In recent years, eco-friendly driving or ecodriving technologies are being developed to assist human drivers to achieve maximum fuel/energy efficiency in different driving conditions. Enhanced by V2X wireless communications, connected ecodriving is expected to be very promising in reducing transportation-related fossil fuel consumption as well as pollutant emissions. Besides, the deployment of electric vehicles (EVs) also has great potential in reducing greenhouse gas emissions due to the use of batteries as the sole energy source. Although recent research shows that significant energy savings can be achieved with the aid of ecodriving systems in real-world driving, there have been very few research efforts that consider human driver error, especially for electric vehicle (EV) driving. In this paper, a connected cooperative ecodriving system for energy-efficient driving that considers human driver error is designed and evaluated with an EV energy consumption model. Real-world driving data were collected and used to evaluate system performance in terms of energy consumption. The simulation and numerical analysis shows that an average of 12% energy savings can be achieved by the proposed system that considers human driver error comparing with the conventional ecodriving system without considering driver error.
G OING THE E XTRA M ILE INTELLIGENT ENERGY MANAGEMENT OF PLUG-IN HYBRID ELECTRIC VEHICLES K A N O K B O R I B O O N S O M S I N , G U O Y U A N W U , A N D M AT T H E W B A R T H P lug-in hybrid electric vehicles (PHEVs) have generated significant interest for their potential to decrease dependence on imported oil and to cut pollution and greenhouse gas emissions. While hybrid electric vehicles (HEVs) rely on their internal combustion engines to recharge their batteries, PHEVs generally have larger batteries and can be recharged by plugging into an outside electricity source, such as a standard home outlet (Figure 1). As a result, PHEVs are potentially more efficient and cleaner than HEVs, in part because more of their energy can come from clean, renewable sources. A critical consideration in PHEV development is how energy is produced and used. More flexible and intelligent PHEV energy management strategies can save energy and produce lower emissions. This can translate to an increase in fuel economy of 5 to 10 more miles per gallon of gas for a typical PHEV that already gets 60 miles per gallon. We discuss one strategy to optimize energy management by accounting for vehicle position, speed and acceleration, trip progress, roadway characteristics, traffic conditions, and battery recharging opportunities at intermediate stops. We then evaluate this energy management strategy using an example trip, and find that it can result in substantial efficiency gains. Kanok Boriboonsomsin is Associate Research Engineer at the College of Engineering - Center for Environmental Research and Technology, the University of California at Riverside (kanok@cert.ucr.edu). Guoyuan Wu is Assistant Research Engineer at the College of Engineering – Center for Environmental Research and Technology, the University of California at Riverside (gywu@cert.ucr.edu). Matthew Barth is Professor of Electrical and Computer Engineering and Director of the College of Engineering – Center for Environmental Research and Technology, the University of California at Riverside (barth@ee.ucr.edu). A C C E S S
The connected vehicle eco-approach and departure (EAD) application for signalized intersections has been widely studied and is deemed to be effective in terms of reducing energy consumption and both greenhouse gas and other criteria pollutant emissions. Prior studies have shown that tangible environmental benefits can be gained by communicating the driver with the signal phase and timing (SPaT) information of the upcoming traffic signals with fixed time control to the driver. However, similar applications to actuated signals pose a significant challenge due to their randomness to some extent caused by vehicle actuation. Based on the framework previously developed by the authors, a real-world testing has been conducted along the El Camino Real corridor in Palo Alto, CA, USA, to evaluate the system performance in terms of energy savings and emissions reduction. Strategies and algorithms are designed to be adaptive to the dynamic uncertainty for actuated signal and real-world traffic. It turns out that the proposed EAD system can save 6% energy for the trip segments when activated within DSRC ranges and 2% energy for all trips. The proposed system can also reduce 7% of CO, 18% of HC, and 13% of NOx for all trips. Those results are compatible with the simulation results and validate the previously developed EAD framework.
Recently, there has been significant research on environment-focused connected vehicle (CV) applications that involve determining optimal speed profiles for vehicles traveling through signalized intersections and conveying this information to drivers via driver-vehicle interfaces (DVI's). However, findings from previous studies indicate that drivers may not be able to precisely follow the recommended speed profiles, resulting in degraded effectiveness of the applications. Moreover, the DVI could be distracting, which may compromise safety. As an alternative, partial automation can play an important role in ensuring that the benefits of these CV applications are fully realized. In this study, a partially automated vehicle system with an eco-approach and departure feature (called the GlidePath Prototype), which can receive dedicated short range communication message sets from the intersection and automatically follow recommended speed profiles, was developed, demonstrated, and evaluated. The results revealed that compared to manually following the recommended speed profiles, the GlidePath Prototype reduced fuel consumption by 17% on average. In some cases, the fuel savings are greater than 40% while the travel time is shortened by up to 64%. Furthermore, the system potentially improved the driving comfort since it would smooth out the speed profiles.
Eco-friendly Intelligent Transportation System (ITS) applications (e.g., eco-routing) focus on reducing the environmental impacts of vehicle emissions. However, pollutant emissions from vehicles can also cause adverse health impacts for those who live close to the emission sources (e.g., roadways). In our previous work, we had evaluated the Low Exposure Routing (LER) strategy at both vehicle and transportation system levels from a human exposure point of view, and showed that a significant reduction in human exposure with a slight trade-off of travel time could be achieved. In this paper, we propose a new framework to integrate the LER algorithm into an existing agent-based modeling platform, where we apply the LER to heavy-duty diesel trucks (HDDTs) with different technology penetration rates while keeping other vehicles unchanged. Within each iteration of simulation run, the agent-based model considers the interactions among all the vehicles and updates the transportation system information (e.g., link speed, link travel time), which is then used in the LER algorithm in the next iteration. Modeling results show that the inhaled mass of fine particulate matter (PM2.5) for the whole city could be reduced by approximately 30% on a typical workday with the implementation of the proposed agent-based LER truck routing strategy.
Detailed road features like lane markers and stop bars are crucial for many recent Intelligent Transportation System (ITS) applications, especially for advanced driving assistant systems or autonomous vehicles. In this paper, a data-driven method is proposed to identify intersection areas and map stop bar positions without prior knowledge of road information. The proposed method includes 1) a novel and efficient approach to identify intersections by analyzing the entropy of vehicles’ moving directions; and 2) a statistical model for estimating the number, coordinates, and directions of stop bars by evaluating the upstream vehicles’ stopping locations. By applying the method to real-world vehicle positioning data collected at Ann Arbor, its applicability and robustness to handle data at an urban regional scale (a 1.2 km by 2 km rectangular area) are proven. The accuracy of intersection identification is 95.7% for trajectory covered regions. For stop bar positioning, the mean and standard deviation of the errors are 0.27 m and 0.32 m respectively, which satisfy most of the mobility and eco-driving connected and automated vehicle applications such as eco-approach and departure at signalized intersections.
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