• Energy Research

Due to increased public awareness on global climate change and other energy and environmental problems, a variety of strategies are being developed and used to reduce the energy consumption and environmental impact of roadway travel. In advanced traveler information systems, recent efforts have been made in developing a new navigation concept called “eco-routing,” which finds a route that requires the least amount of fuel and/or produces the least amount of emissions. This paper presents an eco-routing navigation system that determines the most eco-friendly route between a trip origin and a destination. It consists of the following four components: 1) a Dynamic Roadway Network database, which is a digital map of a roadway network that integrates historical and real-time traffic information from multiple data sources through an embedded data fusion algorithm; 2) an energy/emissions operational parameter set, which is a compilation of energy/emission factors for a variety of vehicle types under various roadway characteristics and traffic conditions; 3) a routing engine, which contains shortest path algorithms used for optimal route calculation; and 4) user interfaces that receive origin-destination inputs from users and display route maps to the users. Each of the system components and the system architecture are described. Example results are also presented to prove the validity of the eco-routing concept and to demonstrate the operability of the developed eco-routing navigation system. In addition, current limitations of the system and areas for future improvements are discussed.

Recently, advanced navigation systems have been developed that provide users the ability to select not only a shortest-distance route and even the shortest-duration route (on the basis of real-time traffic congestion information) but also routes that minimize fuel consumption as well as greenhouse gas and pollutant emissions. In these ecorouting systems, fuel consumption and emission attributes are estimated for roadway links on the basis of the measured traffic volume, density, and average speed. Instead of standard travel time or distance attributes, these link attributes are then used as cost factors when an optimal route for any particular trip is selected. In addition to roadway congestion attributes, road grade factors also have an effect on fuel consumption and emissions. This study evaluated the effect of road grade on vehicle fuel consumption (and thus carbon dioxide [CO2] emissions). The real-world experimental results show that road grade does have significant effects on the fuel economy of light-duty vehicles both at the roadway link level and at the route level. Comparison of the measured fuel economy between a flat route and example hilly routes revealed that the vehicle fuel economy of the flat route is superior to that of the hilly routes by approximately 15% to 20%. This road grade effect will certainly play a significant role in advanced ecorouting navigation algorithms, in which the systems can guide drivers away from steep roadways to achieve better fuel economy and reduce CO2 emissions.

Abstract Battery electric vehicles (BEVs) are a critical pathway towards achieving energy independence and meeting greenhouse and criteria pollutant gas reduction goals in the current and future transportation sector. Emerging connected and automated vehicle (CAV) technologies further open the door for developing innovative applications and systems to leverage vehicle efficiency and substantially transform transportation systems. Therefore, we present a simulation study of various BEV types and compare the performance when driving on real-road drive cycles to highly optimized eco-driving cycles using advanced CAV technologies. The results demonstrate that eco-driving has a high potential to reduce energy consumption for all types of BEVs considered. The investigated BEVs include a compact vehicle, a transit city bus, and a Class 7 delivery truck. The impact of eco-driving on conventional vehicles was also compared to comparable BEVs. Compared to the BEVs, eco-driving provides a larger reduction in the conventional vehicle's braking energy loss, and also provides conventional vehicles with greater reductions in the engine mechanical energy output but the fuel savings did not show a consistent trend among all the conventional vehicle types. As part of the study, a comprehensive EV powertrain model was developed to account for key EV components and powertrain configurations.

Abstract Heavy-duty trucks are a critical component of any goods movement system; however, they consume a large amount of fuel and emit significant pollutant and greenhouse gas emissions. An important consideration for reducing fuel consumption and improving trucking operations is efficient vehicle routing. Many existing fleet management and routing systems are based on minimizing distance traveled which does not necessarily minimize fuel consumption or emissions, particularly when under traffic congestion and in areas with hilly terrain. This paper describes the development of an eco-routing and navigation system for heavy-duty trucks, including an underlying truck energy and emission model that accounts for vehicle weight, real-time traffic speed, and road grade. Validation results presented in this paper show that the eco-routing system was able to predict fuel consumption within 7.5% over the test routes. In addition, this paper presents an analysis of the tradeoff between the amount of fuel savings and the added travel time relative to the fastest route. The elasticity of fuel with respect to travel time is calculated and a sensitivity analysis with respect to fuel price and the value of travel time is performed which provides the “break even” conditions between a fuel optimized and a time optimized route.

In this paper, a stochastic optimization framework is developed to reduce congestion on distribution feeders using batteries, under offline and online design paradigms. Our design is customized, implemented, tested, and analyzed in a real-world testbed that was built based on a university-utility collaboration in California. Our proposed method seeks to optimize peak load at the feeder while taking into account feeder load uncertainty as well as hardware, utility, and customer constraints. We present both experimental and numerical results. Insightful observations, design trade-offs, and lessons learned are discussed.

Surface transportation consumes a vast quantity of fuel and accounts for about a third of the US carbon dioxide (CO2) emissions. In addition to the use of more fuel-efficient vehicles and carbon-neutral alternative fuels, fuel consumption and CO2 emissions can be lowered through a variety of strategies that reduce congestion, smooth traffic flow, and reduce excessive vehicle speeds. Eco-driving is one such strategy. It typically consists of changing a person's driving behavior by providing general static advice to the driver (e.g. do not accelerate too quickly, reduce speeds, etc.). In this study, we investigate the concept of dynamic eco-driving, where advice is given in real-time to drivers changing traffic conditions in the vehicle's vicinity. This dynamic strategy takes advantage of real-time traffic sensing and telematics, allowing for a traffic management system to monitor traffic speed, density, and flow, and then communicates advice in real-time back to the vehicles. By providing dynamic advice to drivers, approximately 10-20% in fuel savings and lower CO2 emissions are possible without a significant increase in travel time. Based on simulations, it was found that in general, higher percentage reductions in fuel consumption and CO2 emission occur during severe compared to less congested scenarios. Real-world experiments have also been carried out, showing similar reductions but to a slightly smaller degree.

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.

Built upon real-world supervisory control and data acquisition (SCADA) and other measurements of a featured utility-scale testbed, this paper addresses the participation of customer side battery energy storage in providing peak load shaving at a 12.47 kV distribution feeder. A stochastic optimization-based battery operation framework is developed that enables feeder load peak shaving under offline (day-ahead) as well as online (close-to-real-time) control settings. Both designs work through establishing a secured communications line to the utility’s feeder-level SCADA system. Multiple field experiments are conducted, including a full day test with complete control of a 1 MWh/200 kW battery system, as well as various numerical assessments based upon one year of real feeder data.