Kevin Walkowicz

In-Use and Vehicle Dynamometer Evaluation and Comparison of Class 7 Hybrid Electric and Conventional Diesel Delivery Trucks

This study compared fuel economy and emissions between heavy-duty hybrid electric vehicles (HEVs) and equivalent conventional diesel vehicles. In-use field data were collected from daily fleet operations carried out at a FedEx facility in California on six HEV and six conventional 2010 Freightliner M2-106 straight box trucks. Field data collection primarily focused on route assessment and vehicle fuel consumption over a six-month period. Chassis dynamometer testing was also carried out on one conventional vehicle and one HEV to determine differences in fuel consumption and emissions. Route data from the field study was analyzed to determine the selection of dynamometer test cycles. From this analysis, the New York Composite (NYComp), Hybrid Truck Users Forum Class 6 (HTUF 6), and California Air Resource Board (CARB) Heavy Heavy-Duty Diesel Truck (HHDDT) drive cycles were chosen. The HEV showed 31% better fuel economy on the NYComp cycle, 25% better on the HTUF 6 cycle and 4% worse on the CARB HHDDT cycle when compared to the conventional vehicle. The in-use field data indicates that the HEVs had around 16% better fuel economy than the conventional vehicles. Dynamometer testing also showed that the HEV generally emitted higher levels of nitric oxides than the conventional vehicle over the drive cycles, up to 77% higher on the NYComp cycle (though this may at least in part be attributed to the different engine certification levels in the vehicles tested). The conventional vehicle was found to accelerate up to freeway speeds over ten seconds faster than the HEV.

Characterization of in-use medium duty electric vehicle driving and charging behavior

The U.S. Department of Energys 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.

A Statistical Characterization of School Bus Drive Cycles Collected via Onboard Logging Systems

In an effort to characterize the dynamics typical of school bus operation, National Renewable Energy Laboratory (NREL) researchers set out to gather in-use duty cycle data from school bus fleets operating across the country. Employing a combination of Isaac Instruments GPS/CAN data loggers in conjunction with existing onboard telemetric systems resulted in the capture of operating information for more than 200 individual vehicles in three geographically unique domestic locations. In total, over 1,500 individual operational route shifts from Washington, New York, and Colorado were collected. Upon completing the collection of in-use field data using either NREL-installed data acquisition devices or existing onboard telemetry systems, large-scale duty-cycle statistical analyses were performed to examine underlying vehicle dynamics trends within the data and to explore vehicle operation variations between fleet locations. Based on the results of these analyses, high, low, and average vehicle dynamics requirements were determined, resulting in the selection of representative standard chassis dynamometer test cycles for each condition. In this paper, the methodology and accompanying results of the large-scale duty-cycle statistical analysis are presented, including graphical and tabular representations of a number of relationships between key duty-cycle metrics observed within the larger data set. In addition to presenting the results of this analysis, conclusions are drawn and presented regarding potential applications of advanced vehicle technology as it relates specifically to school buses.

King County Metro - Allison Hybrid Electric Transit Bus Testing

The National Renewable Energy Laboratorys ReFUEL facility conducted chassis dynamometer testing of two 60-foot articulated transit buses, one conventional and one hybrid to study emissions trends.

A technology optimization process for heavy hybrid electric vehicle systems using computational models

We have developed a computer-based technology optimization process to define vehicle systems that meet specified goals and constraints using a minimum amount of resources. The process is built around answering three questions: Where are we now? Where do we want to go? And what is the best way to get there? A technical target setting algorithm helps answer the last question. The target setting algorithm uses computational models that predict system responses for various combinations of technology attributes, quantitatively defined requirements and goals, and relationships between improvements in technology attributes and resource requirements. The algorithm yields the mix of technologies that achieve overall program and vehicle goals using a minimum amount of resources. This process was developed for and applied to the U.S. Department of Energys Advanced Heavy Hybrid Propulsion Systems activity. In this paper, we describe the technology optimization process, with a focus on technical target setting, and illustrate its use with a simple example.

The DOE/NREL Next Generation Natural Gas Vehicle Program - An Overview

This paper summarizes the Next Generation Natural Gas Vehicle (NG-NGV) Program that is led by the U.S. Department Of Energys (DOEs) Office of Heavy Vehicle Technologies (OHVT) through the National Renewable Energy Laboratory (NREL). The goal of this program is to develop and implement one Class 3-6 compressed natural gas (CNG) prototype vehicle and one Class 7-8 liquefied natural gas (LNG) prototype vehicle in the 2004 to 2007 timeframe. OHVT intends for these vehicles to have 0.5 g/bhp-hr or lower emissions of oxides of nitrogen (NOx) by 2004 and 0.2 g/bhp-hr or lower NOx by 2007. These vehicles will also have particulate matter (PM) emissions of 0.01 g/bhp-hr or lower by 2004. In addition to ambitious emissions goals, these vehicles will target life-cycle economics that are compatible with their conventionally fueled counterparts.