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Zeng, XiangruiOptimally-Personalized Hybrid Electric Vehicle Powertrain Control
Doctor of Philosophy, The Ohio State University, 2016, Mechanical Engineering
One of the main goals of hybrid electric vehicle technology is to improve the energy efficiency. In industry and most of academic research, the powertrain control is designed and evaluated under standard driving cycles. However, the situations that a vehicle may encounter in the real world could be quite different from the standard cycles. Studies show that the human drivers have a great influence on the vehicle energy consumptions and emissions. The actual operating conditions that a vehicle faces are not only dependent on the roads and traffic, but also dependent on the drivers. A standard driving cycle can only represent the typical and averaged driving style under the typical driving scenarios, therefore the control strategies designed based on a standard driving cycle may not perform well for all different driving styles. This motivates the idea to design optimally-personalized hybrid electric vehicle control methods that can be adaptive to individual human driving styles and their driving routes. Human-subject experiments are conducted on a driving simulator to study the driving behaviors. A stochastic driver pedal model that can learn individual driver’s driving style is developed first. Then a theoretic investigation on worst-case relative cost optimal control problems, which is closely related to vehicle powertrain optimal control under real-world uncertain driving scenarios, is presented. A two-level control structure for plug-in hybrid electric vehicles is proposed, where the parameters in the lower-level controller can be on-line adjusted via optimization using historical driving data. The methods to optimize these parameters are designed for fixed-route driving first, and then extended to multi-routes driving using the idea similar to the worst-case relative cost optimal control. The performances of the two proposed methods are shown through simulations using human driving data and stochastic driver model data respectively. The energy consumption results in both situations are close to the posteriori optimal result and outperform other existing methods, which show the effectiveness of applying optimally-personalized energy management strategy on hybrid electric vehicles. Finally, a route-based global energy-optimal speed planning method is also proposed. This off-line method provides a useful tool to evaluate the potential of other speed planning methods, for either eco-driving guidance applications or future automated vehicle controls. The contributions of this dissertation include 1) a novel stochastic driver pedal behavior model which can learn independent drivers’ driving styles is created, 2) a new worst-case relative cost optimal control method is proposed, 3) a real-time implementable stochastic optimal energy management strategy for hybrid electric vehicles running on fixed routes is designed using the statistics of history driving data, 4) the fix-route strategy is extended to the multi-route situation, and 5) an off-line global energy-optimal speed planning solution for road vehicles on a given route is presented.

Committee:

Junmin Wang (Advisor); Ryan Harne (Committee Member); Chia-Hsiang Menq (Committee Member); Haijun Su (Committee Member)

Subjects:

Automotive Engineering; Mechanical Engineering

Keywords:

Hybrid electric vehicle; energy management strategy; optimal control; speed planning; driver model

AbuAli, MohamedTechniques for Non-Intrusive Machine Energy and Health Modeling
PhD, University of Cincinnati, 2010, Engineering and Applied Science: Industrial Engineering

An Energy Management System (EMS) monitors, evaluates, and controls the performance of different energy-consuming equipment such as motors and compressors and extending to plant-floor machinery. This research explores and develops a systematic framework and statistically-significant analytic models for using electric consumption power variables as an indicator for machine-level health or performance. This is in an effort to explore new techniques for improving the current capabilities of traditional energy management systems.

Power data is collected real-time for electrical power consumption usage of machines, under consistent operational conditions. Three levels of performance assessment and associated models are developed based on acquired power signals that effectively consider the power consumed by a machine as an indicator for overall machine performance. The research hypothesis is that a relationship exists between a machine’s electric energy consumption levels and the machine’s level of performance and potential health degradation. An intuitive predictive model is developed to give a power-based performance prediction for one machining cycle or cycle step ahead.

The models are successfully implemented and validated on a real-world industrial case study for an injection molding process where electrical power consumption data is collected. A standard moving average method is used to benchmark the results of this analysis.

Committee:

Jay Lee, PhD (Committee Chair); Hongdao Huang, PhD (Committee Member); Ernest Hall, PhD (Committee Member); Hiroshi Nakajima, PhD (Committee Member); Richard Leroy Shell, PhD (Committee Member)

Subjects:

Industrial Engineering

Keywords:

Power Monitoring;Prognostics and Health Management;Energy Management

Zhou, YuEnergy Harvesting Using a Thermoelectric Generator and Generic Rule-based Energy Management
Master of Sciences, Case Western Reserve University, 2008, Computer Engineering
Harvesting energy from previously unemployed ambient sources can play an important role in saving energy and reducing the dependency to primary energy sources (AC power or battery) of an electronic system. In this work, we investigate harvesting thermo-electric energy from wasted heat in a microprocessor and propose a generic rule-based framework for energy management. We develop an analytical model to accurately estimate the recycled energy considering the non-uniformity of temperature distribution on the die surface. Further, we propose a possible arrangement for using the TEG on a processor and provide measurement results on the amount of harvested energy. Next, a rule-based energy management system is proposed for managing the acquisition, mixing, delivery and storage of energy for any collection of electrical energy sources and electrical appliances, which have different energy generation and consumption parameters. The proposed energy management system is easily scalable, to cater to a variety of applications with different requirements, while improving the energy utilization and operational lifetime of energy sources.

Committee:

Swarup Bhunia (Advisor)

Keywords:

TEG; energy sources; energy users; fuel cell; ENERGY MANAGEMENT; Heat

Waldman, Colin ADevelopment and Implementation of an Adaptive PMP-based Control Strategy for a Conventional Vehicle Electrical System
Master of Science, The Ohio State University, 2014, Mechanical Engineering
This thesis details the development, implementation, and experimental testing of a supervisory energy management control strategy for the vehicle electrical system of a passenger car. The control strategy commands the alternator duty cycle such that vehicle fuel economy is optimized whilst the instantaneous load current demand is met and constraints on the system voltage and battery state of charge are satisfied. To this extent, Pontryagin's Minimum Principle (PMP) is utilized alongside a vehicle plant model in order to evaluate the behavioral characteristics of the vehicle electrical system subjected to optimal control. These observations are employed in the development of an adaptive, PMP-based supervisory strategy capable of real-time control. Experimental testing of the in-house developed control strategy, termed "A-PMP", is benchmarked against a baseline production control strategy, demonstrating consistent improvements in vehicle fuel economy.

Committee:

Marcello Canova (Advisor); Shawn Midlam-Mohler (Committee Member)

Subjects:

Automotive Engineering; Engineering; Mechanical Engineering

Keywords:

Automotive; Model-based Control; Electrical System; Optimal Control; Energy Management

Vallur Rajendran, AvinashA Methodology for Development of Look Ahead Based Energy Management System Using Traffic In Loop Simulation
Master of Science, The Ohio State University, 2018, Mechanical Engineering
This thesis details efforts towards developing a methodology that enables the design of a look ahead based energy management system. It explores various technologies that are required to enable such a system to function on a physical vehicle. A new simulation framework known as `Traffic-In-Loop' (TIL) simulation is developed to mimic real-world driving. It serves as a drive cycle independent controls development platform. The framework is enabled by combining microscopic traffic simulation with a detailed mathematical powertrain model. The TIL simulation technique facilitates emulation of on-board sensors, V2X communication and capture causal behavior of real-world scenarios. Data collected from these virtual sensors are used to forecast future drive scenarios -- called `Look ahead predictions'. Further a strategy to integrate future drive scenario forecasts with powertrain control is introduced. The above advances, catalyzed the design of a look ahead based energy management controller, called 'Delta Energy Controller'. It aims at improving a vehicle's fuel economy by utilizing available drive scenario forecasts. Simulation results are used to prove the optimality of this controller and study the improvement in fuel economy as a function of better look ahead predictions.

Committee:

Giorgio Rizzoni (Advisor); Marcello Canova (Committee Member); Qadeer Ahmed (Committee Member)

Subjects:

Automotive Engineering; Electrical Engineering; Mechanical Engineering; Transportation

Keywords:

hybrid vehicles;electric vehicles;REEV;look ahead control;energy management;traffic simulation;powertrain control;traffic in loop simulation;data enabled control;fuel economy improvement; automotive sensors; SUMO; real world fuel economy estimation;

Tang, LiOptimal energy management strategy for hybrid electric vehicles with consideration of battery life
Doctor of Philosophy, The Ohio State University, 2017, Mechanical Engineering
The dissertation offers a systematic analysis on the interdependency between fuel economy and battery capacity degradation in hybrid electric vehicles. Optimal control approaches including Dynamic Programming and Pontryagin's Minimum Principle are used to develop energy management strategies, which are able to optimally tradeoff fuel consumption and battery aging. Based on the optimal solutions, a real-time implementable battery-aging-conscious Adaptive Equivalent Consumption Management Strategy is proposed, which is able to achieve performance that is comparable to optimal results. In addition, an optimal control based charging strategy for plug-in hybrid electric vehicles and battery electric vehicles is developed, which minimizes battery capacity degradation incurred during charging by optimizing the charging current profile. Combining a generic control-oriented vehicle cabin thermal model with the battery aging model, the benefit of this strategy in terms of decreasing battery aging is significant, when compared with the existing strategies, such as the widely accepted constant current constant voltage (CC-CV) protocol. Thus this dissertation presents a complete set of optimal control solutions related to xEVs with consideration of battery aging.

Committee:

Giorgio Rizzoni (Advisor)

Subjects:

Automotive Engineering; Engineering; Mechanical Engineering

Keywords:

hybrid electric vehicles, energy management strategy, battery life

Meyer, Danielle LEnergy Optimization of a Hybrid Unmanned Aerial Vehicle (UAV)
Master of Science, The Ohio State University, 2018, Electrical and Computer Engineering
Unmanned Aerial Vehicles (UAV) have continued to receive attention from corporations and governmental agencies due to their wide range of potential applications and hybrid nature. More Electric Aircraft (MEA) promise many benefits (e.g., reduced weight, decreased fuel consumption, and high reliability) and their development continues to be the trend. Hybrid UAVs are an ideal prototype to implement concepts of aircraft electrification due to their small size and the DC nature of their power systems. However, papers addressing the energy optimization UAV electric power systems fail to consider the importance of high accuracy and computational speed. This thesis proposes an energy optimization method to enhance the energy durability of a UAV through a novel approach integrating an optimization formulation and a detailed UAV simulation model, with physical circuitry characteristics. This approach allows for increased computation efficiency while still capturing physical system constraints experienced during real world flight, which are complex and highly nonlinear due to aerial, thermal, and electrical dynamics. Optimization formulations created within this work are based on dynamic programming and moving-horizon model predictive control (MPC). The efficacy of this method is proven on a realistic UAV system. Within the MPC formulation, various charge strategies are implemented and fuel consumption is calculated to provide insight into the trade-offs inherent within the UAV system, wherein battery discharging is required for high demand dash periods, but additional charge can only be supplied via increased output engine power. That is, minimal fuel consumption must be considered in light of the need for non-optimal output engine power to charge the battery such that a total mission can be completed. Algorithmic considerations regarding horizon size for MPC and algorithmic enhancements, considering random loads and renewable generation capacity on-board the UAV are presented. These results regarding enhanced algorithmic elements provide insight into the capability of the algorithm to function within a real-time environment and the benefit of solar arrays to provide additional generation. Using MPC as the optimization technique of choice allows for the development of an algorithm capable of handling both missions with a deterministic load and within online implementations, as deterministic cases represent a downsized problem where algorithmic considerations can be studied and iterated to reach satisfactory online implementation. While this thesis approaches the problem from the perspective of UAV design, i.e., optimization for a deterministic load profile, the algorithmic enhancements provided here represent initial steps towards online implementation.

Committee:

Jiankang Wang (Advisor); Mahesh Illindala (Committee Member)

Subjects:

Electrical Engineering

Keywords:

Unmanned Aerial Vehicle; UAV; Optimization; Model Predictive Control; Algorithm; Modeling; Energy Management; Aerial Power System

Ding, FeiSmart Distribution System Automation: Network Reconfiguration and Energy Management
Doctor of Philosophy, Case Western Reserve University, 2015, EECS - Electrical Engineering
Smart distribution system automation is the key to realizing a highly reconfigurable, reliable, flexible and active distribution system. Automated network reconfiguration including restoration is the most studied area in distribution automation, and it contributes to power loss minimization, voltage improvement and also can enable the distribution network to respond to contingencies and changes happened in the grid. Distributed energy resources at the customer premises, energy storage systems and plug-in electric vehicles are indispensable parts of future smart distribution systems. Their participations have brought more dynamics and uncertainties into the grid, and hence new technologies at both planning and operation levels must be developed to manage the energy dispatched from distributed energy resources and energy storage units, the charging and discharging behaviors of electric vehicles so that the entire power distribution system could operate stably and efficiently. Meantime, due to the intermittent, imperfectly predicted renewable energy and more complicated, uncertain load patterns, two challenges have arisen on network reconfiguration study, including more frequent reconfiguration actions and more complicated optimization problems for determining the optimal network topology. Thus, new approaches for reconfiguring distribution networks must be developed to overcome these challenges. In order to address the above challenges which distribution systems are facing to and develop new technologies for realizing smart distribution automation, a comprehensive study on network reconfiguration and energy management of distributed generation systems was studied. The contributions of this dissertation include: (1) proposed a novel problem formulation for network reconfiguration problem based on “switch states”; (2) developed three new methods to solve the optimization problem including heuristic algorithm, hybrid algorithm and revised genetic algorithm; (3) proposed a hierarchical, decentralized network reconfiguration approach that has been proved to have significant computational advantage compared with other existing methods; (4) proposed the concept of “dynamic network reconfiguration” in which the impact of time-varying load demands, renewable energy generation and other contingencies on the optimal distribution network topology were fully addressed and analyzed. (5) Since DG has become one of the most important parts in distribution systems. The mechanism of distributed generation itself and the impact of distributed generation on distribution system analysis must be studied. This dissertation has studied the modeling and reactive control of multiple DG systems, and also studied the unbalanced distribution feeder reconfiguration and proposed energy management strategy for controlling all grid-connected DGs in order to optimize distribution system operation.

Committee:

Kenneth Loparo (Advisor); Vira Chankong (Committee Member); Hong Mingguo (Committee Member); Prica Marija (Committee Member)

Subjects:

Electrical Engineering; Energy

Keywords:

Smart Distribution System, Distribution Automation, Network Reconfiguration, Energy Management, Distributed Generation

Simmons, Kyle SModeling and Optimal Supervisory Controller Design for a Hybrid Fuel Cell Passenger Bus
Master of Science, The Ohio State University, 2013, Mechanical Engineering
This thesis presents the modeling and optimal supervisory energy management of a fuel cell/battery-powered passenger bus. The work presented was completed in conjunction with the DesignLine Corporation and the National Fuel Cell Bus Program. With growing concerns about petroleum usage and greenhouse gas emissions in the transportation sector, finding alternative methods for vehicle propulsion is necessary. Proton Exchange Membrane (PEM) fuel cells are viable possibilities due to their high efficiencies and zero emissions. It has been shown that the benefits of PEM fuel cells can be greatly improved through hybridization, which requires an energy management system. First, the modeling of an energy-based, forward-simulator representative of the bus is presented. Each component of the powertrain is modeled separately for ease of modification. Experimentally obtained data was used to represent components, when available. Several different battery cells were modeled through experimental identification at The Center for Automotive Research at The Ohio State University. These models were used in the simulator to aid in battery examination and selection for the actual hybrid fuel cell bus. The formal definition of the energy management control problem of the hybrid fuel cell bus is then outlined. Literature has provided numerous techniques for conventional hybrid vehicle control, many of which can be extended to a fuel cell hybrid. One such technique uses Pontryagin’s Minimum Principle (PMP). PMP is a very powerful tool in optimal control theory. It can provides a set of necessary conditions to ensure global optimality of a constrained control problem An optimal controller for the hybrid fuel cell bus control problem is developed by applying PMP. The PMP controller finds the optimal control trajectory to follow a given velocity profile that minimizes hydrogen fuel consumption by the fuel cell while maintaining battery state of charge, and satisfying physical limitations of the components. Finally, numerous simulations were completed using the PMP controller. Multiple drive cycles were examined, with and without road grade profiles to ensure every possible operating condition of the bus was explored. A range of different bus weights, battery sizes and different battery chemistries were also simulated. The optimal PMP controller was able to achieve a fuel economy between 4.0 and 8.7 miles per kilogram hydrogen (4.5 and 9.8 miles per diesel gallon equivalent), depending on the drive cycle and bus weight. It was found that the optimal control trajectories of the battery and fuel cell were nearly identical, regardless of battery chemistry. For the component sizing used in the bus, the optimal results show that the battery supplies most of the transit power demand, while the fuel cell operates around the average power demand of the given cycle. Because this average power demand varies greatly with the drive cycle considered, the fuel cell operation is strongly dependent on the severity of the drive cycle. A practical, implementable controller can be designed based on the trends seen from the optimal PMP results. To conclude the work, a possible algorithmic controller that can be implemented on the bus is briefly discussed.

Committee:

Yann Guezennec, Dr. (Advisor); Simona Onori, Dr. (Other); Shawn Midlam-Mohler, Dr. (Committee Member)

Subjects:

Automotive Engineering; Mechanical Engineering

Keywords:

Fuel Cell; Hybrid Vehicle; Modeling; Energy Management; Pontryaginm

Li, XuchenDriving Style Adaptive Electrified Powertrain Control
Master of Science, The Ohio State University, 2018, Mechanical Engineering
The performance of a powertrain is dependent on how its controller parameters are tuned for the given duty cycle. In real world, the duty cycle is hard to predict, however, if the historical performance is evaluated and used to fine tune the controller parameters, the performance of the powertrain can be improved. This thesis presents a retrospective information-based powertrain performance improvement. The performance is evaluated using the fuel economy and charge sustaining operation of an electrified powertrain. A Retrospective Cost Adaptive Controller (RCAC) has been designed for parallel HEV, which improves it performance based on the former performance. The simulation results demonstrate that the controller parameters are re-calibrated to show improved performance for standard test driving cycles as well as cycles with different driving styles. A dynamic programming (DP) solution is also given as a benchmark to evaluate RCAC results.

Committee:

Giorgio Rizzoni, Prof. (Advisor); Vadim Utkin, Prof. (Committee Member); Qadeer Ahmed, Dr. (Committee Member)

Subjects:

Mechanical Engineering

Keywords:

retrospective cost adaptive control; energy management strategy; powertrain control; HEV

Xu, ZichenEnergy Modeling and Management for Data Services in Multi-Tier Mobile Cloud Architectures
Doctor of Philosophy, The Ohio State University, 2016, Electrical and Computer Engineering
Researchers' prediction about the emergence of very small and very large computing devices is becoming true. Computer users create personal content from their mobile devices and these contents are processed/stored in the remote server. This mobile cloud computing architecture contains millions of smartphone devices as the edge and high-end servers as the cloud, in order to provide data services worldwide. Unlike data services in traditional architectures, data services in the mobile computing architecture is greatly constrained by by energy consumption. Data services running in the cloud consume a large amount of electricity that accounts for 4% of the global energy use. Data processing and transmission in mobiles devices, such as smartphones, quickly drain out their batteries. Therefore, energy is one of the most important criterion in the design of these systems. To address this problem, we need to build an energy modeling and management framework to profile, estimate and manage the energy consumption for data processing in the mobile cloud architecture. We first start with energy profiling of data processing in a single node. The study discovers that there exist possibilities of finding energy-efficient execution plans other than fast plans only. Based on the profile, we propose our online estimation tools for modeling and estimating energy consumption of relational data operations. Further, we provide power performance control for data processing. The control framework provide service level agreement guarantee while reducing the power consumption. The control-theoretic design provide system stability when facing unpredictable workloads. Using the modeling processing, we expand our research to optimize energy-related objectives, such as carbon footprint and cloud expense, in multiple nodes. We carefully study the processing of data in multiple nodes, and find that the processing (i.e., read/write) significantly affects the objectives when replicating data objects across multiple nodes. By solving this problem, we build two data storage systems--CADRE and BOSS, to reduce the carbon footprint of serving data, and the cloud expense of processing in-memory data, respectively. The modeling and managing process can also be applied to edge devices, such as smartphones. We start with building an energy estimation tool for specific applications on smartphones using performance counters. Unlike traditional modeling work, using performance counters can provide energy estimation for fine-grained executions and isolate the target energy profile. Based on the energy/battery model, we propose a dual-battery management system on battery-powered devices. Altering the power supply between the two batteries can significantly improve the service time of the device. Combining all energy modeling and management system designs above, we are able to significantly improve the energy efficiency of data services in each tier of the mobile cloud architecture.

Committee:

Xiaorui Wang (Advisor); Fusun Ozguner (Committee Member); Christopher Stewart (Committee Member)

Subjects:

Computer Engineering; Computer Science

Keywords:

Energy modeling, energy management, distributed data services, cost-effective optimization, computing system design

Gudi, NikhilA Simulation Platform to Demonstrate Active Demand-Side Management by Incorporating Heuristic Optimization for Home Energy Management
Master of Science, University of Toledo, 2010, Electrical Engineering
Demand-Side Management (DSM) can be defined as the implementation of policies and measures to control, regulate, and reduce energy consumption. This document introduces home energy management through dynamic distributed resource management and optimized operation of household appliances in a DSM based simulation platform. The principal purpose of the simulation platform is to illustrate customer-driven DSM operation, and evaluate an estimate for home electricity consumption while minimizing the customer's cost. A heuristic optimization algorithm i.e. Binary Particle Swarm Optimization (BPSO) is used for the optimization of DSM operation in the platform. The platform also simulates the operation of household appliances as a Hybrid Renewable Energy System (HRES). The resource management technique is implemented using an optimization algorithm, i.e. Particle Swarm Optimization (PSO), which determines the distribution of energy obtained from various sources depending on the load. The validity of the platform is illustrated through an example case study for various household scenarios.

Committee:

Dr. Lingfeng Wang, PhD (Advisor); Dr. Vijay Devabhaktuni, PhD (Advisor); Dr. Gursel Serpen, PhD (Committee Member)

Subjects:

Computer Science; Electrical Engineering; Energy; Technology

Keywords:

Demand-side management; home energy management; distributed energy resources; particle swarm optimization; simulation tool; smart grid

Tulpule, Pinak J.Control and optimization of energy flow in hybrid large scale systems - A microgrid for photovoltaic based PEV charging station
Doctor of Philosophy, The Ohio State University, 2011, Electrical and Computer Engineering

This dissertation presents a hybrid large scale system model of a DC microgrid, its input to state stability analysis and an optimal control algorithm for load side energy management. The theoretical principles of hybrid large scale system modeling, stability, and optimal control for stochastic systems are applied to DC microgrid designed for a photovoltaic based charging station at a workplace parking garage. The example DC microgrid has two energy sources (renewable energy source and power grid) and many plug-in electric vehicle (PEV) charging stations. Stochastic inputs to the system are solar power and charging demand of the PEVs and the control inputs are the vehicle charging power and duration.

The hybrid large scale system model of the DC microgrid is developed in state space form to model the large number of DC-DC converters and discrete changes in the system configurations caused by actions of a supervisory controller and converter operating modes. Stability analysis of the model using the Gersgorin principle, an eigenvalue inclusion theorem and connective stability principles provide design guidelines and conditions on interconnection properties. Necessary conditions for the large scale system stability are provided using eigenvalue analysis. The input to state stability analysis is performed using Lyapunov theory for hybrid systems to provide constraints on the dwell time of the switching signal.

The optimization problem is structured as an inventory control problem and solved using dynamic programming with stochastic inputs to find the charging power of all the vehicles at each time step. A simple but realistic rule based algorithm is developed to distribute the total charging power among available vehicles. The control algorithm schedules PEV charging power to maximize the use of solar energy, reduce energy taken from the grid, and satisfy the charging demand of all vehicles within the switching constraints. Finally, this research is accompanied by the overall energy-economic analysis of the PV based PEV charging station to show the feasibility of the proposed method in real world applications. The economic analysis is based on one time charging during a day and considering bidirectional power flows with the grid using net metering.

Committee:

Stephen Yurkovich, PhD (Advisor); Giorgio Rizzoni, PhD (Committee Member); Jin Wang, PhD (Committee Member)

Subjects:

Alternative Energy; Economics; Electrical Engineering; Energy

Keywords:

Photovoltaic workplace charging; plug-in electric vehicles; DC microgrid; Hybrid Large Scale System Model; Economic analysis; solar power; Optimal load side energy management; Inventory control problem

Koprubasi, KeremModeling and Control of a Hybrid-Electric Vehicle for Drivability and Fuel Economy Improvements
Doctor of Philosophy, The Ohio State University, 2008, Mechanical Engineering

The gradual decline of oil reserves and the increasing demandfor energy over the past decades has resulted in automotive manufacturers seeking alternative solutions to reduce the dependency on fossil-based fuels for transportation. A viable technology that enables significant improvements in the overall tank-to-wheel vehicle energy conversion efficiencies is the hybridization of electrical and conventional drive systems.

Sophisticated hybrid powertrain configurations require careful coordination of the actuators and the onboard energy sources for optimum use of the energy saving benefits. The term optimality is often associated with fuel economy, although other measures such as drivability and exhaust emissions are also equally important. This dissertation focuses on the design of hybrid-electric vehicle (HEV) control strategies that aim to minimize fuel consumption while maintaining good vehicle drivability.

In order to facilitate the design of controllers based on mathematical models of the HEV system, a dynamic model that is capable of predicting longitudinal vehicle responses in the low-to-mid frequency region (up to 10 Hz) is developed for a parallel HEV configuration. The model is validated using experimental data from various driving modes including electric only, engine only and hybrid. The high fidelity of the model makes it possible to accurately identify critical drivability issues such as time lags, shunt, shuffle, torque holes and hesitation.

Using the information derived from the vehicle model, an energy management strategy is developed and implemented on a test vehicle. The resulting control strategy has a hybrid structure in the sense that the main mode of operation (the hybrid mode) is occasionally interrupted by event-based rules to enable the use of the engine start-stop function. The changes in the driveline dynamics during this transition further contribute to the hybrid nature of the system.

To address the unique characteristics of the HEV drivetrain and to ensure smooth vehicle operation during mode changes, a special control method is developed. This method is generalized to a broad class of switched systems in which the switching conditions are state dependent or are supervised. The control approach involves partitioning the state-space such that the control law is modified as the state trajectory approaches a switching set and the state is steered to a location within the partition with low transitioning cost. Away from the partitions that contain switching sets, the controller is designed to achieve any suitable control objective. In the case of the HEV control problem, this objective generally involves minimizing fuel consumption.

Finally, the experimental verification of this control method is illustrated using the application that originally motivated the development of this approach: the control of a HEV driveline during the transition from electric only to hybrid mode.

Committee:

Giorgio Rizzoni, PhD (Advisor); Yann Guezennec, PhD (Committee Member); Andrea Serrani, PhD (Committee Member); Steve Yurkovich, PhD (Committee Member)

Subjects:

Mechanical Engineering

Keywords:

Hybrid electric vehicles; hybrid vehicles; automotive control systems; vehicle modeling; model validation; driveline control; control of switched systems; energy management in hybrid vehicles;

Srivastava, RahulEfficient Energy Management in Wireless Sensor Networks
Doctor of Philosophy, The Ohio State University, 2010, Electrical and Computer Engineering

Recent advances in wireless networking and data acquisition have enabled us with a unique capability to remotely sense our environment. Data acquisition networks can be used to sense natural as well as human-created phenomena. As these applications may require deployment in remote and hard-to-reach areas, it is critical to ensure that such wireless sensor networks are capable of operating unattended for long durations. The lack of easy access to a continuous power source in most scenarios and the limited lifetime of batteries have hindered the deployment of such networks. Consequently, the central objective in wireless sensor network design is to utilize the available energy as efficiently as possible. In this thesis, we study the design of optimal or near-optimal energy management schemes for various wireless sensor networks composed of nodes with different capabilities.

Firstly, we derive theoretical upper bounds on the performance of a transmission scheduler for sensor networks. We do this by calculating the information theoretic channel capacity of finite-state Markov channels with imperfect feedback containing different grades of channel state information including that, obtained through Automatic Repeat Request (ARQ) feedback. Secondly, we consider the problem of energy optimal transmission scheduling over a finite state Markov channel with imperfect feedback. We propose a transmission controller that utilizes different "grades" of channel state information to schedule packet transmissions in an energy-optimal way, while meeting a deadline constraint for all packets waiting in the transmission queue. Our scheduler is readily implementable and it is based on the dynamic programming solution to the finite-horizon transmission control problem. We illustrate that our scheduler achieves a given throughput at a power level that is fairly close to the information-theoretic limit. Finally, we consider the problem of energy management in nodes with energy replenishment capabilities. Here, we derive the performance limits of sensor nodes with limited energy, being replenished at a variable and random rate. We provide a simple localized energy management scheme for nodes with limited energy storage space, and show that our scheme achieves a performance asymptotically close to that available with an unlimited energy source. Based on the insights developed, we address the problem of energy management for energy-replenishing nodes with finite data buffer capacities as well as limited energy storage space. To this end, we give an energy management scheme that is provably asymptotically optimal.

Committee:

Can Emre Koksal, PhD (Committee Chair); Ness B. Shroff, PhD (Committee Member); Eylem Ekici, PhD (Committee Member)

Subjects:

Electrical Engineering

Keywords:

Wireless Sensor Networks; Energy Management

Barnawi, AbdulwasaHybrid PV/Wind Power Systems Incorporating Battery Storage and Considering the Stochastic Nature of Renewable Resources
Doctor of Philosophy, University of Toledo, 2016, Electrical Engineering
Hybrid power generation system and distributed generation technology are attracting more investments due to the growing demand for energy nowadays and the increasing awareness regarding emissions and their environmental impacts such as global warming and pollution. The price fluctuation of crude oil is an additional reason for the leading oil producing countries to consider renewable resources as an alternative. Saudi Arabia as the top oil exporter country in the word announced the "Saudi Arabia Vision 2030" which is targeting to generate 9.5 GW of electricity from renewable resources. Two of the most promising renewable technologies are wind turbines (WT) and photovoltaic cells (PV). The integration or hybridization of photovoltaics and wind turbines with battery storage leads to higher adequacy and redundancy for both autonomous and grid connected systems. This study presents a method for optimal generation unit planning by installing a proper number of solar cells, wind turbines, and batteries in such a way that the net present value (NPV) is minimized while the overall system redundancy and adequacy is maximized. A new renewable fraction technique (RFT) is used to perform the generation unit planning. RFT was tested and validated with particle swarm optimization and HOMER Pro under the same conditions and environment. Renewable resources and load randomness and uncertainties are considered. Both autonomous and grid-connected system designs were adopted in the optimal generation units planning process. An uncertainty factor was designed and incorporated in both autonomous and grid connected system designs. In the autonomous hybrid system design model, the strategy including an additional amount of operation reserve as a percent of the hourly load was considered to deal with resource uncertainty since the battery storage system is the only backup. While in the grid-connected hybrid system design model, demand response was incorporated to overcome the impact of uncertainty and perform energy trading between the hybrid grid utility and main grid utility in addition to the designed uncertainty factor. After the generation unit planning was carried out and component sizing was determined, adequacy evaluation was conducted by calculating the loss of load expectation adequacy index for different contingency criteria considering probability of equipment failure. Finally, a microgrid planning was conducted by finding the proper size and location to install distributed generation units in a radial distribution network.

Committee:

Lingfeng Wang (Committee Chair); Hong Wang (Committee Member); Jackson Carvalho (Committee Member); Richard Molyet (Committee Member); Weiqing Sun (Committee Member)

Subjects:

Electrical Engineering; Energy

Keywords:

PV and Wind; net present value; optimal sizing; particle swarm optimization; electricity pricing; energy management; deferrable load scheduling; imputed demand; adequacy evaluation

Gogia, AshishTowards a Zero - Energy Smart Building with Advanced Energy Storage Technologies
Master of Science (M.S.), University of Dayton, 2016, Electrical Engineering
Current trends in energy demands and supply are unsustainable – economically, environmentally and socially. If this trend continues then the amount of energy related emissions of carbon dioxide will be more than double by 2050 leading to uncontrollable global warming, and the increased fossil fuel demand will become a serious threat to the security of resources. Energy efficient buildings, energy demand forecasting, integration of renewable energy systems, and advanced energy storage technologies are the various measures that can support energy security and climate change. Energy storage technologies can help in better integration of our electricity and heating systems, and can play a crucial role in energy system de-carbonization. They can also assist in improving electricity grid stability, reliability and resilience, better distribution of energy and system efficiency. Most promising energy storage technologies are still in the early stages of development and are currently struggling to compete with other state-of-the-art market technologies due to high costs and reliability issues. This research focuses on forecasting the energy load-demand profile of any residential or commercial building on a monthly/hourly basis with variations in climate/weather. Based on the load forecasted, this work predicts suitable energy storage technologies that are cost-efficient, and can meet the forecasted heating and cooling demands of buildings in any region.

Committee:

Jitendra Kumar, Dr. (Committee Co-Chair); Guru Subramanyam, Dr. (Committee Chair)

Subjects:

Energy; Engineering

Keywords:

Smart energy management;Energy storage technologies;Renewable energy;Reliability

Serrao, LorenzoA comparative analysis of energy management strategies for hybrid electric vehicles
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

Keywords:

Hybrid electric vehicle; HEV; PHEV; energy management; optimal control; modeling; powertrain

Kumar, Sri Adarsh A.Cloud Computing based Velocity Profile Generation for Minimum Fuel Consumption
Master of Science, The Ohio State University, 2012, Electrical and Computer Engineering

Vehicle fuel management problem is a promising field of research in light of the recently proposed CAFE standards. Previous works in this field mostly cater to Hybrid Electric vehicles or Plug-in Hybrid Electric Vehicles that compose only a minority of total vehicles on road. Our research is aimed at effective fuel management strategies that can be applied to everyday conventional vehicles using the hardware already on-board the vehicles. The minimal need for additional hardware, transferability to other vehicles and effective optimization techniques would be the main achievement of this research.

Vehicle velocity profile is optimized using dynamic programming to consume minimal fuel. The backwards model of the vehicle is constructed to calculate of fuel consumption of velocity profiles. Using the fuel consumption as a cost, spatial domain dynamic programming method is used to calculate the optimal velocity profile to minimize fuel consumption. A novel method of dynamic programming is proposed to achieve highly accurate results with reduced run-time. A majority of this thesis is dedicated to constructing driving scenarios that test the performance of the dynamic programming. These scenarios increase the complexity of optimization, gradually from a mere simulation to fully integrated stand-alone processing module to optimize off-site vehicles through wireless communication.

Committee:

Giorgio Rizzoni (Advisor); Umit Ozguner (Committee Member); Simona Onori (Committee Member)

Subjects:

Automotive Engineering; Electrical Engineering; Engineering

Keywords:

automotive control; optimization; dynamic programming; control; energy management; fuel;

Yang, ChengDevelopment of Intelligent Energy Management System Using Natural Computing
Master of Science in Engineering, University of Toledo, 2012, College of Engineering
In this thesis an Intelligent Energy Management System (EMS) for end consumer has been proposed. This system develops an algorithm for smart meter which is integrated between distribution grid and end consumers. The smart meter determines when to draw the energy from the grid or the storage unit for consumption. The first objective of the intelligent EMS is to save the cost for consumers by shifting the power drawn from the grid from high cost period to low cost period. The second objective of the intelligent EMS is to avoid grid overload by shifting the power drawn from the grid from high demand period to low demand period. The algorithm takes into consideration the hourly price and load demand of the grid. The algorithm was tested with the real data collected by ISO New England for the six states of Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island and Vermont, during the period of Jan 1, 2011 to Dec 31, 2011. Two approaches based on Fuzzy Logic and Genetic Algorithm (GA) were used. It was demonstrated the GA based approach outperformed the Fuzzy Logic based approach. The intelligent approach based on GA resulted in more cost saving as compared to what was theoretically foreseen and predicted.

Committee:

Dr. Devinder Kaur, PhD (Committee Chair); Dr. Ezzatollah Salari, PhD (Committee Member); Dr. Mansoor Alam, PhD (Committee Member)

Subjects:

Computer Engineering; Computer Science; Electrical Engineering; Energy; Engineering

Keywords:

smart grid; energy management system; smart meter; fuzzy logic; genetic algorithm

Sampathnarayanan, BalajiAnalysis and Design of Stable and Optimal Energy Management Strategies for Hybrid Electric Vehicles
Doctor of Philosophy, The Ohio State University, 2012, Electrical and Computer Engineering

The ubiquitous influence of fossil fuels in driving the world economy and the imperative need to reduce dependence of transportation on these fuels, has brought about a decade of research on alternative propulsion systems. Of the several alternative propulsion systems, hybrid electric vehicles (HEVs) are seen as an important short-term solution. In the most generic sense, a HEV consists of a battery and one or more electric machines in addition to the engine powered by petroleum/diesel. Depending on the vehicle architecture, the additional degree of freedom in selecting the amount of energy supplied by the primary and the secondary source of energy is a challenging control and optimization problem. The energy management strategy in a HEV aims at finding the optimal distribution of energy between the battery and the fuel to satisfy the requested power from the driver.Different energy management strategies have been developed both by the industry and the academia and they can be classified into non-realizable and realizable energy management strategies based on the amount of information required for real-time implementation. Traditionally, the non-realizable strategies formulate the energy management problem as a constrained optimal control problem of minimizing a performance index over a finite time interval under operational constraints. These strategies provide the global optimal solution and are used as benchmark solutions for comparative analysis of strategies. The realizable strategies in the literature have been primarily developed for implementation in real vehicles and have been shown to produce results similar to the global optimal solution. In spite of the extensive amount of research on both non-realizable and realizable energy management strategies, there are many shortcomings in the literature which have been addressed in this dissertation.

The energy management problem of finding the optimal split between the different sources of energy in a charge-sustaining pre-transmission parallel HEV, ensuring stability and optimality with respect to a performance objective, is addressed in this dissertation. The dissertation develops a generic stability and optimality framework within which energy management strategies can be analyzed and designed. The energy management problem is cast in the form of a nonlinear optimal regulation (with disturbance rejection) problem and a control Lyapunov function is used to design the control law. A series of theorems ensuring optimality and asymptotic stability of the energy management strategy are proposed and proved. The theorems use an appropriate Willans line model of the engine fuel consumption rate and a zero-th order model of the battery state of charge/energy dynamics. The sufficient conditions for optimality and stability are used to derive an analytical expression for the control law as a function of the battery state of charge/state of energy error, engine fuel consumption model and battery model parameters.

In this dissertation, several non-realizable and realizable energy management strategies are developed and implemented in the backward and forward vehicle simulators. The optimal control law (OCL) proposed in this dissertation is compared against dynamic programming (DP) and a version of equivalent consumption minimization strategy (ECMS) based on Pontryagin’s minimum principle. The OCL strategy is further modified to develop a realizable strategy (called real-time OCL) and its performance is compared with an adaptive version of ECMS using a forward vehicle simulator. Throughout the dissertation, the performance of the proposed strategy is evaluated against the global optimal solution from DP. The significant contribution of the dissertation is in developing and easy to implement strategy that has very less calibration effort. Though the framework and the strategy has been presented for a pre-transmission parallel HEV, it is scalable to different vehicle architectures and component sizes. The dissertation also presents a comprehensive comparison of the different proposed and developed energy management strategies.

Committee:

Giorgio Rizzoni, Professor (Advisor); Stephen Yurkovich, Professor (Committee Member); Vadim Utkin, Professor (Committee Member); Yann Guezennec, Professor (Committee Member); Simona Onori, PhD (Committee Member)

Subjects:

Alternative Energy; Automotive Engineering; Electrical Engineering; Mechanical Engineering

Keywords:

hybrid electric vehicles; pre-transmission parallel hev; energy management strategies; nonlinear optimal control with disturbance rejection; optimal control law; constrained optimization problem;

Bovee, Katherine MarieOptimal Control of Electrified Powertrains with the Use of Drive Quality Criteria
Doctor of Philosophy, The Ohio State University, 2015, Mechanical Engineering
In today's world, automotive manufacturers face the difficult challenge of building vehicles that are capable of meeting the increasingly stringent fuel economy and emissions standards, while also maintaining the performance and drive quality that consumers have come to expect. The automotive industry's response to this has been to make increasingly advanced vehicles that require more complex control systems, often resulting in longer development times and higher costs. One way to help reduce the development time and cost associated with these advanced vehicles is to use a model-based design approach. This approach allows engineers to design more of the vehicle's control system in a virtual environment, before hardware is available to test the control software. While model-based design techniques have helped reduce the amount of development time and cost that is needed to design the control system for a vehicle, these model-based techniques may not fully account for a vehicle's drive quality characteristics. Many of the energy management optimal control algorithms for hybrid vehicles designed in virtual environments today are capable of achieving high fuel economy numbers, but may result in poor drive quality characteristics when implemented on a vehicle. Therefore, a new methodology is needed to account for a vehicle's drive quality during the initial stages of a vehicle's control development. The research presented here describes a new methodology where drive quality metrics are added to the optimal control algorithm's cost function, in order to allow the algorithm to find a good balance between fuel economy and drive quality. Although some research has been previously published in this area, the majority of research does not specifically link the criteria used to improve drive quality to the physical behavior of the vehicle. Other research solves the optimal energy management problem to minimize fuel consumption, but then filters the results to prevent drive quality problems. This filtering can potentially result in a non-optimal final solution. The research presented in this dissertation formally links the drive quality behavior of the vehicle to the criteria used in the formulation of the optimal energy management problem. This allows the final solution to the optimal energy management problem to be directly applicable to the vehicle, without the need to filter the results.

Committee:

Giorgio Rizzoni (Advisor); Shawn Midlam-Mohler (Committee Member); Wei Zhang (Committee Member); Manoj Srinivasan (Committee Member)

Subjects:

Mechanical Engineering

Keywords:

drive quality; energy management strategy; hybrid electric vehicle; optimal control; torque shaping; genetic algorithm

Sharma, Oruganti PrashanthA practical implementation of a near optimal energy management strategy based on the Pontryagin's minimum principle in a PHEV
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

Keywords:

PHEV; HEV; hybrid electric; optimal control; pontryagins minimum principle; control; energy management strategy; optimization

Li, HailongAnalytical Model for Energy Management in Wireless Sensor Networks
PhD, University of Cincinnati, 2013, Engineering and Applied Science: Computer Science and Engineering
Wireless sensor networks (WSNs) are one type of ad hoc networks with data-collecting function. Because of the low-power, low-cost features, WSN attracts much attention from both academia and industry. However, since WSN is driven by batteries and the multi-hop transmission pattern introduces energy hole problem, energy management of WSN became one of fundamental issues. In this dissertation, we study the energy management strategies for WSNs. Firstly, we propose a packets propagation scheme for both deterministic and random deployment of WSNs so to prolong their lifetime. The essence of packets propagation scheme is to control transmission power so as to balance the energy consumption for the entire WSN. Secondly, a characteristic correlation based data aggregation approach is presented. Redundant information during data collection can be effectively mitigated so as to reduce the packets transmission in the WSN. Lifetime of WSN is increased with limited overhead. Thirdly, we also provide a two-tier lifetime optimization strategy for wireless visual sensor network (VSN). By deploying redundant cheaper relay nodes into existing VSN, the lifetime of VSN is maximized with minimal cost. Fourthly, our two-tier visual sensor network deployment is further extended considering multiple base stations and image compression technique. Last but not the least, description of UC AirNet WSN project is presented. At the end, we also consider future research topics on energy management schemes for WSN.

Committee:

Dharma Agrawal, D.Sc. (Committee Chair); Kenneth Berman, Ph.D. (Committee Member); Yizong Cheng, Ph.D. (Committee Member); Chia Han, Ph.D. (Committee Member); Wen Ben Jone, Ph.D. (Committee Member)

Subjects:

Computer Engineering

Keywords:

Wireless Sensor Networks;Wireless Visual Sensor Network;Energy Management;Data Aggregation;Gaussian Random Distribution;Lifetime Optimization;

Couch, Jeremy RobertAn ECMS-Based Controller for the Electrical System of a Passenger Vehicle
Master of Science, The Ohio State University, 2013, Mechanical Engineering
A primary concern for automotive manufacturers is increasing the fuel economy of their vehicles. One way to accomplish this is by reducing the losses associated with operating the ancillary loads such as the loads of the vehicle’s electrical system. In the electrical system of a vehicle, the alternator provides current to the electrical loads. The difference between the load current demand and the current provided by the alternator is either accepted or supplied by the battery. Therefore, the current demand of the electrical loads can be met by the alternator, the battery or a combination thereof. While improving the efficiency of the actual components of the electrical system (alternator, battery and electrical loads) is beneficial, additional gains can be realized with a smart control strategy for the alternator. Conventional alternator control strategies make little use of the battery; the power demand from the electrical loads is almost solely met by the alternator. However, since the alternator is directly connected to the engine, this results in increased fuel consumption, particularly at idle speed conditions. To this extent, more advanced control strategies could be implemented to make use of the battery energy buffer to limit the use of the alternator at low engine efficiency conditions. The focus of this thesis is the design of an advanced alternator control strategy. First, a model of a vehicle’s electrical system is developed with control design in mind. The system is modeled starting from a lumped-parameter, energy-based characterization of the battery and alternator. This is followed by a thorough calibration using experimental data and, finally, validation on a vehicle chassis dynamometer considering a standard (production) alternator control strategy. Next, a novel alternator control algorithm is designed by applying the Equivalent Consumption Minimization Strategy (ECMS), a well known energy management approach often used to control the powertrain of hybrid electric vehicles. This strategy works by determining the optimal alternator current to minimize the instantaneous fuel consumption while complying with input and state of charge constraints. The ECMS algorithm was extensively calibrated for a variety of drive cycles and load current profiles. This proposed control strategy was then compared in simulation to the production alternator controller and fuel consumption reductions of up to 2.18% have been shown. An adaptive ECMS (A-ECMS) is then defined, using feedback from the battery’s state of charge to dynamically tune the ECMS calibration parameter in real-time. Simulation results for the A-ECMS show fuel savings compared to the baseline alternator control strategy that are on the same order of magnitude as the ECMS. Furthermore, a robustness study verifies the A-ECMS is insensitive to model inaccuracies, poor tuning of the parameters and variations in the load current profile.

Committee:

Marcello Canova (Advisor); Lisa Fiorentini (Committee Member); Giorgio Rizzoni (Committee Member)

Subjects:

Automotive Engineering; Mechanical Engineering

Keywords:

automotive; energy management; vehicle electrical system; control oriented model; adaptive ECMS