<div class="section abstract"><div class="htmlview paragraph">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 [<span class="xref">1</span>, <span class="xref">2</span>, <span class="xref">3</span>, <span class="xref">4</span>, <span class="xref">5</span>]. 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.</div></div>

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<div class="section abstract"><div class="htmlview paragraph">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.</div></div>

<div class="section abstract"><div class="htmlview paragraph">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 NO_X standards and a system with advanced aftertreatment and engine technologies designed to meet low NO_X 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 NO_X over the baseline cycle. With the low NO_X system, eco-driving achieved a similar reduction in CO₂. NO_X emissions increased 108% over the baseline but remained below the low NO_X standard. The eco-driving cycles generated lower exhaust temperatures than the baseline cycles, which inhibited SCR catalyst performance and increased tailpipe NO_X. Conversely, a port drayage cycle with eco-driving showed improvements in both CO₂ and NO_X 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.</div></div>