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Evaluating the Impact of Connected Vehicle Technology on Heavy-Duty Vehicle Emissions
Abstract
div class="section abstract"> Eco-driving algorithms enabled by Vehicle to Everything (V2X) communications in Connected and Automated Vehicles (CAVs) can improve fuel economy by generating an energy-efficient velocity trajectory for vehicles to follow in real time. Southwest Research Institute (SwRI) demonstrated a 7% reduction in energy consumption for fully loaded class 8 trucks using SwRI’s eco-driving algorithms. However, the impact of these schemes on vehicle emissions is not well understood. This paper details the effort of using data from SwRI’s on-road vehicle tests to measure and evaluate how eco-driving could impact emissions. Two engine and aftertreatment configurations were evaluated: a production system that meets current NOX standards and a system with advanced aftertreatment and engine technologies designed to meet low NOX 2031+ emissions standards. For the production system, eco-driving on an urban cycle resulted in a CO2 reduction of 8.4% but an increase of 18% in brake specific NOX over the baseline cycle. With the low NOX system, eco-driving achieved a similar reduction in CO2. NOX emissions increased 108% over the baseline but remained below the low NOX standard. The eco-driving cycles generated lower exhaust temperatures than the baseline cycles, which inhibited SCR catalyst performance and increased tailpipe NOX. Conversely, a port drayage cycle with eco-driving showed improvements in both CO2 and NOX emissions over the baseline. The results demonstrate that eco-driving algorithms can be a technological enabler to meet current and potential future emissions targets for heavy-duty applications.
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... However, in both cases, the traffic component was not studied, as was the absence of incorporating adaptive coordinated traffic lights. Gankov et al. (2023) Via V2X communication, 7% of fuel consumption and 8.4% of CO 2 were reduced. Zhou et al. (2022) they obtained better performance with a 17.56% reduction in fuel consumption. ...
Transportation is among the most prevalent, challenging, and complex sectors that significantly influence energy consumption, environmental sustainability, and economic dynamics. So, it is critical to identify effective strategies to lessen its detrimental consequences. The conventional signalized intersections, which use pre-programmed schedules to control traffic flow at junctions, are one of the most used systems and one of the biggest high Energy consumption rates creators. The study focuses on reducing fuel consumption and enhancing traffic flow by utilizing advanced technologies such as intelligent transportation systems (ITS), communication technologies, adaptive traffic systems, and coordination techniques. The study incorporates manually collected real-world data with an average time step of 15 min through various simulation scenarios. The aim is to develop adaptive coordination strategies for signalized intersections using intelligent communication technologies (ICT) and uncover the importance of coordination techniques combined with ICT. Additionally, the study investigates the effects of these technologies on traffic flow, energy consumption, and air pollution emissions. The results show that using adaptive traffic lights only reduces fuel consumption and emissions by 8%, while applying coordination techniques can increase the percentage up to 25%. In addition, by implementing ICT combined with coordination technique, fuel consumption and emissions, average queue length, and waiting time decreased by around 48%, 63%, and 41%, respectively while increase the average vehicle speed by 46% compared to the pretimed isolated intersections. The findings highlight the developed strategy capability in enhancing traffic flow, energy efficiency, environmental sustainability, leading to a cleaner environment, and transportation sector cost savings.
div class="section abstract"> Eco-Driving with connected and automated vehicles has shown potential to reduce energy consumption of an individual (i.e., ego) vehicle by up to 15%. In a project funded by ARPA-E, a team led by Southwest Research Institute demonstrated an 8-12% reduction in energy consumption on a 2017 Prius Prime. This was demonstrated in simulation as well as chassis dynamometer testing. The authors presented a simulation study that demonstrated corridor-level energy consumption improvements by about 15%. This study was performed by modeling a six-kilometer-long urban corridor in Columbus, Ohio for traffic simulations. Five powertrain models consisting of two battery electric vehicles (BEVs), a hybrid electric vehicle (HEV), and two internal combustion engine (ICE) powered vehicles were developed. The design of experiment consisted of sweeps for various levels of traffic, penetration of smart vehicles, penetration of technology, and powertrain electrification. The large-scale simulation study consisted of doing approximately 96,000 powertrain simulations. A sophisticated clustering scheme was built and utilized to down select representative traces for each scenario from the simulation study for vehicle testing on a chassis dynamometer. This paper provides a summary of individual ego vehicle testing as well as a comprehensive overview of the method utilized for down selecting representative traces from large scale simulation studies that can be used to quantify corridor level benefits. Vehicle test results along with corresponding analyses are presented.
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In recent years, the development of connected and automated vehicle (CAV) technology has inspired numerous advanced applications targeted at improving existing transportation systems. As one of the widely studied applications of CAV technology, connected eco-driving takes advantage of Signal Phase and Timing (SPaT) information from traffic signals to enable CAVs to approach and depart from sig-nalized intersections in an energy-efficient manner. However, the majority of the connected eco-driving studies have been numerical or microscopic traffic simulations. Only few studies have implemented the application on real vehicles, and even fewer have been focused on heavy-duty trucks. In this study, we developed a connected eco-driving system and equipped it on a heavy-duty diesel truck using cellular-based wireless communications. Field trials were conducted in the City of Carson, California, along two corridors with six connected signalized intersections capable of communicating their SPaT information. Early results showed the benefits of the system in smoothing the speed profiles of the equipped truck when approaching the connected signalized intersections.
div class="section abstract"> Despite considerable progress over the last several decades, California continues to face some of the most significant air quality problems in the United States. These continued issues highlight the need for further mobile source NOX reductions to help California and other areas meet ambient air quality targets mandated by the U.S. EPA. Beginning in 2014, the California Air Resources Board (CARB) launched a program aimed at demonstrating technologies that could enable heavy-duty on-highway engines to reach tailpipe NOX levels up to 90% below the current standards, which were implemented in 2010. At the same time, mandated improvements to greenhouse gas emissions (GHG) require that these NOX reductions be achieved without sacrificing fuel consumption and increasing GHG emissions. The CARB demonstration program has progressed through several stages since it was initiated, and the Stage 3 Low NOX program completed in 2020 represents the culmination of these technology demonstration efforts. This effort, using a 2017 production diesel engine as a baseline, demonstrated a combination of technologies that enabled Low NOX emission levels near the 90% reduction target, while at the same time maintaining GHG emission rates at the same levels as the base engine.
Previous publications have gone into detail regarding individual elements of the Stage 3 technology package. This paper will present a summary of the final configuration and final results of the Stage 3 program, including results for the fully aged aftertreatment system after the equivalent of 435,000 miles of operation. The performance of the final test article will be shown over a variety of both regulatory duty cycles and other off-cycle operations. The final fuel consumption and GHG performance of the system will also be described based on the benchmarking methods specified by EPA in the Phase 2 GHG standards.
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div class="section abstract"> This paper presents the evolution of a series of connected, automated vehicle technologies from simulation to in-vehicle validation for the purposes of minimizing the fuel usage of a class-8 heavy duty truck. The results reveal that an online, hierarchical model-predictive control scheme, implemented via the use of extended horizon driver advisories for velocity and gear, achieves fuel savings comparable to predictions from software-in-the-loop (SiL) simulations and engine-in-the-loop (EiL) studies that operated with a greater degree of powertrain and chassis automation. The work of this paper builds on prior work that presented in detail this predictive control scheme that successively optimizes vehicle routing, arrival and departure at signalized intersections, speed trajectory planning, platooning, predictive gear shifting, and engine demand torque shaping. This paper begins by outlining the controller development progression from a previously published engine-in-the-loop study to the in-vehicle driver-in-the-loop testing highlighted in this work. The purpose of this field testing is to quantify the level of agreement between field data and prior results, particularly noting the influence of noise, disturbances and unmodeled dynamics. Also detailed are the steps to effectively implement the predictive horizons in experimentation with human rather than automated driver inputs by replacing the conventional speedometer with an augmented driver display. The driver display seeks to maintain driver awareness and route preview simultaneously. Additional features of the instrumented truck testing protocol include detailed route maps on speed limits, intersections, traffic and road grade; highly accurate fuel flowrate measurements calibrated via gravimetric methods; and closed-loop control between the predictive controller, driver, and vehicle controller area network (CAN). These combined capabilities allow for model validation, investigation of the effects of human-in-the-loop on controller performance, and identification of unmodeled dynamics and disturbances. The results show that the driver advisory implementation matches fuel consumption predictions from software-in-the-loop simulations and engine-in-the-loop studies to within 2-9%. Following the study of in-vehicle performance and variability, these results also validate, using real road tests, the up to 21% fuel savings achieved by using the hierarchical control scheme with a combination of multiple chassis and powertrain optimization technologies.
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div class="section abstract"> Onboard sensing and external connectivity using Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I) and Vehicle-to-Everything (V2X) technologies allows a vehicle to "know" its future operating environment with some degree of certainty, greatly narrowing prior information gaps. The increased development of such connected and automated vehicle systems, currently used mostly for safety and driver convenience, presents new opportunities to improve the energy efficiency of individual vehicles [ 1 , 2 , 3 , 4 , 5 ]. Southwest Research Institute (SwRI) in collaboration with Toyota Motor North America and University of Michigan is currently working on improving energy consumption of a Toyota Prius Prime 2017 by 20%. This paper will provide an overview of the various algorithms that are being developed to achieve the energy consumption target. Custom tools such as a traffic simulator was built to model traffic flow in Fort Worth, Texas with sufficient accuracy. The benefits of a traffic simulator are two-fold: (1) generation of repeatable traffic flow patterns and (2) evaluation of the robustness of control algorithms by introducing disturbances. The traffic simulator is integrated with a high-precision hub dynamometer for testing the various control algorithms in a controlled environment. Vehicle testing results from the hub dynamometer is presented.
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Eco-Driving Driver Advisory Application for Connected Vehicles Ground Vehicle Systems Engineering and Technology Symposium
- P Bhagdikar
- S Gankov
- S Rengarajan
- S Hotz