Fuel-efficient driving strategies

This thesis is concerned with fuel-efficient driving strategies for vehicles driving on roads with varying topography, as well as estimation of road grade\ua0and vehicle mass for vehicles utilizing such strategies. A framework referred\ua0to as speed profile optimization (SPO), is introduced for reducing the fuel\ua0or energy consumption of single vehicles (equipped with either combustion\ua0or electric engines) and platoons of several vehicles. Using the SPO-based\ua0methods, average reductions of 11.5% in fuel consumption for single trucks,\ua07.5 to 12.6% energy savings in electric vehicles, and 15.8 to 17.4% average\ua0fuel consumption reductions for platoons of trucks were obtained. Moreover,\ua0SPO-based methods were shown to achieve higher savings compared to\ua0the commonly used methods for fuel-efficient driving. Furthermore, it was\ua0demonstrated that the simulations are sufficiently accurate to be transferred\ua0to real trucks. In the SPO-based methods, the optimized speed profiles were\ua0generated using a genetic algorithm for which it was demonstrated, in a\ua0discretized case, that it is able to produce speed profiles whose fuel consumption\ua0is within 2% of the theoretical optimum.A feedforward neural network (FFNN) approach, with a simple feedback\ua0mechanism, is introduced and evaluated in simulations, for simultaneous estimation of the road grade and vehicle mass. The FFNN provided road grade\ua0estimates with root mean square (RMS) error of around 0.10 to 0.14 degrees,\ua0as well as vehicle mass estimates with an average RMS error of 1%, relative\ua0to the actual value. The estimates obtained with the FFNN outperform road\ua0grade and mass estimates obtained with other approaches

Reduced Fuel Emissions through Connected Vehicles and Truck Platooning

Vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication enable the sharing, in real time, of vehicular locations and speeds with other vehicles, traffic signals, and traffic control centers. This shared information can help traffic to better traverse intersections, road segments, and congested neighborhoods, thereby reducing travel times, increasing driver safety, generating data for traffic planning, and reducing vehicular pollution. This study, which focuses on vehicular pollution, used an analysis of data from NREL, BTS, and the EPA to determine that the widespread use of V2V-based truck platooning—the convoying of trucks in close proximity to one another so as to reduce air drag across the convoy—could eliminate 37.9 million metric tons of CO2 emissions between 2022 and 2026

Development of Machine Learning based approach to predict fuel consumption and maintenance cost of Heavy-Duty Vehicles using diesel and alternative fuels

One of the major contributors of human-made greenhouse gases (GHG) namely carbon dioxide (CO2), methane (CH4), and nitrous oxide (NOX) in the transportation sector and heavy-duty vehicles (HDV) contributing to about 27% of the overall fraction. In addition to the rapid increase in global temperature, airborne pollutants from diesel vehicles also present a risk to human health. Even a small improvement that could potentially drive energy savings to the century-old mature diesel technology could yield a significant impact on minimizing greenhouse gas emissions. With the increasing focus on reducing emissions and operating costs, there is a need for efficient and effective methods to predict fuel consumption, maintenance costs, and total cost of ownership for heavy-duty vehicles. Every improvement so achieved in this direction is a direct contributor to driving the reduction in the total cost of ownership for a fleet owner, thereby bringing economic prosperity and reducing oil imports for the economy. Motivated by these crucial goals, the present research considers integrating data-driven techniques using machine learning algorithms on the historical data collected from medium- and heavy-duty vehicles. The primary motivation for this research is to address the challenges faced by the medium- and heavy-duty transportation industry in reducing emissions and operating costs. The development of a machine learning-based approach can provide a more accurate and reliable prediction of fuel consumption and maintenance costs for medium- and heavy-duty vehicles. This, in turn, can help fleet owners and operators to make informed decisions related to fuel type, route planning, and vehicle maintenance, leading to reduced emissions and lower operating costs. Artificial Intelligence (AI) in the automotive industry has witnessed massive growth in the last few years. Heavy-duty transportation research and commercial fleets are adopting machine learning (ML) techniques for applications such as autonomous driving, fuel economy/emissions, predictive maintenance, etc. However, to perform well, modern AI methods require a large amount of high-quality, diverse, and well-balanced data, something which is still not widely available in the automotive industry, especially in the division of medium- and heavy-duty trucks. The research methodology involves the collection of data at the West Virginia University (WVU) Center for Alternative Fuels, Engines, and Emissions (CAFEE) lab in collaboration with fleet management companies operating medium- and heavy-duty vehicles on diesel and alternative fuels, including compressed natural gas, liquefied propane gas, hydrogen fuel cells, and electric vehicles. The data collected is used to develop machine learning models that can accurately predict fuel consumption and maintenance costs based on various parameters such as vehicle weight, speed, route, fuel type, and engine type. The expected outcomes of this research include 1) the development of a neural network model 3 that can accurately predict the fuel consumed by a vehicle per trip given the parameters such as vehicle speed, engine speed, and engine load, and 2) the development of machine learning models for estimating the average cost-per-mile based on the historical maintenance data of goods movement trucks, delivery trucks, school buses, transit buses, refuse trucks, and vocational trucks using fuels such as diesel, natural gas, and propane. Due to large variations in maintenance data for vehicles performing various activities and using different fuel types, the regular machine learning or ensemble models do not generalize well. Hence, a mixed-effect random forest (MERF) is developed to capture the fixed and random effects that occur due to varying duty-cycle of vocational heavy-duty trucks that perform different tasks. The developed model helps in predicting the average maintenance cost given the vocation, fuel type, and region of operation, making it easy for fleet companies to make procurement decisions based on their requirement and total cost of ownership. Both the models can provide insights into the impact of various parameters and route planning on the total cost of ownership affected by the fuel cost and the maintenance and repairs cost. In conclusion, the development of a machine learning-based approach can provide a reliable and efficient solution to predict fuel consumption and maintenance costs impacting the total cost of ownership for heavy-duty vehicles. This, in turn, can help the transportation industry reduce emissions and operating costs, contributing to a more sustainable and efficient transportation system. These models can be optimized with more training data and deployed in a real-time environment such as cloud service or an onboard vehicle system as per the requirement of companies

Impacts of Connected and Automated Vehicles on Energy and Traffic Flow: Optimal Control Design and Verification Through Field Testing

This dissertation assesses eco-driving effectiveness in several key traffic scenarios that include passenger vehicle transportation in highway driving and urban driving that also includes interactions with traffic signals, as well as heavy-duty line-haul truck transportation in highway driving with significant road grade. These studies are accomplished through both traffic microsimulation that propagates individual vehicle interactions to synthesize large-scale traffic patterns that emerge from the eco-driving strategies, and through experimentation in which real prototyped connected and automated vehicles (CAVs) are utilized to directly measure energy benefits from the designed eco-driving control strategies. In particular, vehicle-in-the-loop is leveraged for the CAVs driven on a physical test track to interact with surrounding traffic that is virtually realized through said microsimulation software in real time. In doing so, model predictive control is designed and implemented to create performative eco-driving policies and to select vehicle lane, as well as enforce safety constraints while autonomously driving a real vehicle. Ultimately, eco-driving policies are both simulated and experimentally vetted in a variety of typical driving scenarios to show up to a 50% boost in fuel economy when switching to CAV drivers without compromising traffic flow. The first part of this dissertation specifically assesses energy efficiency of connected and automated passenger vehicles that exploit intention-sharing sourced from both neighboring vehicles in a highway scene and from traffic lights in an urban scene. Linear model predictive control is implemented for CAV motion planning, whereby chance constraints are introduced to balance between traffic compactness and safety, and integer decision variables are introduced for lane selection and collision avoidance in multi-lane environments. Validation results are shown from both large-scale microsimulation and through experimentation of real prototyped CAVs. The second part of this dissertation then assesses energy efficiency of automated line-haul trucks when tasked to aerodynamically platoon. Nonlinear model predictive control is implemented for motion planning, and simulation and experimentation are conducted for platooning verification under highway conditions with traffic. Then, interaction-aware and intention-sharing cooperative control is further introduced to eliminate experimentally measured platoon disengagements that occur on real highways when using only status-sharing control. Finally, the performance of automated drivers versus human drivers are compared in a point-to-point scenario to verify fundamental eco-driving impacts -- experimentally showing eco-driving to boost energy economy by 11% on average even in simple driving scenarios

Cooperative Adaptive Cruise Control Based on Reinforcement Learning for Heavy-Duty BEVs

Advanced driver assistance systems (ADAS) are playing an increasingly important role in supporting the driver to create safer and more efficient driving conditions. Among all ADAS, adaptive cruise control (ACC) is a system that provides consistent aid, especially in highway mobility, guaranteeing safety by minimizing the possible risk of collision due to variations in the speed of the vehicle in front, automatically adjusting the vehicle velocity and maintaining the correct spacing. Theoretically, this type of system also makes it possible to optimize road throughput, increasing its capacity and reducing traffic congestion. However, it was found in practice that the current generation of ACC systems does not guarantee the so-called string stability of a vehicle platoon and can therefore lead to an actual decrease in traffic capacity. To overcome these issues, new cooperative adaptive cruise control (CACC) systems are being proposed that exploit vehicle-to-vehicle (V2V) connectivity, which can provide additional safety and robustness guarantees and introduce the possibility of concretely improving traffic flow stability

An Enhanced Predictive Cruise Control System Design with Data-Driven Traffic Prediction

The predictive cruise control (PCC) is a promising method to optimize energy consumption of vehicles, especially the heavy-duty vehicles (HDV). Due to the limited sensing range and computational capabilities available on-board, the conventional PCC system can only obtain a sub-optimal speed trajectory based on a shorter prediction horizon. The recently emerging information and communication technologies such as vehicular communication, cloud computing, and Internet of Things provide huge potentials to improve the traditional PCC system. In this paper, we propose a general framework for the enhanced cloud-based PCC system which integrates a data-driven traffic predictive model and the instantaneous control algorithms. Specifically, we introduce a novel multi-view CNN deep learning algorithm to predict traffic situation based on the historical and real-time traffic data collected from fields, and the time-varying adaptive model predictive control (MPC) to calculate the instantaneous optimal speed profile with the aim of minimizing energy consumption. We verified our approach via simulations in which the impact of various traffic condition on the PCC-enabled HDV has been fully evaluated

Leveraging Connected Highway Vehicle Platooning Technology to Improve the Efficiency and Effectiveness of Train Fleeting Under Moving Blocks

Future advanced Positive Train Control systems may allow North American railroads to introduce moving blocks with shorter train headways. This research examines how closely following trains respond to different throttle and brake inputs. Using insights from connected automobile and truck platooning technology, six different following train control algorithms were developed, analyzed for stability, and evaluated with simulated fleets of freight trains. While moving blocks require additional train spacing beyond minimum safe braking distance to account for train control actions, certain following train algorithms can help minimize this distance and balance fuel efficiency and train headway by changing control parameters

Energy-Efficient and Semi-automated Truck Platooning

This open access book presents research and evaluation results of the Austrian flagship project “Connecting Austria,” illustrating the wide range of research needs and questions that arise when semi-automated truck platooning is deployed in Austria. The work presented is introduced in the context of work in similar research areas around the world. This interdisciplinary research effort considers aspects of engineering, road-vehicle and infrastructure technologies, traffic management and optimization, traffic safety, and psychology, as well as potential economic effects. The book’s broad perspective means that readers interested in current and state-of-the-art methods and techniques for the realization of semi-automated driving and with either an engineering background or with a less technical background gain a comprehensive picture of this important subject. The contributors address many questions such as: Which maneuvers does a platoon typically have to carry out, and how? How can platoons be integrated seamlessly in the traffic flow without becoming an obstacle to individual road users? What trade-offs between system information (sensors, communication effort, etc.) and efficiency are realistic? How can intersections be passed by a platoon in an intelligent fashion? Consideration of diverse disciplines and highlighting their meaning for semi-automated truck platooning, together with the highlighting of necessary research and evaluation patterns to address such a broad task scientifically, makes Energy-Efficient and Semi-automated Truck Platooning a unique contribution with methods that can be extended and adapted beyond the geographical area of the research reported

Platooning-based control techniques in transportation and logistic

This thesis explores the integration of autonomous vehicle technology with smart manufacturing systems. At first, essential control methods for autonomous vehicles, including Linear Matrix Inequalities (LMIs), Linear Quadratic Regulation (LQR)/Linear Quadratic Tracking (LQT), PID controllers, and dynamic control logic via flowcharts, are examined. These techniques are adapted for platooning to enhance coordination, safety, and efficiency within vehicle fleets, and various scenarios are analyzed to confirm their effectiveness in achieving predetermined performance goals such as inter-vehicle distance and fuel consumption. A first approach on simplified hardware, yet realistic to model the vehicle's behavior, is treated to further prove the theoretical results. Subsequently, performance improvement in smart manufacturing systems (SMS) is treated. The focus is placed on offline and online scheduling techniques exploiting Mixed Integer Linear Programming (MILP) to model the shop floor and Model Predictive Control (MPC) to adapt scheduling to unforeseen events, in order to understand how optimization algorithms and decision-making frameworks can transform resource allocation and production processes, ultimately improving manufacturing efficiency. In the final part of the work, platooning techniques are employed within SMS. Autonomous Guided Vehicles (AGVs) are reimagined as autonomous vehicles, grouping them within platoon formations according to different criteria, and controlled to avoid collisions while carrying out production orders. This strategic integration applies platooning principles to transform AGV logistics within the SMS. The impact of AGV platooning on key performance metrics, such as makespan, is devised, providing insights into optimizing manufacturing processes. Throughout this work, various research fields are examined, with intersecting future technologies from precise control in autonomous vehicles to the coordination of manufacturing resources. This thesis provides a comprehensive view of how optimization and automation can reshape efficiency and productivity not only in the domain of autonomous vehicles but also in manufacturing