Traffic-Aware Ecological Cruising Control for Connected Electric Vehicle

The advent of intelligent connected technology has greatly enriched the capabilities of vehicles in acquiring information. The integration of short-term information from limited sensing range and long-term information from cloud-based systems in vehicle motion planning and control has become a vital means to deeply explore the energy-saving potential of vehicles. In this study, a traffic-aware ecological cruising control (T-ECC) strategy based on a hierarchical framework for connected electric vehicles in uncertain traffic environments is proposed, leveraging the two distinct temporal-dimension information. In the upper layer that is dedicated for speed planning, a sustainable energy consumption strategy (SECS) is introduced for the first time. It finds the optimal economic speed by converting variations in kinetic energy into equivalent battery energy consumption based on long-term road information. In the lower layer, a synthetic rolling-horizon optimization control (SROC) is developed to handle real-time traffic uncertainties. This control approach jointly optimizes energy efficiency, battery life, driving safety, and comfort for vehicles under dynamically changing traffic conditions. Notably, a stochastic preceding vehicle model is presented to effectively capture the uncertainties in traffic during the driving process. Finally, the proposed T-ECC is validated through simulations in both virtual and real-world driving conditions. Results demonstrate that the proposed strategy significantly improves the energy efficiency of the vehicle

Fuel Efficient Connected Cruise Control for Heavy-Duty Trucks in Real Traffic

In this paper, we present a systematic approach for fuel-economy optimization of a connected automated truck that utilizes motion information from multiple vehicles ahead via vehicle-to-vehicle (V2V) communication. Position and velocity data collected from a chain of human-driven vehicles are utilized to design a connected cruise controller that smoothly responds to traffic perturbations while maximizing energy efficiency. The proposed design is evaluated using a high-fidelity truck model and the robustness of the design is validated on real traffic data sets. It is shown that optimally utilizing V2V connectivity leads to around 10% fuel economy improvements compared to the best nonconnected design

Fuel Efficient Connected Cruise Control for Heavy-Duty Trucks in Real Traffic

In this paper, we present a systematic approach for fuel-economy optimization of a connected automated truck that utilizes motion information from multiple vehicles ahead via vehicle-to-vehicle (V2V) communication. Position and velocity data collected from a chain of human-driven vehicles are utilized to design a connected cruise controller that smoothly responds to traffic perturbations while maximizing energy efficiency. The proposed design is evaluated using a high-fidelity truck model and the robustness of the design is validated on real traffic data sets. It is shown that optimally utilizing V2V connectivity leads to around 10% fuel economy improvements compared to the best nonconnected design

Saving Fuel for Heavy-Duty Vehicles Using Connectivity and Automation

The booming of e-commerce is placing an increasing burden on freight transport system by demanding faster and larger amount of delivery. Despite the variety in freight transport means, the dominant freight transport method is still ground transport, or specifically, transport by heavy-duty vehicles. Roughly one-third of the annual ground freight transport expense goes to fuel expenses. If fuel costs could be reduced, the finance of freight transport would be improved and may increase the transport volume without additional charge to average consumers. A further benefit of reducing fuel consumption would be the related environmental impact. The fuel consumption of the heavy-duty vehicles, despite being the minority of road vehicles, has a major influence on the whole transportation sector, which is a major contributor to greenhouse gas emissions. Thus, saving fuel for heavy-duty trucks would also reduce greenhouse gas emission, leading to environmental benefits. For decades, researchers and engineers have been seeking to improve the fuel economy of heavy-duty vehicles by focusing on vehicles themselves, working on advancing the vehicle design in many aspects. More recently, attention has turned to improve fuel efficiency while driving in the dynamic traffic environment. Fuel savings effort may be realized due to advancements in connected and automated vehicle technologies, which provide more information for vehicle design and control. This dissertation presents state-of-the-art techniques that utilize connectivity and automation to improve the fuel economy of heavy-duty vehicles, while allowing them to stay safe in real-world traffic environments. These techniques focus on three different levels of vehicle control, and can result in significant fuel improvements at each level. Starting at the powertrain level, a gear shift schedule design approach is proposed based on hybrid system theory. The resulting design improves fuel economy without comprising driveability. This new approach also unifies the gear shift logic design of human-driven and automated vehicles, and shows a large potential in fuel saving when enhanced with higher level connectivity and automation. With this potential in mind, at the vehicle level, a fuel-efficient predictive cruise control algorithm is presented. This mechanism takes into account road elevation, wind, and aggregated traffic information acquired via connectivity. Moreover, a systematic tool to tune the optimization parameters to prioritize different objectives is developed. While the algorithm and the tool are shown to be beneficial for heavy-duty vehicles when they are in mild traffic, such benefits may not be attainable when the traffic is dense. Thus, at the traffic level, when a heavy-duty vehicle needs to interact with surrounding vehicles in dense traffic, a connected cruise control algorithm is proposed. This algorithm utilizes beyond-line-of-sight information, acquired through vehicle-to-vehicle communication, to gain a better understanding of the surrounding traffic so that the vehicle can response to traffic in a fuel efficient way. These techniques can bring substantial fuel economy improvements when applied individually. In practice, it is important to integrate these three techniques at different levels in a safe manner, so as to acquire the overall benefits. To achieve this, a safety verification method is developed for the connected cruise control, to coordinate the algorithms at the vehicle level and the traffic level, maximizing the fuel benefits while staying safe.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147523/1/hchaozhe_1.pd

Development and implementation of an adaptive cruise control system with a customized engine control unit / Desenvolvimento e implementação de um sistema de controle de cruzeiro adaptativo com uma unidade de controlo de motor personalizada

This paper describes the development of an adaptive cruise control system (ACC), a driving assistant system, whose purpose is to control the vehicle’s speed, determined by a setpoint inputted by the driver. Simultaneously, the system must also monitor the environment and adjust the car’s velocity in order to maintain a safety distance from the other vehicles driving on the lane. This system’s main purpose is to increase the driver’s comfort, thus making it so he will no longer be required to control the throttle pedal and therefore be able to focus on the other tasks involved in driving

Automatic Code Generation of Real-Time Nonlinear Model Predictive Control for Plug-in Hybrid Electric Vehicle Intelligent Cruise Controllers

Control systems have always been a vital part of the novel technological advancements of human being in any industry, especially transportation. With the introduction of the idea of autonomous driving, classical control systems are not effective anymore and the need for intelligent control systems is inevitable. Advanced Driver Assistance Systems (ADASs), which are systems proposed to help drivers improve the process of driving, and Intelligent Transportation Systems (ITS), which are proposed to provide information that promotes more coordinated and more ecological driving, require novel intelligent controllers that are adaptive to driving conditions. Therefore, the development of different strategic vehicle control systems by employing state-of-the-art intelligent control methods has been an active field of research in recent years. The highly variant nature of transportation implies that an effective intelligent control technique must be able to handle a large multi-input multi-output (MIMO) system with nonlinear complex dynamics. It must also store and analyse a large amount of data and information about the vehicle, its environment and traffic conditions in the process of decision-making. Nonlinear Model Predictive Control (NMPC), as a unique optimal model-based approach to intelligent control systems design, is a promising candidate that comprises all of these characteristics. The ability to solve constrained multi-objective optimization problems with a predictive approach has made this technique powerful. However, NMPC controller developers face real-time implementation challenges as this method suffers from huge computational loads. Hence, fast Real-Time Optimization (RTO) methods are proposed to overcome this drawback. Optimization methods based on Generalized Minimum Residual (GMRES) method are examples of these RTO algorithms that have shown great potential for real-time applications such as vehicle control. This thesis investigates the potential of employing GMRES-based RTO algorithms to design intelligent vehicle control systems, in particular intelligent cruise controllers. Plug-in Hybrid Electric vehicles (PHEVs) are introducing themselves as the future solutions for green and ecological transportation, the thesis also introduces an intelligent cruise controller for the Toyota Prius 2013 PHEV. To this end, an automatic multi-solver NMPC code generator based on GMRES-based RTO algorithms is developed in MATLAB. The user-friendly environment of this code generation tool allows the user to easily generate NMPC controller codes for further model-in-the-loop (MIL) and hardware-in-the-loop (HIL) simulations. Simulations are performed for two different driving scenarios: driving on hilly roads and a car-following scenario. In the case of driving on hilly roads, a comparative study is conducted between different real-time optimizers and it is concluded that the Newton/GMRES algorithm is faster than the Continuation/GMRES algorithm. A novel adaptive prediction horizon length approach is also developed to enhance the performance of the NMPC controller. Simulation results demonstrate a minimum of 3.4% energy consumption improvement as compared to a PID controller performance as well as improvement of reference speed tracking when using an adaptive prediction horizon length. In case of the car-following scenario, the effect of several tuning parameters and adaptive gains on the performance of the proposed NMPC controller is studied. Then the ecological adaptive cruise controller was tested on urban and highway driving cycles, and resulted in 3.4% and 1.2%, respectively, improvement in the cost of the trip. Finally, the proposed NMPC controllers for both intelligent cruise control systems are tested on an HIL platform for rapid control prototyping. The HIL results on a dSPACE prototype Electronic Control Unit (ECU) indicate that the real-time optimizers and the proposed NMPC controllers are fast enough to be implementable on an actual ECU for a certain range of prediction horizon sizes

ENERGY CONSUMPTION AND SAVINGS ANALYSIS OF A PHEV IN REAL WORLD DRIVING THROUGH VEHICLE CONNECTIVITY USING VEHICLE PLATOONING, BLENDED MODE OPERATION AND ENGINE START-STOP OPTIMIZERS

This report presents an analysis on energy consumption of a Gen II Chevrolet Volt PHEV and its energy savings potential in Real World Driving scenarios with the help of vehicle connectivity. The research on the energy consumption analysis and optimization using connectivity will focus on four main areas of contribution which includes 1.) vehicle testing on a pre-defined drive cycle and alternative routing near the Michigan Tech campus and APS research center that is a continuation of previous students\u27 works, 2) the energy savings potential of vehicle platooning and various vehicle platoon configurations, 3) the updating of a PHEV implementation of a charge depleting-charge sustaining energy blending optimization algorithm and 4) the development of an IC Engine start-stop prediction algorithm for HEV and PHEV\u27s using connectivity data. The first part of the report discusses the development of a Real World Drive Cycle called Reverse MTU Drive Cycle which is the successor of MTU Drive Cycle, a drive cycle previously developed local to the Michigan Technological University. The energy consumption of the PHEV on the R-MTUDC is analyzed and the baseline characteristics of the drive cycle is setup. A set of baseline drive cycle characteristics was developed and tests on the drive cycle proved that the energy consumption on the real-world drive route is consistent with variability less than 3%. The next part of the report investigates the energy savings potential of the cars when they are traveling in a platoon rather than independently. Various tests have been conducted to investigate energy savings under different platoon scenarios, like variable gap settings, variable speeds, inclusion of a vehicle with aero-modifier and effect of moving collinearly in a platoon. A platoon wide savings as high as 8.3% was achieved in the study. After that, the report discusses the on-road implementation of a Route Based Blended Mode Optimizer, in PHEVs, which comes up with an optimal control matrix using Dynamic Programming and Cost-To-Go matrix, to make use of the Hold mode capability of the Volts, to operate the cars in Charge Sustaining mode at sections of Drive Cycles where it is most efficient to be operated. Upto, 5% savings in energy was obtained using the optimizer. Some of the runs didn\u27t provide the desired results and this is also investigated. Finally, the report presents the development of two kinds of Engine Start-Stop Optimizers, which utilizes vehicle connectivity and vehicle energy consumption model to come up with an optimal control map of regions on the predicted driving route where the engine should be turned On and Off for minimizing energy consumption in HEVs and PHEVs. The first optimizer uses vehicle and route characteristics to predict engine starts and stops and then optimizes these signals based on decisions made from energy calculations. The second optimizer uses Dynamic Programming to create a matrix of engine On and Off signals based on the route characteristics. These controllers are shown to provide energy savings as high as 8% on some routes

Traffic microsimulation of Autonomous Vehicles Flow in Ronda de Dalt of Barcelona

This final degree project consists on the validation of an autonomous vehicle controller on a section of the Ronda de Dalt road in Barcelona, using the Aimsun Next traffic simulation software. In order to achieve this goal, two prerequisites are necessary. Firstly, a controller, in this case designed by a research group at the UPC. This controller has been implemented using C++ (one of the programming languages accepted by Aimsun Next). Secondly, a real simulation model, where the study of the controller will be carried out. In this case, the Ronda de Dalt model has been created by obtaining and analyzing real data from public sources and calibrated by comparison with simulated data. Once both prerequisites were fulfilled, a series of tests were carried out to validate the controller beforehand in isolated simulation models to check its efficiency and correct performance. Finally, its impact in the previously mention real simulation model, that is on a section of Ronda de Dalt road, was studied. The main objective of this study is to observe and analyze the impact of the introduction of autonomous vehicle (controlled by that specific controller) would have on real traffic condition

Eco-Driving Optimization Based on Variable Grid Dynamic Programming and Vehicle Connectivity in a Real-World Scenario

In a context in which the connectivity level of last-generation vehicles is constantly onthe rise, the combined use of Vehicle-To-Everything (V2X) connectivity and autonomous drivingcan provide remarkable benefits through the synergistic optimization of the route and the speedtrajectory. In this framework, this paper focuses on vehicle ecodriving optimization in a connectedenvironment: the virtual test rig of a premium segment passenger car was used for generatingthe simulation scenarios and to assess the benefits, in terms of energy and time savings, that theintroduction of V2X communication, integrated with cloud computing, can have in a real-worldscenario. The Reference Scenario is a predefined Real Driving Emissions (RDE) compliant route,while the simulation scenarios were generated by assuming two different penetration levels of V2Xtechnologies. The associated energy minimization problem was formulated and solved by means of aVariable Grid Dynamic Programming (VGDP), that modifying the variable state search grid on thebasis of the V2X information allows to drastically reduce the DP computation burden by more than95%. The simulations show that introducing a smart infrastructure along with optimizing the vehiclespeed in a real-world urban route can potentially reduce the required energy by 54% while shorteningthe travel time by 38%. Finally, a sensitivity analysis was performed on the biobjective optimizationcost function to find a set of Pareto optimal solutions, between energy and travel time minimization