Master of Science, The Ohio State University, 2023, Mechanical Engineering

Global vehicle emission regulations along with a growing consumer demand is a driving force in shifting the automotive industry towards a cleaner future. This shift requires significant automotive advancements in energy efficiency. Powertrain electrification and connected and autonomous vehicle (CAV) technology are key innovations that can reduce energy consumption and emissions. This thesis aims to improve the energy efficiency of a vehicle under varying conditions and determine the effect of charging on energy consumption. The vehicle model is established and utilized in the formulation of an optimal control problem in order to minimize energy consumption. The developed method to solve the optimization problem is applied in a large-scale study, culminating in an analysis of the effects of varying charging behavior on the energy consumption of the vehicle. The vehicle model is developed and validated over 25 real-world cycles resulting in an average fuel consumption, battery energy consumption, and total energy consumption errors of 2.2%, 2.9%, and 2.8%, respectively. The velocity dynamics and powertrain of the modeled vehicle are co-optimized to improve a weighted cost between energy consumption and travel time. The optimization results in an average decrease of 10% in fuel consumption, 8% in battery energy consumption, and 19% in total energy consumption. Lastly, the large-scale study reveals a correlation between charging behavior and both the effect of charging event placement and the presence of look-ahead information on energy efficiency. The resulting trends in charging behavior give context for energy efficient trip planning.

Committee: Stephanie Stockar (Advisor); Marcello Canova (Committee Chair) Subjects: Mechanical Engineering

Doctor of Philosophy, The Ohio State University, 2020, Mechanical Engineering

Today's world faces numerous environmental challenges as we attempt to meet the growing demands for mobility while tackling its negative externalities. In recognition of these negative externalities, world governments have enacted increasingly stricter standards that the means of mobility must meet. To meet these demands, private and public industries have invested tremendous resources into alternative means of mobility. The most promising step to immediately reduce mobility's negative externalities is the use of hybrid technologies. Hybrid technologies utilizes traditional petrochemical energy sources combined with electrical sources to power mobility. If properly controlled, this allows for a reduction in energy consumption and pollutant production in vehicles. The challenge for mobility engineers is how to properly control these multi-domain systems to reduce negative externalities. Extensive research in the field of optimal control applied to hybrid vehicles has already shown that fuel consumption can be minimized within a charge sustaining hybrid through optimized torque splitting. Furthermore, research into pollutant production and control has greatly reduced air pollution from vehicles. Both fields have already penetrated the consumer market and helped form control strategies that are already on the road. However, the increasing demands placed by regulations require constantly pushing the bounds for the extra reduction in fuel consumption or pollutant production. As such, this research develops a methodology that can be applied to HEVs to establish a controls strategy for the simultaneous reduction of fuel consumption and pollutant production. This work relies on model-based techniques to simulate vehicle operation, and optimal control techniques to use the developed vehicle models to establish control policies to reduce fuel consumption and pollution production. This work goes through the development of a catalyst and emissions model that predicts tailpipe (open full item for complete abstract)

Committee: Giorgio Rizzoni (Advisor); Shawn Midlam-Mohler (Advisor); Ahmet Selamet (Committee Member); Vadim Utkin (Committee Member); Punit Tulpule (Committee Member) Subjects: Mechanical Engineering

Master of Science, The Ohio State University, 2020, Mechanical Engineering

With the on-going electrification and data-intelligence trends in logistics industries, enabled by the advances in powertrain electrification, and connected and autonomous vehicle technologies, the traditional ways vehicles are designed by engineering experience and sales data are to be updated with a design for operation notion that relies intensively on operational data collection and large scale simulations. In this work, this design for operation notion is revisited with a specific combination of optimization and control techniques that promises accurate results with relatively fast computational time. The specific application that is explored here is a Class 6 pick-up and delivery truck that is limited to a given driving mission. A Gaussian Process (GP) based statistical learning approach is used to refine the search for the most accurate, optimal designs. Five hybrid powertrain architectures are explored, and a set of Pareto-optimal designs are found for a specific driving mission that represents the variations in a hypothetical operational scenario. A cross-architecture performance and cost comparison is performed and the selected architecture is developed further in the form of a forward simulator with a dedicated ECMS controller. In the end, a traffic-in-the-loop simulation is performed by integrating the selected powertrain architecture with a SUMO traffic simulator to evaluate the performance of the developed controller against varying driving conditions.

Committee: Giorgio Rizzoni (Advisor); Qadeer Ahmed (Committee Member) Subjects: Automotive Engineering; Engineering; Mechanical Engineering; Sustainability; Systems Design; Transportation

Doctor of Philosophy, The Ohio State University, 2013, Mechanical Engineering

A crucial step towards the large-scale introduction of plug-in hybrid electric vehicles (PHEVs) in the market is to reduce the cost of their energy storage systems. One of the goals of U.S Department of Energy (DOE) Vehicle Technologies Program for hybrid electric systems is to, by 2014, reduce the production cost of Li-ion batteries by nearly 70 percent from 2009 costs. Currently, battery cycle- and calendar-life represents one of the greatest uncertainties in the total life-cycle cost of advanced energy storage devices. Batteries are inherently subject to aging. Aging is the reduction in performance, availability, reliability, and life span of a system or component. The generation of long-term predictions describing the evolution of the aging in time for the purpose of predicting the Remaining Useful Life (RUL) of a system may be understood as Prognosis. The field of battery prognosis has seen progress with respect to model based and data driven algorithms to model aging and estimate RUL of battery cells. However, in advanced battery systems, cells are interconnected and aging propagates. The aging propagation from one cell to others exhibits itself in a reduced system life. Propagation of aging has a profound effect on the accuracy of battery systems state of health (SOH) assessment and prognosis. This thesis proposes a systematic methodology for modeling the propagation of aging in advanced battery systems. The modeling approach is such that it is able to predict battery pack aging, thermal, and electrical dynamics under actual PHEV operation, and includes consideration of random variability of the cells, electrical topology and thermal management. The modeling approach is based on the interaction between dynamic system models and dynamic models of aging propagation. The system level SOH is assessed based on knowledge of individual cells SOH, electrical topology and voltage equalization approach. The proposed methodology is used to develop a computational model- (open full item for complete abstract)

Committee: Giorgio Rizzoni (Advisor); Simona Onori (Advisor); Yann Guezennec (Committee Member); Manoj Srinivasan (Committee Member); Zhang Wei (Committee Member) Subjects: Mechanical Engineering

Master of Science, The Ohio State University, 2012, Mechanical Engineering

Increasingly stringent government regulations and the rising price of oil are causing automotive manufactures to develop vehicles capable of obtaining higher fuel economies and lower emissions. To achieve these goals, automotive manufactures have been developing hybrid electric vehicles (HEV) and plug-in hybrid electric vehicles (PHEV) that use both electricity and petroleum based fuels as their power sources. The additional power the vehicle receives from the high voltage batteries and the electric machines allow automotive manufacturers to downsize the engine inside of the vehicle. Vehicles with smaller engines are able to obtain a higher overall fuel economy because the smaller engine is able to operate at its more efficient high load operating points more frequently. The addition of a high voltage battery pack and at least one electric machine to a vehicle's conventional powertrain significantly increases the complexity of optimizing the operation of the vehicle's powertrain components. In a hybrid vehicle, the driver's power demand from the accelerator pedal can be met by the engine, the electric machines or a combination of the two. Therefore the vehicle needs a sophisticated control strategy that can divide the driver's power demand between the different torque producing powertrain components as efficiently as possible. The process of designing an optimal control strategy for a vehicle can require a significant amount of time, money and in-vehicle testing. Therefore many automotive manufacturers use Software-in-the-Loop (SIL) simulation to both speed up and reduce the cost of developing a vehicle's control strategy. Software-in-the-Loop simulation allows multiple versions of a control strategy to be tested in a virtual environment, in order to find the control strategy version most likely to increase the vehicle's fuel economy. The best version of the control strategy from the SIL simulations can then be tested later on the vehicle. The work described in this (open full item for complete abstract)

Committee: Dr. Giorgio Rizzoni (Advisor); Dr. Shawn Midlam-Mohler (Advisor); Dr. Yann Guezennec (Committee Member) Subjects: Mechanical Engineering

Master of Science, The Ohio State University, 2012, Electrical and Computer Engineering

This thesis presents the optimal control problem of energy management in a plug-in hybrid electric vehicle. Review of the literature suggests the need for a methodology which follows a blended strategy unlike the traditional charge depleting - charge sustaining (CD-CS) strategy for state of charge of the battery. Many present blended strategies require a-priori knowledge of the driving mission which is obtained by prediction. The performance of these strategies again depends on the prediction algorithms and often end up being sub-optimal in implementation. There is a need for an energy management strategy that provides near optimal results with minimal information about the driving mission. This thesis proposes one such controller. Knowledge of the optimal trajectories under various driving conditions is obtained by implementing a Pontryagin's Minimum Principle (PMP) based energy management scheme. With this knowledge, a practical implementable controller is proposed which performs with near optimal results under different driving missions. A comparison of the optimal PMP solution, the practical controller solution and the traditional CD-CS solution is done to conclude this work.

Committee: Giorgio Rizzoni PhD (Advisor); Yann Guezennec PhD (Advisor); Simona Onori PhD (Advisor); Mahesh Illindala PhD (Committee Member) Subjects: Alternative Energy; Automotive Engineering; Electrical Engineering; Mechanical Engineering

Master of Science, The Ohio State University, 2012, Electrical and Computer Engineering

Intelligent Transportation Systems (ITS) is a broad category of research relating to new technologies that can improve systems in vehicles, such as safety or energy management. The studies in this thesis discuss how energy management systems can be improved with theories and information from ITS research areas. New types of vehicles are entering the marketplace now that include electric vehicles (EV's), hybrid electric vehicles (HEV's), and plug-in hybrid electric vehicles (PHEV's). HEV's and PHEV's are a particular challenge to control engineers because of the flexibility of their powertrains. These vehicles contain two power sources, their internal combustion engine and their battery-powered electric motor. The powersplit control problem will be discussed and how optimal control theory can be implemented to optimize the powersplit resulting in lower fuel consumption. Chapter 2 discusses the areas of ITS that are relevant to the PHEV control problem. These include sourcing geographic data such as road grade and computing the length and geometry of a route to be traversed. Chapter 3 covers the Challenge X vehicle simulator and the dynamic equations that form the vehicle model. The Challenge X vehicle was designed for the 2004 Challenge X competition sponsored by General Motors where student teams competed to convert a small SUV into a hybrid electric vehicle. This simulator was modified from its original form to reflect a prototype plug-in hybrid electric vehicle. This included modifying the battery model to include more capacity and change the cell chemistry to lithium ion from nickel-metal hydride. Chapter 4 includes the details of the powersplit control algorithm implemented, called the Adaptive Equivalent Consumption Minimization Strategy(A-ECMS). A new formulation called the finite horizon adaptive ECMS is introduced and its performance analyzed under varying road load conditions and compared with the global optimal solution from Dynamic Programming.

Committee: Umit Ozguner (Advisor); Giorgio Rizzoni (Committee Member); Simona Onori (Committee Member) Subjects: Automotive Engineering; Electrical Engineering; Energy; Engineering

Master of Science, The Ohio State University, 2011, Mechanical Engineering

Battery lifespan is one of the largest considerations when designing battery packs for electrified vehicles. Even during vehicle operation, it is essential to monitor the progression of a battery health as it degrades and predict battery life. This thesis presents a preliminary severity factor analysis based on available experimental data and details the development of an algorithm for predicting, while in operation, the remaining life of a battery based on the growth of internal resistance. Nine lithium ion batteries were systematically aged through severe aging protocols spanning multiple C-rates (2C, 4C and 8C), low ranges of SOC (0-10, 0-20 and 0-30%), and elevated temperature (55 deg C). Their internal resistance was continuously calculated at each sharp current transition, and these values were filtered and processed. Severity factors were calculated for each battery by determining the average rate of resistance growth over a battery life and a preliminary analysis of these factors was carried out. A resistance growth dynamic model was developed to identify rates of resistance growth on a local basis as resistance values were collected. These local rates of resistance growth were then used to calculate predicted future rates of resistance growth, which were in turn used to predict remaining life. The life prediction algorithm produced continuously updated predictions of remaining battery life that proved relatively accurate for cases of constant battery aging conditions. This computationally simple algorithm could be implemented onboard an electrified vehicle to provide estimates of remaining battery life based on resistance growth. This methodology can in principle be readily extended to track capacity degradation as well, provided that a feasible capacity estimator can be developed on the basis of vehicle measurements.

Committee: Yann Guezennec (Advisor); Giorgio Rizzoni (Committee Member); Simona Onori (Committee Member) Subjects: Automotive Engineering; Electrical Engineering; Mechanical Engineering

Master of Science, The Ohio State University, 2011, Mechanical Engineering

Current model-based design research on automotive catalytic converters mainly fall into three basic categories: either modeling the catalyst as a continuous system based on physics, discretizing the system to reduce modeling complexity, or developing a highly-simplified, mean-value model for control. Continuous models are computationally intensive and therefore not well-suited for implementation into a vehicle model for Hardware in the Loop or control design. Highly-simplified models are calibrated for a particular system without incorporating the governing physical laws into the model, and mean-value models are only able to predict the response for a single lumped element. Although a simplified, mean-value model can be developed to accurately predict system response, it does not lend itself to being extended to broader applications without significant re-calibration efforts. Therefore, a model is needed that can account for the physics of the system so it can be extended to further applications while decreasing computation time to allow the model to be implemented for Hardware in the Loop and vehicle control design. This research investigates the development of such a model to predict automotive catalytic converter thermal response during warm-up. A one-dimensional, lumped-parameter model of a three-way catalyst was developed in Matlab/Simulink. The catalyst length was divided into discrete elements. Each discrete element contained states for the temperatures of the gas, substrate, and can wall. Heat transfer mechanisms were modeled from physics-based equations. For each discrete element, these equations modeled the enthalpy of the gas flow axially through the catalyst, convective heat transfer between the gas and substrate, conduction between discrete elements axially along the catalyst for the substrate and for the can, conduction between the substrate and can wall, and convection from the can wall to ambient. Model predictions were validated against experimental (open full item for complete abstract)

Committee: Shawn Midlam-Mohler PhD (Advisor); Giorgio Rizzoni PhD (Advisor); Yann Guezennec PhD (Committee Member) Subjects: Automotive Engineering; Mechanical Engineering

Doctor of Philosophy, The Ohio State University, 2009, Mechanical Engineering

The dissertation offers an overview of the energy management problem in hybrid electric vehicles. Several control strategies described in literature are presented and formalized in a coherent framework. A detailed vehicle model used for energy flow analysis and vehicle performance simulation is presented. Three of the strategies (dynamic programming, Pontryagin's minimum principle, and equivalent consumption minimization strategy, also known as ECMS) are analyzed in detail and compared from a theoretical point of view, showing the underlying similarities. Simulation results are also provided to demonstrate the application of the strategies.

Committee: Giorgio Rizzoni (Advisor); Yann Guezennec (Committee Member); Steve Yurkovich (Committee Member); Junmin Wang (Committee Member) Subjects: Mechanical Engineering

Master of Science in Engineering, University of Akron, 2012, Civil Engineering

Every day Americans drive countless miles on the millions of miles of roads. As this general trend continues to climb, it has led to an increasing demand in the diminishing oil supply and an increase in greenhouse gas production. Although many solutions have been proposed, the most viable option currently entering the market and gaining widespread support is the electric car. Manufactures, utility companies and government entities have raised the question of how well will these vehicles perform in the consumer market. This is not only critical to estimating the level of required charging infrastructure but also the reliability of projected economic benefits. This paper proposes to use a diffusion model to study the future market of PEVs. Sales data from hybrid cars are taken to estimate the diffusion model coefficients. Then, a consumer value matrix is employed to simulate the effects of changes in the consumer market that would influence a consumer's decision whether or not to purchase a vehicle. From these estimates, short term market growth projections have been made and found capable of reflecting consumer's reaction to different market growth scenarios.

Committee: Ping Yi Dr. (Advisor); William Schneider IV Dr. (Committee Member); Ala Abbas Dr. (Committee Member) Subjects: Technology; Transportation; Transportation Planning

Master of Science, The Ohio State University, 2013, Electrical and Computer Engineering

The present thesis presents an insight to the problem of battery aging diagnosis and prognosis problem for automotive applications (PHEV vehicles). The work has been carried out at Center for Automotive Research (CAR) USA. The problem itself is not a trivial one and it requiters certain systems theory tools that have been well developed in the literature to be carried out on an experimental data set that has been obtained at CAR by aging LI-ION batteries throughout various real time driving cycles profiles. Estimating the Remaining Useful Life (RUL) of a component is at the center of the problem of prognosis and health management. It is a useful tool that informs the engineer with how much time it is left before the functionality of the competent is lost. The problem of prediction is to deal with multiple sources of uncertainties such as noises in the systems, degradation in the component's functionality.

Committee: Vadim Utkin Professor (Advisor); Giorgio Rizzoni Professor (Committee Member) Subjects: Electrical Engineering