices: conventional (consuming grid electricity), self-powered (consuming no grid electricity), and self-powered with heat pump. The efficiency of each is derived as a function of up to six factors: (1) the fraction of combustion heat captured, (2) the rate of parasitic power consumption, (3) the fraction of electric energy dissipated as useful heat, (4) the power cycle conversion efficiency, (5) the grid efficiency, when applicable, and (6) the heat pump COP, when applicable. Scenarios are identified in which it is possible to achieve efficiency greater than 100% on a higher heating value basis. Plausible configurations using existing technology options are outlined

Medium Truck Duty Cycle Data from Real-World Driving Environments: Final Report

Since the early part of the 20th century, the US trucking industry has provided a safe and economical means of moving commodities across the country. At present, nearly 80% of US domestic freight movement involves the use of trucks. The US Department of Energy (DOE) is spearheading a number of research efforts to improve heavy vehicle fuel efficiencies. This includes research in engine technologies (including hybrid and fuel cell technologies), lightweight materials, advanced fuels, and parasitic loss reductions. In addition, DOE is developing advanced tools and models to support heavy vehicle research and is leading the 21st Century Truck Partnership and the SuperTruck development effort. Both of these efforts have the common goal of decreasing the fuel consumption of heavy vehicles. In the case of SuperTruck, a goal of improving the overall freight efficiency of a combination tractor-trailer has been established. This Medium Truck Duty Cycle (MTDC) project is a critical element in DOE s vision for improved heavy vehicle energy efficiency; it is unique in that there is no other existing national database of characteristic duty cycles for medium trucks based on collecting data from Class 6 and 7 vehicles. It involves the collection of real-world data on medium trucks for various situational characteristics (e.g., rural/urban, freeway/arterial, congested/free-flowing, good/bad weather) and looks at the unique nature of medium trucks drive cycles (stop-and-go delivery, power takeoff, idle time, short-radius trips). This research provides a rich source of data that can contribute to the development of new tools for FE and modeling, provide DOE a sound basis upon which to make technology investment decisions, and provide a national archive of real-world-based medium-truck operational data to support energy efficiency research. The MTDC project involved a two-part field operational test (FOT). For the Part-1 FOT, three vehicles each from two vocations (urban transit and dry-box delivery) were instrumented for the collection of one year of operational data. The Part-2 FOT involved the towing and recovery and utility vocations for a second year of data collection. The vehicles that participated in the MTDC project did so through gratis partnerships in return for early access to the results of this study. Partnerships such as these are critical to FOTs in which real-world data is being collected. In Part 1 of the project, Oak Ridge National Laboratory (ORNL) established partnerships with the H.T. Hackney Company (HTH), one of the largest wholesale distributors in the country, distributing products to 21 states; and with Knoxville Area Transit (KAT), the city of Knoxville s transit system, which operates across Knoxville and parts of Knox County. These partnerships and agreements provided ORNL access to three Class-7 day-cab tractors that regularly haul 28 ft pup trailers (HTH) and three Class-7 buses for the collection of duty cycle data. In addition, ORNL collaborated with the Federal Motor Carrier Safety Administration (FMCSA) to determine if there were possible synergies between this duty cycle data collection effort and FMCSA s need to learn more about the operation and duty cycles of medium trucks. FMCSA s primary interest was in collecting safety data relative to the driver, carrier, and vehicle. In Part 2 of the project, ORNL partnered with the Knoxville Utilities Board, which made available three Class-8 trucks. Fountain City Wrecker Service was also a Part 2 partner, providing three Class-6 rollback trucks. In order to collect the duty cycle and safety-related data, ORNL developed a data acquisition system (DAS) that was placed on each test vehicle. Each signal recorded in this FOT was collected by means of one of the instruments incorporated into each DAS. Other signals were obtained directly from the vehicle s J1939 and J1708 data buses. A VBOX II Lite collected information available from a global positioning system (GPS), including speed, acceleration, and spatial location information at a rate of 5 Hz for the Part 1 FOT. For the Part 2 FOT, this information was obtained from DAS-based GPS instrumentation. The Air-Weigh LoadMaxx, a self-weighing system that determines the vehicle s gross weight by means of pressure transducers, was used to collect vehicle payload information for the combination, urban transit, and towing and recovery vehicles. A cellular modem, the Raven X EVDO V4221, facilitated the communication between the eDAQ-lite (the data collection engine of the system) and the user. The modem functioned as a wireless gateway, allowing data retrievals and system checks to be performed remotely. Also, in partnership with FMCSA, two additional safety sensors were installed on the combination vehicles: the MGM e-Stroke brake monitoring system and the Tire SafeGuard tire pressure monitoring system. All of these sensors posted data to the J1939 data bus, enabling the signals to be read withou..

Material Selection and Sizing of a Thermoelectric Generator (TEG) for Power Generation in a Self-Powered Heating System

By employing the high temperature heat source to directly generate the electricity needed to power auxiliary systems in a natural gas furnace, boiler or hot water heater, a “self-powered” heating system can provide several benefits. Compared with a traditional furnace, boiler or hot water heater, when overall fuel utilization is kept constant, the self-powered system will have a higher primary energy efficiency, lower operating costs, and dramatically improved building safety and resilience during electric grid outages. Furthermore, a self-powered heating system only has a single utility connection – natural gas, without an electric connection – thus simplifying installation. A thermoelectric generator can be used for direct energy conversion of thermal energy to electricity with no moving parts, which offers a very simple means to provide power for the self-powered heating system, and the operation is without noise or vibration and can thus provide a very long system life. This paper provides an analysis focused on materials selection and the thermal power requirements for a thermoelectric generator (TEG) for use in a self-powered heating system. The dimensionless figure of merit for thermoelectric materials, zT, is used to estimate the optimal efficiency that can be achieved with a TEG to produce the electric power required in such an application. Comparisons of the predicted efficiency, the required heat transfer rate to the TEG and the heat transfer area needed for sustained operation under thermal conditions relevant to the self-powered heating application are made for several potential thermoelectric materials. This analysis was used to develop system requirements for a self-powered hot water heater using a TEG for electric power generation

Large Scale Duty Cycle (LSDC) Project: Tractive Energy Analysis Methodology and Results from Long-Haul Truck Drive Cycle Evaluations

This report addresses the approach that will be used in the Large Scale Duty Cycle (LSDC) project to evaluate the fuel savings potential of various truck efficiency technologies. The methods and equations used for performing the tractive energy evaluations are presented and the calculation approach is described. Several representative results for individual duty cycle segments are presented to demonstrate the approach and the significance of this analysis for the project. The report is divided into four sections, including an initial brief overview of the LSDC project and its current status. In the second section of the report, the concepts that form the basis of the analysis are presented through a discussion of basic principles pertaining to tractive energy and the role of tractive energy in relation to other losses on the vehicle. In the third section, the approach used for the analysis is formalized and the equations used in the analysis are presented. In the fourth section, results from the analysis for a set of individual duty cycle measurements are presented and different types of drive cycles are discussed relative to the fuel savings potential that specific technologies could bring if these drive cycles were representative of the use of a given vehicle or trucking application. Additionally, the calculation of vehicle mass from measured torque and speed data is presented and the accuracy of the approach is demonstrated

A Novel Deep Reinforcement Learning Approach to Traffic Signal Control with Connected Vehicles

The advent of connected vehicle (CV) technology offers new possibilities for a revolution in future transportation systems. With the availability of real-time traffic data from CVs, it is possible to more effectively optimize traffic signals to reduce congestion, increase fuel efficiency, and enhance road safety. The success of CV-based signal control depends on an accurate and computationally efficient model that accounts for the stochastic and nonlinear nature of the traffic flow. Without the necessity of prior knowledge of the traffic system’s model architecture, reinforcement learning (RL) is a promising tool to acquire the control policy through observing the transition of the traffic states. In this paper, we propose a novel data-driven traffic signal control method that leverages the latest in deep learning and reinforcement learning techniques. By incorporating a compressed representation of the traffic states, the proposed method overcomes the limitations of the existing methods in defining the action space to include more practical and flexible signal phases. The simulation results demonstrate the convergence and robust performance of the proposed method against several existing benchmark methods in terms of average vehicle speeds, queue length, wait time, and traffic density

Medium Truck Duty Cycle Data from Real-World Driving Environments: Final Report

Since the early part of the 20th century, the US trucking industry has provided a safe and economical means of moving commodities across the country. At present, nearly 80% of US domestic freight movement involves the use of trucks. The US Department of Energy (DOE) is spearheading a number of research efforts to improve heavy vehicle fuel efficiencies. This includes research in engine technologies (including hybrid and fuel cell technologies), lightweight materials, advanced fuels, and parasitic loss reductions. In addition, DOE is developing advanced tools and models to support heavy vehicle research and is leading the 21st Century Truck Partnership and the SuperTruck development effort. Both of these efforts have the common goal of decreasing the fuel consumption of heavy vehicles. In the case of SuperTruck, a goal of improving the overall freight efficiency of a combination tractor-trailer has been established. This Medium Truck Duty Cycle (MTDC) project is a critical element in DOE s vision for improved heavy vehicle energy efficiency; it is unique in that there is no other existing national database of characteristic duty cycles for medium trucks based on collecting data from Class 6 and 7 vehicles. It involves the collection of real-world data on medium trucks for various situational characteristics (e.g., rural/urban, freeway/arterial, congested/free-flowing, good/bad weather) and looks at the unique nature of medium trucks drive cycles (stop-and-go delivery, power takeoff, idle time, short-radius trips). This research provides a rich source of data that can contribute to the development of new tools for FE and modeling, provide DOE a sound basis upon which to make technology investment decisions, and provide a national archive of real-world-based medium-truck operational data to support energy efficiency research. The MTDC project involved a two-part field operational test (FOT). For the Part-1 FOT, three vehicles each from two vocations (urban transit and dry-box delivery) were instrumented for the collection of one year of operational data. The Part-2 FOT involved the towing and recovery and utility vocations for a second year of data collection. The vehicles that participated in the MTDC project did so through gratis partnerships in return for early access to the results of this study. Partnerships such as these are critical to FOTs in which real-world data is being collected. In Part 1 of the project, Oak Ridge National Laboratory (ORNL) established partnerships with the H.T. Hackney Company (HTH), one of the largest wholesale distributors in the country, distributing products to 21 states; and with Knoxville Area Transit (KAT), the city of Knoxville s transit system, which operates across Knoxville and parts of Knox County. These partnerships and agreements provided ORNL access to three Class-7 day-cab tractors that regularly haul 28 ft pup trailers (HTH) and three Class-7 buses for the collection of duty cycle data. In addition, ORNL collaborated with the Federal Motor Carrier Safety Administration (FMCSA) to determine if there were possible synergies between this duty cycle data collection effort and FMCSA s need to learn more about the operation and duty cycles of medium trucks. FMCSA s primary interest was in collecting safety data relative to the driver, carrier, and vehicle. In Part 2 of the project, ORNL partnered with the Knoxville Utilities Board, which made available three Class-8 trucks. Fountain City Wrecker Service was also a Part 2 partner, providing three Class-6 rollback trucks. In order to collect the duty cycle and safety-related data, ORNL developed a data acquisition system (DAS) that was placed on each test vehicle. Each signal recorded in this FOT was collected by means of one of the instruments incorporated into each DAS. Other signals were obtained directly from the vehicle s J1939 and J1708 data buses. A VBOX II Lite collected information available from a global positioning system (GPS), including speed, acceleration, and spatial location information at a rate of 5 Hz for the Part 1 FOT. For the Part 2 FOT, this information was obtained from DAS-based GPS instrumentation. The Air-Weigh LoadMaxx, a self-weighing system that determines the vehicle s gross weight by means of pressure transducers, was used to collect vehicle payload information for the combination, urban transit, and towing and recovery vehicles. A cellular modem, the Raven X EVDO V4221, facilitated the communication between the eDAQ-lite (the data collection engine of the system) and the user. The modem functioned as a wireless gateway, allowing data retrievals and system checks to be performed remotely. Also, in partnership with FMCSA, two additional safety sensors were installed on the combination vehicles: the MGM e-Stroke brake monitoring system and the Tire SafeGuard tire pressure monitoring system. All of these sensors posted data to the J1939 data bus, enabling the signals to be read withou..