Heavy-Duty Vehicle Port Drayage Drive Cycle Characterization and Development


Book Description

In an effort to better understand the operational requirements of port drayage vehicles and their potential for adoption of advanced technologies, National Renewable Energy Laboratory (NREL) researchers collected over 36,000 miles of in-use duty cycle data from 30 Class 8 drayage trucks operating at the Port of Long Beach and Port of Los Angeles in Southern California. These data include 1-Hz global positioning system location and SAE J1939 high-speed controller area network information. Researchers processed the data through NREL's Drive-Cycle Rapid Investigation, Visualization, and Evaluation tool to examine vehicle kinematic and dynamic patterns across the spectrum of operations. Using the k-medoids clustering method, a repeatable and quantitative process for multi-mode drive cycle segmentation, the analysis led to the creation of multiple drive cycles representing four distinct modes of operation that can be used independently or in combination. These drive cycles are statistically representative of real-world operation of port drayage vehicles. When combined with modeling and simulation tools, these representative test cycles allow advanced vehicle or systems developers to efficiently and accurately evaluate vehicle technology performance requirements to reduce cost and development time while ultimately leading to the commercialization of advanced technologies that meet the performance requirements of the port drayage vocation. The drive cycles, which are suitable for chassis dynamometer testing, were compared to several existing test cycles. This paper presents the clustering methodology, accompanying results of the port drayage duty cycle analysis and custom drive cycle creation.




Heavy-Duty Vehicle Port Drayage Drive Cycle Characterization and Development


Book Description

In an effort to better understand the operational requirements of port drayage vehicles and their potential for adoption of advanced technologies, National Renewable Energy Laboratory (NREL) researchers collected over 36,000 miles of in-use duty cycle data from 30 Class 8 drayage trucks operating at the Port of Long Beach and Port of Los Angeles in Southern California. These data include 1-Hz global positioning system location and SAE J1939 high-speed controller area network information. Researchers processed the data through NREL's Drive-Cycle Rapid Investigation, Visualization, and Evaluation tool to examine vehicle kinematic and dynamic patterns across the spectrum of operations. Using the k-medoids clustering method, a repeatable and quantitative process for multi-mode drive cycle segmentation, the analysis led to the creation of multiple drive cycles representing four distinct modes of operation that can be used independently or in combination. These drive cycles are statistically representative of real-world operation of port drayage vehicles. When combined with modeling and simulation tools, these representative test cycles allow advanced vehicle or systems developers to efficiently and accurately evaluate vehicle technology performance requirements to reduce cost and development time while ultimately leading to the commercialization of advanced technologies that meet the performance requirements of the port drayage vocation. The drive cycles, which are suitable for chassis dynamometer testing, were compared to several existing test cycles. This paper presents the clustering methodology, accompanying results of the port drayage duty cycle analysis and custom drive cycle creation.




Heavy-Duty Vehicle Port Drayage Drive Cycle Characterization and Development


Book Description

In an effort to better understand the operational requirements of port drayage vehicles and their potential for adoption of advanced technologies, National Renewable Energy Laboratory (NREL) researchers collected over 36,000 miles of in-use duty cycle data from 30 Class 8 drayage trucks operating at the Port of Long Beach and Port of Los Angeles in Southern California. These data include 1-Hz global positioning system location and SAE J1939 high-speed controller area network information. Researchers processed the data through NREL's Drive-Cycle Rapid Investigation, Visualization, and Evaluation tool to examine vehicle kinematic and dynamic patterns across the spectrum of operations. Using the k-medoids clustering method, a repeatable and quantitative process for multi-mode drive cycle segmentation, the analysis led to the creation of multiple drive cycles representing four distinct modes of operation that can be used independently or in combination. These drive cycles are statistically representative of real-world operation of port drayage vehicles. When combined with modeling and simulation tools, these representative test cycles allow advanced vehicle or systems developers to efficiently and accurately evaluate vehicle technology performance requirements to reduce cost and development time while ultimately leading to the commercialization of advanced technologies that meet the performance requirements of the port drayage vocation. The drive cycles, which are suitable for chassis dynamometer testing, were compared to several existing test cycles. This paper presents the clustering methodology, accompanying results of the port drayage duty cycle analysis and custom drive cycle creation.




Medium Truck Duty Cycle Data from Real-World Driving Environments


Book Description

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 ...




Driving Cycles Development


Book Description




Medium Truck Duty Cycle Data from Real-World Driving Environments


Book Description

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 the present time, nearly 80% of the 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 truck research, and is leading the 21st Century Truck Partnership whose stretch goals involve a reduction by 50% of the fuel consumption of heavy vehicles on a ton-mile basis. This Medium Truck Duty Cycle (MTDC) Project is a critical element in DOE s vision for improved heavy vehicle energy efficiency and is unique in that there is no other national database of characteristic duty cycles for medium trucks. It involves the collection of real-world data for various situational characteristics (rural/urban, freeway/arterial, congested/free-flowing, good/bad weather, etc.) and looks at the unique nature of medium trucks drive cycles (stop-and-go delivery, power takeoff, idle time, short-radius trips), to provide a rich source of data that can contribute to the development of new tools for fuel efficiency 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 heavy vehicle energy efficiency research. The MTDC project involves 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 one year of data collection. The Part-2 FOT will involve the towing/recovery and utility vocations. The vehicles participating in the MTDC project are doing 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, one of the largest wholesale distributors in the country, distributing products to 21 states; and with the Knoxville Area Transit (KAT), the City of Knoxville s transit system, operating services across the city of Knoxville and parts of Knox co. These partnerships and agreements provided ORNL access to three Class-7 2005/2007 International day-cab tractors, model 8600, which regularly haul 28 ft pup trailers (H.T. Hackney Co) and three Class-7 2005 Optima LF-34 buses (KAT), for collection of duty cycle data. In addition, ORNL has 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 the second-largest fuel consuming commercial vehicle category in the US. FMCSA s primary interest was in collecting safety data relative to the driver, carrier, and vehicle. In order to collect the duty cycle and safety-related data, ORNL developed a data acquisition and wireless communication system 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 data acquisition system (DAS). Native signals were obtained directly from the vehicle s J1939 and J1708 data buses. A VBOX II Lite collected Global Positioning System related information including speed, acceleration, and spatial location information at a rate of 5 Hz, and communicated this data via the CAN (J1939) protocol. The Air-Weigh LoadMaxx, a self-weighing system which determines the vehicle s gross weight by means of pressure transducers and posts the weight to the vehic ...




Study of the Driving Cycle for Heavy Duty Trucks in Hilly Terrain and Its Effect on Calculated Emissions, and Comparison of Two Mobile Emission Models


Book Description

Vehicle emissions were estimated using MOVES2010a and MOBILE6.2 for a Pittsburgh case study involving a modal shift in freight transportion. MOVES2010a (hereafter referred to as MOVES) is currently the USEPA official mobile source emissions computer model; it replaced the older model, MOBILE6.2. Changing the method of hauling freight from highway to waterway is the transport modal shift. Results from this part of the study showed that emission estimates for all vehicle types using MOVES were higher than emissions estimated using MOBILE6.2/NMIM for CO, NO[subscript X], PM10, PM2.5, and VOC, but emissions were lower for CO2 and NH3 using MOVES relative to MOBILE6.2. For heavy-heavy duty diesel (HHDD) vehicles, higher emissions were estimated using MOVES for all pollutants except for NH3 when compared to MOBILE6.2. The largest difference between the two models was seen in PM10 and PM2.5. The second part of this dissertation focused on driving cycles for HHDD vehicles in hilly terrain and its effect on emissions. The MOVES model incorporates 12 default driving schedules for HHDD vehicles. Each driving schedule represents different average vehicle speeds, which tend to over generalize the driving patterns for these vehicles in hilly terrain. The characteristics of HHDD vehicle driving cycles were analyzed by using actual GPS speed and terrain data from driving activity that occurred on a section of the Federal Interstate to demonstrate possible drawbacks of default driving schedules in the current version of MOVES. Profiles of speed versus time as well as road grades were constructed to validate this. Emissions were calculated using a MOVES' operating mode approach. Results showed that a wider range of speeds and higher scaled tractive power occurred in the driving cycles constructed from the real activity data in hilly terrain than the MOVES default driving schedules. NO[subscript X], PM2.5, and THC emissions and total energy consumption calculated using the synthetic driving cycles of the trucks with grades, associated with the hilly terrain, were 7.6%, 14%, 3%, and 11%, respectively, higher than when using the MOVES default driving schedules at the same average speed (63.9 mph) for 0.3% average road grade. On the other hand, CO emissions were 3.4% lower for the synthetic driving cycles. More analyses associated with the driving cycles were presented in this dissertation, and recommendations were made regarding an improvement of default driving schedules in MOVES as well.







Heavy-Duty Wheeled Vehicles


Book Description

Heavy-duty wheeled vehicles (HDWVs) are all-wheel-drive vehicles that carry 25 tons or more and have three or more axles. They transport heavy, bulky cargo such as raw minerals, timber, construction materials, pre-fabricated modules, weapons, combat vehicles, and more. HDWVs are used in a variety of industries (mining, logging, construction, energy) and are critical to a country’s economy and defense. These vehicles have unique development requirements due to their high loads, huge dimensions, and specific operating conditions. Hauling efficiencies can be improved by increasing vehicle load capacity; however capacities are influenced by legislation, road limits, and design. Designing HDWVs differs from other multi-purpose all-wheel-drive vehicles. The chassis must be custom-designed to suit the customer’s particular purpose. The number of axles is another variable, as well as which ones are driving and which are driven. Tires are also customizable. Translated by SAE from Russian, this book narrates the history of HDWVs and presents the theory and calculations required to design them. It summarizes results of the authors’ academic research and experience and presents innovative technical solutions used for electric and hydrostatic transmissions, steering systems, and active safety of these vehicles. The book consists of three parts. Part one covers HDWV design history and general design methods, including basic vehicle design, and evaluating HDWV use conditions. Part one also covers general operation requirements and consumer needs, and a brief analysis of structural components of existing HDWVs and prototypes. Part two outlines information needs for designing HDWVs. Part three reviews basic theory and calculation of innovative technical solutions, as well as special requirements for component parts. This comprehensive title provides the following information about HDWVs: • History of design and manufacture. • Manufacturers’ summary design data. • Background data on sample vehicles. • Component calculation examples. • Overview of motion theory, which is useful in design and placement of bulky cargo.




Data Mining With Decision Trees: Theory And Applications (2nd Edition)


Book Description

Decision trees have become one of the most powerful and popular approaches in knowledge discovery and data mining; it is the science of exploring large and complex bodies of data in order to discover useful patterns. Decision tree learning continues to evolve over time. Existing methods are constantly being improved and new methods introduced.This 2nd Edition is dedicated entirely to the field of decision trees in data mining; to cover all aspects of this important technique, as well as improved or new methods and techniques developed after the publication of our first edition. In this new edition, all chapters have been revised and new topics brought in. New topics include Cost-Sensitive Active Learning, Learning with Uncertain and Imbalanced Data, Using Decision Trees beyond Classification Tasks, Privacy Preserving Decision Tree Learning, Lessons Learned from Comparative Studies, and Learning Decision Trees for Big Data. A walk-through guide to existing open-source data mining software is also included in this edition.This book invites readers to explore the many benefits in data mining that decision trees offer: