https://pure.tue.nl/ws/files/3887204/760250.pdf

A review of recent research on green road freight transportation

Power-based electric vehicle energy consumption model: Model development and validation

Connected and automated vehicles (CAV) are marketed for their increased safety, driving comfort, and time saving potential. With much easier access to information, increased processing power, and precision control, they also offer unprecedented opportunities for energy efficient driving. This paper is an attempt to highlight the energy saving potential of connected and automated vehicles based on first principles of motion, optimal control theory, and a review of the vast but scattered eco-driving literature. We explain that connectivity to other vehicles and infrastructure allows better anticipation of upcoming events, such as hills, curves, slow traffic, state of traffic signals, and movement of neighboring vehicles. Automation allows vehicles to adjust their motion more precisely in anticipation of upcoming events, and save energy. Opportunities for cooperative driving could further increase energy efficiency of a group of vehicles by allowing them to move in a coordinated manner. Energy efficient motion of connected and automated vehicles could have a harmonizing effect on mixed traffic, leading to additional energy savings for neighboring vehicles.

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Energy saving potentials of connected and automated vehicles

The research presented in this paper develops a framework to enhance vehicle fuel consumption efficiency while approaching a signalized intersection through the provision of signal phase and timing information that may be available through vehicle-to-infrastructure communication. While past research uses simplified objective functions to optimize fuel consumption and/or emissions caused by signalized intersections, this research highlights the importance of retaining microscopic fuel consumption models in the optimization function. It presents an example which shows that simplified objective functions may result in erroneous conclusions.

Eco-driving at signalized intersections using V2I communication

Driving style, road geometry, and traffic conditions have a significant impact on vehicles' fuel economy. In general, drivers are not aware of the optimal velocity profile for a given route. Indeed, the global optimal velocity trajectory depends on many factors, and its calculation requires intensive computations. In this paper, we discuss the optimization of the speed trajectory to minimize fuel consumption and communicate it to the driver. With this information the driver can adjust his/her speed profile to reduce the overall fuel consumption. We propose to perform the computation-intensive calculations on a distinct computing platform called the “cloud.” In our approach, the driver sends the information of the intended travel destination to the cloud. In the cloud, the server generates a route, collects the associated traffic and geographical information, and solves the optimization problem by a spatial domain dynamic programming (DP) algorithm that utilizes accurate vehicle and fuel consumption models to determine the optimal speed trajectory along the route. Then, the server sends the speed trajectory to the vehicle where it is communicated to the driver. We tested the approach on a prototype vehicle equipped with a visual interface mounted on the dash of a test vehicle. The test results show 5%-15% improvement in fuel economy depending on the driver and route without a significant effect on the travel time. Although this paper implements the speed advisory system in a conventional vehicle, the solution is generic, and it is applicable to any kind of powertrain structure.

Cloud-Based Velocity Profile Optimization for Everyday Driving: A Dynamic-Programming-Based Solution

Existing automobile fuel consumption and emission models suffer from two major drawbacks; they produce a bang–bang control through the use of a linear power model and the calibration of model parameters is not possible using publicly available data thus necessitating in-laboratory or field data collection. This paper develops two fuel consumption models that overcome these two limitations. Specifically, the models do not produce a bang–bang control and are calibrated using US Environmental Protection Agency city and highway fuel economy ratings in addition to publicly available vehicle and roadway pavement parameters. The models are demonstrated to estimate vehicle fuel consumption rates consistent with in-field measurements. In addition the models estimate CO2 emissions that are highly correlated with field measurements.

Virginia Tech Comprehensive Power-Based Fuel Consumption Model: Model Development and Testing

The paper presents the INTEGRATION microscopic traffic assignment and simulation framework for modeling eco-routing strategies. Two eco-routing algorithms are developed: one based on vehicle sub-populations (ECO-Subpopulation Feedback Assignment or ECO-SFA) and another based on individual agents (ECO-Agent Feedback Assignment or ECO-AFA). Both approaches initially assign vehicles based on fuel consumption levels for travel at the facility free-flow speed. Subsequently, fuel consumption estimates are refined based on experiences of other vehicles within the same class. The proposed framework is intended to evaluate the network-wide impacts of eco-routing strategies. This stochastic, multi-class, dynamic traffic assignment framework was demonstrated to work for two scenarios. Savings in fuel consumption levels in the range of 15 percent were observed and potential implementation challenges were identified.

INTEGRATION Framework for Modeling Eco-routing Strategies: Logic and Preliminary Results

A vehicle predictive eco-cruise control system is developed that minimizes vehicle fuel consumption levels utilizing roadway topographic information. The predictive eco-cruise control system consists of three components: a fuel consumption model, a powertrain model, and an optimization algorithm. The developed system generates an optimal vehicle control plan using anticipated roadway grade information so that the vehicle can vary its speed within a preset speed window in a fuel-saving manner. The developed system is tested by simulating a vehicle trip on synthetic roadway profiles and compared to a conventional cruise control system performance. Finally, the potential benefits of the predictive eco-cruise control system are quantified for the entire United States.

Predictive eco-cruise control: Algorithm and potential benefits

A power-based vehicle fuel consumption model, entitled the Virginia Tech Comprehensive Power-based Fuel Consumption Model (VT-CPFM) that was developed in an earlier publication is validated against in-field fuel consumption measurements The study demonstrates that the VT-CPFMs calibrated using the EPA city and highway fuel economy ratings generally provide reliable fuel consumption estimates with a coefficient of determination in the range of 096 More importantly, both estimates and measurements produce very similar behavioral changes depending on engine load conditions The VT-CPFMs are demonstrated to be easily calibrated using publically available data without the need to gather in-field instantaneous data

Virginia Tech Comprehensive Power-based Fuel Consumption Model (VT-CPFM): Model Validation and Calibration Considerations

This research develops a simple vehicle powertrain model that can be incorporated within microscopic traffic simulation software for the modeling of intelligent vehicle applications. This simple model can be calibrated using vehicle parameters that are publically available without the need for field data collection. The model uses the driver throttle level input to compute the engine speed and, subsequently, the engine torque and power to finally compute the vehicle acceleration, speed, and position. The model is tested using field measurements and is demonstrated to produce vehicle power, fuel consumption, acceleration, speed, and position estimates that are consistent with field observations.

Kevin Moran

Papers

Virginia Tech Comprehensive Power-Based Fuel Consumption Model: Model Development and Testing

INTEGRATION Framework for Modeling Eco-routing Strategies: Logic and Preliminary Results

Predictive eco-cruise control: Algorithm and potential benefits

Virginia Tech Comprehensive Power-based Fuel Consumption Model (VT-CPFM): Model Validation and Calibration Considerations

Simple Vehicle Powertrain Model for Modeling Intelligent Vehicle Applications