simulation

Innovative Vehicle Evaluation Techniques

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Overview
CE-CERT researchers are using technologies such as connected vehicles, high fidelity traffic simulations, advanced computer modeling, and a suite of advanced plug-in tools to revolutionize the way vehicle performance is evaluated. These new methods are cost-effective, save time, and lessen our environmental footprint by eliminating the need to travel for vehicle testing, and provide researchers with the ability to examine vehicle performance under a wide variety of conditions.

 

Projects

  • Plug-In Tools

    Plug-in Tools

    To evaluate the system performance (in terms of safety, accessibility and environmental sustainability) of emerging vehicle and transportation technologies (such as connected and automated vehicles), the research team has been leveraging the state-of-the-art simulation tools and developing associated plug-ins over the years. These tools and plug-ins include microscopic traffic simulators (e.g., VISSIM, PARAMICS, SUMO, and TransModeler), multi-agent based simulators (e.g., BEAM), vehicle simulators (e.g., Autonomie), game engines (e.g., Unity), virtual reality (VR) equipment, Safety Surrogate Assessment Model, emissions models (e.g., MOVES, CMEM).

    Lead Faculty: Dr. Matthew Barth, Dr. Kanok Boriboonsomsin, Guoyuan Wu, Peng Hao

    Driving Simulator Systems

  • Innovative Vehicle Evaluation Techniques
    Project name 
    Description
    Comprehensive Modal Emissions Model (CMEM)

    The Comprehensive Modal Emissions Model (CMEM) is a microscale emissions model that was developed under the National Cooperative Highway Research Program (NCHRP) Project 25-11 in the mid to late 1990's. The overall objective of the project was to develop and verify an emissions model that accurately reflected Light-Duty Vehicle (LDV, i.e., cars and small trucks) emissions produced in various modes of operation. The model is comprehensive in the sense that it is able to predict emissions for a wide variety of LDVs in various states of condition (e.g., properly functioning, deteriorated, malfunctioning). The model has since been updated to include Heavy Duty Diesel Vehicles (HDDV) and is capable of predicting second-by-second tailpipe emissions and fuel consumption for a wide range of vehicle/technology categories.

    Lead Faculty: Dr. George Scora, Dr. Matthew Barth, Dr. Kanok Boriboonsomsin

    Development of a Mobile Energy and Emissions Telematic System (MEETS)

    In this work, CE-CERT's Comprehensive Modal Emissions Model (CMEM) was adapted to operate on-board and in real-time with select parametes from a vehicle's Engine Control Module (ECU). This implementation of the CMEM model bypasses various calculations and sources of error in CMEM and provides a low cost and easily implemented estimation for real-time vehicle emissions with the potential for various applications.

    Lead Faculty: Dr. George Scora, Dr. Matthew Barth

    Evaluating the Environmental Impacts of Connected and Automated Vehicles: Potential Shortcomings of a Binned-Based Emissions Mode

    Compared different energy and emissions modeling approaches for analyzing connected and automated vehicles. Actual energy and emissions were measured on a set of vehicles and compared to a binning model similar to the U.S. Environmental Protection Agency’s MOVES model and also to the Comprehensive Modal Emissions Model (CMEM). Measurements and model predictions are done for the Eco-Approach and Departure (EAD) application, where two vehicles are compared: one that has the EAD technology and one that does not. Traffic Smoothing effects tend to get washed out in MOVES due to bin size. Currently researching preserving and enhancing MOVES with a sub-binning approach. MOVES could be used at different “resolutions” using a Bin-Pyramid approach; original MOVES model is preserved.

    Lead Researchers: David Oswald, Dr. Matthew BarthDr. George ScoraKanok Boriboonsomsin

  • Connected and Autonomous Vehicles (CAV), Plug-in Hybrid Transit Vehicles, Hybrid Powertrain Control

    ARPA-E NEXTCAR

    UC Riverside, in partnership with Oak Ridge National Laboratory and US Hybrid, led the design, development, and testing of an innovative vehicle-powertrain eco-operation system for CNG plug-in hybrid electric buses. This system uses CAV applications like predictive approach and departure at traffic signals, efficient adaptive cruise, and optimized stopping and accelerating from stop signs and bus stops. Since stop-and-go operation wastes a large amount of energy, optimizing these maneuvers for an urban transit bus presents significant opportunities for improving energy efficiency. Using look-ahead information on traffic and road grade, the team will optimize the powertrain operation by managing combustion engine output, electric motor output and battery state of charge in this hybrid application.

    Lead Faculty: Dr. Matthew Barth, Dr. Guoyuan WuKanok Boriboonsomsin, Mike Todd Dylan Brown, Zhouqiao Zhao 

  • Dynometer-in-the-loop (DiL)

    Dynometer-in-the-loop (DiL)

    In this project, an innovative hardware-in-the-loop (HiL) development and testing platform, called Dyno-in-the-Loop (DiL) has been developed, the chassis dynamometer is coupled with real test vehicle(s), microscopic traffic simulator. The result is cost effective system evaluation of emerging transportation technologies and services, such as connected and automated vehicle (CAV) applications. For more information on our dynamometer laboratories, please visit: Vehicle Testing Laboratories

    Lead Faculty: Dr. Matthew Barth, Dr. Guoyuan Wu, Mike Todd, Dylan Brown, Zhouqiao Zhao

    Publication 2020