High Level Modeling Supervisory Control Strategy Development And Validation For A Proposed Power Split Hybrid Electric Vehicle Design

High-level Modeling, Supervisory Control Strategy Development, and Validation for a Proposed Power-split Hybrid-electric Vehicle Design

Over the last decade, hybrid-electric vehicles have progressed from a futuristic icon to a firm production reality for a growing number of automobile manufacturers. While the motivation for this trend may vary, hybrid-electric vehicles today symbolize a recognition of the necessity to evolve advanced automotive technologies in order to sustain a culture of freedom of mobility. The Challenge X program communicates this message towards academia and future automotive engineers with strong support from both government and industry. The work of this thesis was aimed toward The Ohio State University's objectives as a participant in the Challenge X competition. As an initial task, the Ohio State team defined a set of vehicle technical specifications to steer and motivate the vehicle design and control strategy development. After an extensive decision-making process, a specific architecture emerged with the potential to meet the vehicle technical specifications. The chosen configuration is a charge-sustaining, power-split, hybrid-electric vehicle design. A downsized Diesel engine and integrated starter/alternator drive the front wheels through an automatic transaxle. A larger, tractive electric machine and single-speed gearbox exist on the rear drivetrain. Both electric machines and their respective inverters connect electrically to a single high-voltage battery pack. The validation procedure for both the vehicle architecture and a control strategy involves use of a computer vehicle simulator. A quasi-static vehicle model acts as a basis for a simulator to validate the design and control strategy with respect to energy management. A dynamic vehicle model establishes a foundation for eventual creation of a second simulator for drivability validation. Both simulators operate in a forward-moving fashion and contain three primary sections: (i) the driver, (ii) the hybrid-electric powertrain, and (iii) the vehicle. Both models are also highly nonlinear, but the main differentiating property is the relatively large system order of the dynamic model as compared to the quasi-static model. The high-level supervisory control strategy strives to accomplish certain objectives. The initial task involves appropriately selecting the vehicle mode from those predefined as being advantageous to the particular architecture. The control strategy then calculates the driver power request and commands the powertrain actuators so as to meet that request. In certain and applicable vehicle modes, the torque split also aims to minimize fuel consumption. High-voltage battery pack state-of-charge management is both indirectly and inherently incorporated into the fuel consumption minimization approach. As a future task, drivability assurance may involve a final adjustment of control strategy commands so as to respect certain levels of several identified drivability metrics during the vehicle response. Rapid prototyping with a rolling chassis apparatus provided a method of investigation into the pragmaticality of solely utilizing the tractive electric machine and high-voltage battery pack for vehicle propulsion. Initial experimentation validates functionality of the electric machine and inverter and also indicates potential for the power electronics system to act alone in acceptably accelerating the vehicle inertia from a rest. More revealing analysis of the vehicle architecture and control strategy occurred via software-in-the-loop techniques using a simulator based upon the quasi-static vehicle model. Simulation results verify expected fuel economy gains from conversion to a downsized Diesel engine, engine disablement at a vehicle rest, and regenerative braking. However, the simulator also demonstrates a reduced fuel economy from extended operation of the vehicle in a pure electric mode. Moreover, the simulator indicates a concern with the ability of the tractive electric machine and proposed high-voltage battery pack to sufficiently and solely power the vehicle in a pure electric mode. Further findings of the simulated vehicle in full hybrid-electric vehicle operation clearly reveal the control strategy's preference in exclusively relying upon the Diesel engine for most normal operation. Reasons for this behavior primarily result from the relatively high efficiency of the Diesel engine and ensuing lack of opportunity to improve overall system efficiency through engine load shifting. Still, the downsized engine necessitates some presence of power electronics for supplementation during large power requests. Therefore, for this particular vehicle architecture, the control strategy may be better suited to simply maintain sufficient charge of the high-voltage battery pack for supplemental power delivery as opposed to aggressive and frequent use of the electric machines. Reflection of these simulation results along with some certain intangible issues motivates several suggestions concerning a few particular potential vehicle architecture modifications for consideration and contemplation by the Ohio State Challenge X team.

Intelligent Control of Connected Plug-in Hybrid Electric Vehicles

Intelligent Control of Connected Plug-in Hybrid Electric Vehicles presents the development of real-time intelligent control systems for plug-in hybrid electric vehicles, which involves control-oriented modelling, controller design, and performance evaluation. The controllers outlined in the book take advantage of advances in vehicle communications technologies, such as global positioning systems, intelligent transportation systems, geographic information systems, and other on-board sensors, in order to provide look-ahead trip data. The book contains simple and efficient models and fast optimization algorithms for the devised controllers to address the challenge of real-time implementation in the design of complex control systems. Using the look-ahead trip information, the authors of the book propose intelligent optimal model-based control systems to minimize the total energy cost, for both grid-derived electricity and fuel. The multilayer intelligent control system proposed consists of trip planning, an ecological cruise controller, and a route-based energy management system. An algorithm that is designed to take advantage of previewed trip information to optimize battery depletion profiles is presented in the book. Different control strategies are compared and ways in which connecting vehicles via vehicle-to-vehicle communication can improve system performance are detailed. Intelligent Control of Connected Plug-in Hybrid Electric Vehicles is a useful source of information for postgraduate students and researchers in academic institutions participating in automotive research activities. Engineers and designers working in research and development for automotive companies will also find this book of interest. Advances in Industrial Control reports and encourages the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.

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