Statistical Characterization of Medium-Duty Electric Vehicle Drive Cycles


Book Description

In an effort to help commercialize technologies for electric vehicles (EVs) through deployment and demonstration projects, the U.S. Department of Energy's (DOE's) American Recovery and Reinvestment Act (ARRA) provided funding to participating U.S. companies to cover part of the cost of purchasing new EVs. Within the medium- and heavy-duty commercial vehicle segment, both Smith Electric Newton and and Navistar eStar vehicles qualified for such funding opportunities. In an effort to evaluate the performance characteristics of the new technologies deployed in these vehicles operating under real world conditions, data from Smith Electric and Navistar medium-duty EVs were collected, compiled, and analyzed by the National Renewable Energy Laboratory's (NREL) Fleet Test and Evaluation team over a period of 3 years. More than 430 Smith Newton EVs have provided data representing more than 150,000 days of operation. Similarly, data have been collected from more than 100 Navistar eStar EVs, resulting in a comparative total of more than 16,000 operating days. Combined, NREL has analyzed more than 6 million kilometers of driving and 4 million hours of charging data collected from commercially operating medium-duty electric vehicles in various configurations. In this paper, extensive duty-cycle statistical analyses are performed to examine and characterize common vehicle dynamics trends and relationships based on in-use field data. The results of these analyses statistically define the vehicle dynamic and kinematic requirements for each vehicle, aiding in the selection of representative chassis dynamometer test cycles and the development of custom drive cycles that emulate daily operation. In this paper, the methodology and accompanying results of the duty-cycle statistical analysis are presented and discussed. Results are presented in both graphical and tabular formats illustrating a number of key relationships between parameters observed within the data set that relate to medium duty EVs.




Statistical Characterization of Medium-Duty Electric Vehicle Drive Cycles


Book Description

With funding from the U.S. Department of Energy's Vehicle Technologies Office, the National Renewable Energy Laboratory (NREL) conducts real-world performance evaluations of advanced medium- and heavy-duty fleet vehicles. Evaluation results can help vehicle manufacturers fine-tune their designs and assist fleet managers in selecting fuel-efficient, low-emission vehicles that meet their economic and operational goals. In 2011, NREL launched a large-scale performance evaluation of medium-duty electric vehicles. With support from vehicle manufacturers Smith and Navistar, NREL research focused on characterizing vehicle operation and drive cycles for electric delivery vehicles operating in commercial service across the nation.




Model-Based Analysis of Electric Drive Options for Medium-Duty Parcel Delivery Vehicles


Book Description

Medium-duty vehicles are used in a broad array of fleet applications, including parcel delivery. These vehicles are excellent candidates for electric drive applications due to their transient-intensive duty cycles, operation in densely populated areas, and relatively high fuel consumption and emissions. The National Renewable Energy Laboratory (NREL) conducted a robust assessment of parcel delivery routes and completed a model-based techno-economic analysis of hybrid electric vehicle (HEV) and plug-in hybrid electric vehicle configurations. First, NREL characterized parcel delivery vehicle usage patterns, most notably daily distance driven and drive cycle intensity. Second, drive-cycle analysis results framed the selection of drive cycles used to test a parcel delivery HEV on a chassis dynamometer. Next, measured fuel consumption results were used to validate simulated fuel consumption values derived from a dynamic model of the parcel delivery vehicle. Finally, NREL swept a matrix of 120 component size, usage, and cost combinations to assess impacts on fuel consumption and vehicle cost. The results illustrated the dependency of component sizing on drive-cycle intensity and daily distance driven and may allow parcel delivery fleets to match the most appropriate electric drive vehicle to their fleet usage profile.




Characterization of In-use Medium Duty Electric Vehicle Driving and Charging Behavior


Book Description

The U.S. Department of Energy's American Recovery and Reinvestment Act (ARRA) deployment and demonstration projects are helping to commercialize technologies for all-electric vehicles (EVs). Under the ARRA program, data from Smith Electric and Navistar medium duty EVs have been collected, compiled, and analyzed in an effort to quantify the impacts of these new technologies. Over a period of three years, the National Renewable Energy Laboratory (NREL) has compiled data from over 250 Smith Newton EVs for a total of over 100,000 days of in-use operation. Similarly, data have been collected from over 100 Navistar eStar vehicles, with over 15,000 operating days having been analyzed. NREL has analyzed a combined total of over 4 million kilometers of driving and 1 million hours of charging data for commercial operating medium duty EVs. In this paper, the authors present an overview of medium duty EV operating and charging behavior based on in-use data collected from both Smith and Navistar vehicles operating in the United States. Specifically, this paper provides an introduction to the specifications and configurations of the vehicles examined; discusses the approach and methodology of data collection and analysis, and presents detailed results regarding daily driving and charging behavior. In addition, trends observed over the course of multiple years of data collection are examined, and conclusions are drawn about early deployment behavior and ongoing adjustments due to new and improving technology. Results and metrics such as average daily driving distance, route aggressiveness, charging frequency, and liter per kilometer diesel equivalent fuel consumption are documented and discussed.




Medium Duty ARRA Data Reporting and Analysis (Presentation)


Book Description

Medium-duty (MD) electric vehicle (EV) data collection and analysis will help drive design, purchase, and research investments. Over 4 million miles and 160,000 driving days of EV driving data were collected under this project. Publicly available data help drive technology research, development, and deployment. Feeding the vocational database for future analysis will lead to a better understanding of usage and will result in better design optimization and technology implementation. The performance of a vehicle varies with drive cycle and cargo load - MD vehicles are 'multi-functional.' Environment and accessory loads affect vehicle range and in turn add cost by adding battery capacity. MD EV vehicles can function in vocations traditionally serviced by gasoline or diesel vehicles. Facility implications (i.e., demand charges) need to be understood as part of site-based analysis for EV implementation.




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.




Statistical Analysis and Generation of Driving Cycles from the St. Louis Heavy Duty Chase-Car Data


Book Description

The U.S. Environmental Protection Agency (EPA) was introduced on December 2, 1970 by President Richard Nixon. The agency is charged with protecting human health and the environment, by writing and enforcing regulations based on laws passed by Congress. The EPA's struggle to protect health and the environment is seen through each of its official publications. These publications outline new policies, detail problems with enforcing laws, document the need for new legislation, and describe new tactics to use to solve these issues. This collection of publications ranges from historic documents to reports released in the new millennium, and features works like: Bicycle for a Better Environment, Health Effects of Increasing Sulfur Oxides Emissions Draft, and Women and Environmental Health.




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: