Navigation, Guidance, Control, and Dynamics Commons^™

Multi-Level Energy Optimization For Connected And Automated Vehicles: From Cooperative Multi-Vehicle Control To Individual Powertrain Management, Pruthwiraj Santhosh

Dissertations, Master's Theses and Master's Reports

The transportation sector currently accounts for nearly 30% of global energy consumption, necessitating urgent advancements in vehicle efficiency to meet Net Zero targets. Leveraging connectivity and automation, this dissertation proposes and validates methodologies to reduce the energy consumption of light-duty vehicles at both fleet and individual levels.

First, a validation framework is developed to bridge the “simulation-to-real world” gap in Cooperative Automated Vehicle (CAV) research. Moving beyond virtual simulations, the study establishes a methodology for physically validating centralized control architectures via a custom Cellular V2X network. By synchronizing vehicle-powertrain models with physical test vehicles, the framework successfully orchestrates complex arterial …

Development And Verification Of Testing Platform For Torque Vectoring Controller, Nicholas Petersen

Dissertations, Master's Theses and Master's Reports

A foremost concept in the automotive industry in recent years has been that of a vehicle “digital twin”. A digital twin is an accurate simulation of a dynamic system for the use of rapid development. These digital twins have a critical advantage over physical testing, which have dominated vehicle development up to now. As the cost of physical testing continues to rise, simulation can deliver rapid system development in a low-cost format. No simulation environment is a perfect representation of reality, however, and physical testing is still necessary to validate systems for use in the real world. This is especially …

Intelligent Systems For Synergistic Optimization Of Vehicle Dynamics And Energy Consumption In Connected And Automated Vehicles, Aman Poovalappil

Dissertations, Master's Theses and Master's Reports

This dissertation focuses on the development and validation of on-board, real-time estimation algorithms and the investigation of energy-saving strategies for modern light-duty vehicles, including connected and automated vehicles (CAVs). The accurate estimation of key vehicle parameters is critical for enhancing energy efficiency, enabling advanced driver-assistance systems (ADAS), and optimizing vehicle performance and energy efficiency. First, this research introduces a novel real-time algorithm to dynamically learn vehicle mass using readily available sensor data. Based on longitudinal vehicle dynamics, a sensitivity analysis was conducted to identify the conditions under which mass estimation is most robust. The algorithm was extensively validated through on-road …

V2i-Based Adaptive Collision Avoidance For Safety And Traffic Efficiency, Vaishnavi Balambeed

Dissertations, Master's Theses and Master's Reports

To leverage the growing communication and connectivity among modern vehicles, Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) systems are increasingly being used to implement active safety applications. However, current research often overlooks the impact of algorithms such as collision avoidance on traffic flow efficiency. This work investigates the adaptation of a collision avoidance algorithm implemented in V2I to incorporate a variable time headway and spacing control strategy. The proposed approach aims to maintain higher average speeds among vehicles, lower individual vehicle’s waiting, and travel times in the vicinity of the infrastructural unit while simultaneously avoiding collisions; thereby enhancing both safety and traffic …

Study Of Eco-Driving And Charging Planning In Connected And Automated Vehicles Environment, Pradeep Bhat

Dissertations, Master's Theses and Master's Reports

In this dissertation, the development of eco-driving and charging planning algorithms in the connected and automated vehicle environment (CAV) are presented. CAV technologies provide opportunities for potential energy savings and efficiency improvement of transportation networks, which are explored through multiple research tasks in this study.

The objective of the first study presented in Chapter 2 is to reduce vehicle dynamic losses and required tractive force while completing trip distance within a given travel time. Sequential Quadratic Programming method is employed for this nonlinearly constrained optimization problem. The validation result illustrates the benefits of optimal velocity trajectories. The objective of the …

Neuroevolution And Machine Learning Research Applied To Connected Automated Vehicle And Powertrain Control, Frédéric F. Jacquelin

Dissertations, Master's Theses and Master's Reports

This dissertation focuses on advancing Predictive Energy Management (PrEM) functions applied to modern connected and automated vehicles (CAV) cohorts. PrEM aims to utilize connectivity and ADAS functions to adaptively minimize vehicle energy consumption in a wide array of operations, extending the original control designed around a reduced set of test cycle procedures to adapt to real-world stochastic operating conditions. This research document is built upon three journal publications covering two PrEM schemes; the global cohort and local vehicle optimization paths. Both optimal control solutions are generated using various Neuroevolution centric processes.

Chapter 1 discusses the methods and reasoning behind the …

Experimental Evaluation And Simulation Of Torque Transmissibility Frequency Response Functions Of Vibration Isolators And Absorbers For Drivetrain Applications, Luke Jurmu

Dissertations, Master's Theses and Master's Reports

Four studies involving torsional vibration isolation performance of automotive drivetrain components, make up this dissertation. One study features a prototype planetary torsional vibration absorber, a unique device that targets low frequency torsion modes in automotive drivetrains. Two studies feature experiments on several torque converters, clutch locked and open, to validate models of the hardware. The last study details experiments on a centrifugal pendulum absorber in a torque converter, to characterize the viscous friction while submerged in automatic transmission fluid (ATF). The enclosed studies improve the state of the art of drivetrain vibration absorbers and isolators, by introducing a new vibration …

Development And Validation Of Dynamic Programming Algorithm For Eco Approach And Departure, Vasu Goyal

Dissertations, Master's Theses and Master's Reports

Eco Approach and Departure (Eco-AnD) is a Connected and Automated Vehicle (CAV) technology developed to reduce energy for crossing a signalized intersection or set of intersections in a corridor that features vehicle to infrastructure (V2I) communication capability. Eco-AnD technology uses the information of the signal phase and timings (SPaT) received from the V2I communication to optimize the vehicle’s speed profile and produce an energy-efficient maneuver to cross the intersection.

The Eco-AnD algorithm is devised for two vehicles (GM-Volt Gen II & GM-Bolt), both with different powertrain architectures but capable of working in electric-only mode. In simulations, the developed algorithm showed …

Development Of Autonomous Vehicle Motion Planning And Control Algorithm With D* Planner And Model Predictive Control In A Dynamic Environment, Somnath Mondal

Dissertations, Master's Theses and Master's Reports

The research in this report incorporates the improvement in the autonomous driving capability of self-driving cars in a dynamic environment. Global and local path planning are implemented using the D* path planning algorithm with a combined Cubic B-Spline trajectory generator, which generates an optimal obstacle free trajectory for the vehicle to follow and avoid collision. Model Predictive Control (MPC) is used for the longitudinal and the lateral control of the vehicle. The presented motion planning and control algorithm is tested using Model-In-the-Loop (MIL) method with the help of MATLAB® Driving Scenario Designer and Unreal Engine® Simulator by Epic Games®. Different …

Development Of An Eco Approach And Departure Application To Improve Energy Consumption Of A Plug-In Hybrid Vehicle In Charge Depleting Mode, Brandon Narodzonek

Dissertations, Master's Theses and Master's Reports

A recent study at Michigan Technological University as part of the NEXTCAR DOE APRA-E Project was conducted to determine the potential energy savings of a plug-in hybrid electric vehicle (PHEV) equipped with various Connected and Automated Vehicle (CAV) Technologies. One aspect of this study focused on the development of an Eco Approach and Departure (Eco AnD) Application that would further reduce the energy consumed around a signalized intersection.

Many modern intersections are equipped with traffic signals that can broadcast Basic Safety (BSM), MAP, and Signal Phase and Timing (SPaT) message sets that contain intersection ID, location, current phase, and cyclic …

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, Pruthwiraj Santhosh

Dissertations, Master's Theses and Master's Reports

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' works, 2) the energy savings potential of vehicle platooning and various vehicle platoon configurations, 3) the updating of a PHEV implementation …

Hybrid Electric Vehicle Energy Management Strategy With Consideration Of Battery Aging, Bin Zhou

Dissertations, Master's Theses and Master's Reports

The equivalent consumption minimization strategy (ECMS) is a well-known energy management strategy for Hybrid Electric Vehicles (HEV). ECMS is very computationally efficient since it yields an instantaneous optimal control. ECMS has been shown to minimize fuel consumption under certain conditions. But, minimizing the fuel consumption often leads to excessive battery damage. The objective of this dissertation is to develop a real-time implementable optimal energy management strategy which improves both the fuel economy and battery aging for Hybrid Electric Vehicles by using ECMS. This work introduces a new optimal control problem where the cost function includes terms for both fuel consumption …

Sensor Fusion And Non-Linear Mpc Controller Development Studies For Intelligent Autonomous Vehicular Systems, Ahammad Basha Dudekula

Dissertations, Master's Theses and Master's Reports

The demand for safety and fuel efficiency on ground vehicles and advancement in embedded systems created the opportunity to develop Autonomous controller. The present thesis work is three fold and it encompasses all elements that are required to prototype the autonomous intelligent system including simulation, state handling and real time implementation. The Autonomous vehicle operation is mainly dependent upon accurate state estimation and thus a major concern of implementing the autonomous navigation is obtaining robust and accurate data from sensors. This is especially true, in case of Inertial Measurement Unit (IMU) sensor data. The IMU consists of a 3-axis gyro, …

Vehicle Dynamics Modeling For Autonomous Drifting And Clothoid Based Waypoint Interpolation, Shubham Vatsa

Dissertations, Master's Theses and Master's Reports

The advent of autonomous vehicles necessitates a redefinition of road safety regulations, considering a controller can possess better driving skills than an average person. The work presented here partly focuses on a vehicle dynamics model development to help imitate and control vehicle drifting maneuvers. As we see, a professional driver drifting through the traffic while keeping the car safe, it can be utilized to avoid accidents at high speeds, if required. Although drifting can produce higher yaw rates than the regular driving regime, these control capabilities have not yet been exploited in the current automotive control systems. Therefore, this report …

Application Of Sensor Fusion For Si Engine Diagnostics And Combustion Feedback, Fnu Muralidhar Nischal

Dissertations, Master's Theses and Master's Reports

Shifting consumer mindsets and evolving government norms are forcing automotive manufacturers the world over to improve vehicle performance and also reduce greenhouse gas emissions. A critical aspect of achieving future fuel economy and emission targets is improved powertrain control and diagnostics.

This study focuses on using a sensor fusion based approach to improving control and diagnostics in a gasoline engine. A four cylinder turbocharged engine was instrumented with a suite of sensors including ion sensors, exhaust pressure sensors, crank position sensors and accelerometers. The diagnostic potential of these sensors was studied in detail. The ability of these sensors to detect …

Effect Of Sensor Errors On Autonomous Steering Control And Application Of Sensor Fusion For Robust Navigation, Shuvodeep Bhattacharjya

Dissertations, Master's Theses and Master's Reports

Autonomous steering control is one the most important features in autonomous vehicle navigation. The nature and tuning of the controller decides how well the vehicle follows a defined trajectory. A poorly tuned controller can cause the vehicle to oversteer or understeer at turns leading to deviation from a defined path. However, controller performance also depends on the state–feedback system. If the states used for controller input are noisy or has bias / systematic error, the navigation performance of the vehicle is affected irrespective of the control law and controller tuning. In this report, autonomous steering controller analysis is done for …

Modeling Chevy Volt Gen Ii Supervisory Controller In Charge Sustaining Operation, Saurabh Bhasme

Dissertations, Master's Theses and Master's Reports

This report is focused on the development of Chevy Volt Gen II powertrain supervisory controller modeling for charge sustaining operation of the vehicle. The modeling process incorporated vehicle parameters and maps provided. The overall powertrain model along with the supervisory controller is developed in MAT- LAB/SIMULINK programming platform. The powertrain model includes all components which are the IC engine, two electric motors & associated TPIM, Battery, transmission auxiliary pump and spin-losses. The supervisory controller includes the vehicle drive mode selection model, the torque blending logic for charge sustaining along with friction brake modeling. The model has been developed to perform …

Automotive Driveline Backlash State And Size Estimator Design For Anti-Jerk Control, Kaushal Kumar Darokar

Dissertations, Master's Theses and Master's Reports

Vehicle drivability is an important factor which more and more customers have started assessing before buying a vehicle. Customers carry out this assessment based on both vehicle reviews/ratings and based on the test drives. One of common maneuver which a customers perform during the test drive is sudden accelerator pedal tip-in or tip-out to accelerate or coast the vehicle. Clunk and shuffle are the phenomena that usually occur during this scenario causing driver discomfort. The clunk and shuffle are caused by the backlash and compliance physical properties of the driveline. Consequently, control strategy needs to be developed which can provide …

Mpc-Based Autonomous Driving Control With Localized Path Planning For Obstacle Avoidance And Navigating Signalized Intersections, Sai Rajeev Devaragudi

Dissertations, Master's Theses and Master's Reports

Connected and autonomous vehicles are becoming the major focus of research for the industry and academia in the automotive field. Many companies and research groups have demonstrated the advantages and the requirement of such technology to improve the energy efficiency of vehicles, decrease the number of crash and road accidents, and control emissions.

This research delves into improving the autonomy of self-driving vehicles by implementing localized path planning algorithms to introduce motion control for obstacle avoidance during uncertainties. Lateral path planning is implemented using the A* algorithm combined with piecewise Bezier curve generation which provides an optimum trajectory reference to …