Guoyuan Wu

Development and Evaluation of an Intelligent Energy-Management Strategy for Plug-in Hybrid Electric Vehicles

There has been significant interest in plug-in hybrid electric vehicles (PHEVs) as a means to decrease dependence on imported oil and to reduce greenhouse gases as well as other pollutant emissions. One of the critical considerations in PHEV development is the design of its energy-management strategy, which determines how energy in a hybrid powertrain should be produced and utilized as a function of various vehicle parameters. In this paper, we propose an intelligent energy-management strategy for PHEVs. At the trip level, the strategy takes into account a priori knowledge of vehicle location, roadway characteristics, and real-time traffic conditions on the travel route from intelligent transportation system technologies in generating a synthesized velocity trajectory for the trip. The synthesized velocity trajectory is then used to determine batterys charge-depleting control that is formulated as a mixed-integer linear programming problem to minimize the total trip fuel consumption. The strategy can be extended to optimize vehicle fuel consumption at the tour level if a preplanned travel itinerary for the tour and the information about available battery recharging opportunities at intermediate stops along the tour are available. The effectiveness of the proposed strategy, both for the trip- and tour-based controls, was evaluated against the existing binary-mode energy-management strategy using real-world trip/tour examples in southern California. The evaluation results show that the fuel savings of the proposed strategy over the binary-mode strategy are around 10%-15%.

Advanced intersection management for connected vehicles using a multi-agent systems approach

Transportation is responsible for approximately a third of greenhouse gases (GHG) and a major source of other pollutants including hydrocarbons (HC), carbon monoxide (CO), and oxides of nitrogen (NOx). Intelligent Transportation System (ITS) technology can be used to lower vehicle emissions and fuel consumption, in addition to reducing traffic congestion, smoothing traffic flow, and improving roadway safety. As wireless communication advances, connected-vehicles-based Advanced Traffic Management Systems (ATMS) have gained significant research interest due to their high potential. In this study, we examine the concept of ATMS for connected vehicles using a multi-agent systems approach, where both vehicle agents and an intersection management agent can take advantage of real-time traffic information exchange. This dynamic strategy allows an intersection management agent to receive state information from vehicle agents, reserve the associated intersection time-space occupancies, and then provide feedback to the vehicles. The vehicle agents then adjust their trajectories to meet their assigned time slot. Based on preliminary simulation experiments, the proposed strategy can significantly reduce fuel consumption and vehicle emissions compared to traditional signal control systems.

Platoon-based multi-agent intersection management for connected vehicle

As wireless communication advances, multi-agent system (MAS) approaches to intersection traffic management have received increased attention. In this paper, we propose an improved multi-agent intersection management system where vehicle agents may form platoons using connected vehicles technologies. Traffic performance measures in terms of mobility and environmental sustainability were evaluated through a series of simulation studies, along with an analysis of the communication load of the system. Compared to the conventional traffic signal control system, the proposed platoon-based multi-agent intersection management system can shorten the average travel time by as much as 30% and reduce the fuel consumption and carbon dioxide emissions by around 23%, when the traffic volume is high. In comparison with a non-platoon-based multi-agent intersection management system, the proposed system can reduce the communication loads of an intersection management agent by up to 90% and exhibits strong robustness against the variation of traffic volumes.

Power-Based Optimal Longitudinal Control for a Connected Eco-Driving System

Automatic longitudinal control of vehicles is an automobile technology that has been implemented for many years. Connected eco-driving has the potential to extend the capability of an automatic longitudinal control by minimizing the energy consumption and emissions of the vehicle. In this paper, we propose a power-based longitudinal control algorithm for a connected eco-driving system, which takes into account the vehicles brake specific fuel consumption or BSFC map, roadway grade, and other constraints (e.g., traffic condition ahead and traffic signal status of the upcoming intersection) in the calculation of an optimal speed profile in terms of energy savings and emissions reduction. The performance of the proposed algorithm was evaluated through extensive numerical analyses of driving along a signalized arterial, and the results validated the effectiveness of the proposed algorithm as compared with baseline and an existing eco-driving algorithm.

Data-Driven Reinforcement Learning–Based Real-Time Energy Management System for Plug-In Hybrid Electric Vehicles

Plug-in hybrid electric vehicles (PHEVs) show great promise in reducing transportation-related fossil fuel consumption and greenhouse gas emissions. Designing an efficient energy management system (EMS) for PHEVs to achieve better fuel economy has been an active research topic for decades. Most of the advanced systems rely either on a priori knowledge of future driving conditions to achieve the optimal but not real-time solution (e.g., using a dynamic programming strategy) or on only current driving situations to achieve a real-time but nonoptimal solution (e.g., rule-based strategy). This paper proposes a reinforcement learning–based real-time EMS for PHEVs to address the trade-off between real-time performance and optimal energy savings. The proposed model can optimize the power-split control in real time while learning the optimal decisions from historical driving cycles. A case study on a real-world commute trip shows that about a 12% fuel saving can be achieved without considering charging opportunities; further, an 8% fuel saving can be achieved when charging opportunities are considered, compared with the standard binary mode control strategy.

The potential role of vehicle automation in reducing traffic-related energy and emissions

In the last few years, there has been a significant increase of interest related to vehicle automation, even though the fundamental building blocks for automating vehicles have been developed over the last several decades. In parallel, there has been a big push to make vehicles more energy efficient and less polluting, through the development of advanced powertrains, the development and promotion of alternative lower-carbon fuels, better managing vehicle miles traveled, and improving traffic operations. One of the key questions is how can vehicle automation contribute to energy efficiency and reducing emissions. In this paper, we outline some of these potential impacts, examining issues such as vehicle design, vehicle and traffic operations, and even potential changes in activity patterns.

Improving traffic operations using real-time optimal lane selection with connected vehicle technology

To better regulate traffic flow and reduce the potential impacts due to uncoordinated lane changes, we proposed a real-time optimal lane selection (OLS) algorithm by using the information available from connected vehicle (CV) technology. Such information includes the location, speed, lane and desired driving speed of individual vehicle agents (VA) on a localized roadway. Microscopic traffic simulation studies show that the proposed algorithm can result in both mobility and environmental benefits for the entire traffic system. Specifically, the application of the OLS algorithm reduces the average travel time by up to 3.8% and the fuel consumption by around 2.2%. In addition, the reduction in emissions of criteria pollutants, such as CO, HC, NOx and PM2.5 ranges from 1% to 19%, depending on the congestion level of the roadway segment. Potential extensions of the proposed OLS algorithm are discussed at the end of this paper.

Dynamic Lane Grouping at Isolated Intersections: Problem Formulation and Performance Analysis

This paper presents some fundamentals of the dynamic lane grouping (DLG) concept, the aim of which is to improve lane utilization under variations in traffic demand. The problem was formulated as a mathematical programming model to determine the optimal lane allocation of an isolated intersection in relation to minimizing the maximum lane flow ratio (defined as the assigned flow divided by the saturation rate). Shared lanes and user equilibrium status for lane use were also considered in the model. Numerical analyses were conducted for four cases with different numbers of lanes, saturation levels, and methods for setting signal timing (adaptive and fixed timing). The results indicated that DLG might provide significant performance improvement in reducing the maximum-flow ratio under the spatial variation in demand. The performance of DLG was also compared with real-time adaptive signal timing by means of the average delay; improvements to the average delay were also observed when the spatial variation was large. The maximum-flow ratio and the average delay of DLG remained almost unchanged for the full spectrum of spatial demand variation when total demand was constant. The results also implied that the more significantly the demand varied, the more benefits the dynamic lane grouping method could potentially provide.

Developing a framework of Eco-Approach and Departure application for actuated signal control

The Eco-Approach and Departure application for fixed-time traffic signals, which uses the signal phase and timing information from the upcoming traffic signal to better guide a driver through the intersection in an environmentally-friendly way, has shown promising results in terms of fuel savings and carbon emissions reduction. However, there is very limited research on the development and evaluation of such application for actuated traffic signals. This paper proposes a framework for the Eco-Approach and Departure application for actuated signals which takes into account uncertainties in count-down information, preceding vehicles state, and potential drivers distraction issues. The framework has been evaluated with numerical experiments. The results indicated that the proposed framework is effective at reducing energy consumption and emissions of the equipped vehicle, especially when the initial entry speed is relatively low.

Development and evaluation of an enhanced eco-approach traffic signal application for Connected Vehicles

The eco-approach and departure application for traffic signals has shown promising results for fuel and carbon dioxide savings. This application uses the signal phase and timing (SPaT) information from the traffic signal to better guide a driver through the signalized intersection in an environmentally-friendly way. This paper proposes an enhanced eco-approach and departure system which takes into account not only the SPaT information, but also the information from the preceding equipped vehicles using connected vehicle technology. Compared to the previously developed eco-approach algorithm which only utilizes the SPaT information, the proposed system has shown much greater network-wide benefits, especially during higher levels of congestion. The system performance has been evaluated in a series of simulation studies by varying different parameters, including congestion level, penetration rate, communication range, and communication delay.