Download or read book Supervisory Control Strategy Development for a Hybrid Electric Vehicle written by Bo Gu and published by . This book was released on 2006 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: As crude oil price rises, the advantages of Hybrid Electric Vehicle (HEV) are more and more attractive to the automotive industry and customers. The work of this thesis is aimed toward the Ohio State University's objective as a participant in the Challenge X competition. During the first year of this three-year project, the vehicle architecture is carefully chosen. Vehicle modeling, simulation and control algorithm designs are partially completed. This thesis covers work completed in the second year -- further improving the supervisory control strategy and its application in a microcontroller system. In the first two chapters, HEV technologies and designs are reviewed. Advantages of HEV are outlined and how such advantages can be achieved is explained. The Challenge X architecture is then introduced. Current control algorithms for HEV are reviewed. In Chapter 3, a quasi-static model of HEV is introduced. In the quasi-static model, the dynamics of the powertrain are not considered. Instead, most of components of the powertrain are simplified as maps. Such approach provides acceptable approximation of vehicle for designing the supervisory control algorithms for energy management, and for further optimization. Novel energy management algorithms are introduced in Chapters 4 and 5. A 3-way Equivalent (fuel) Consumption Minimization Strategy (ECMS), a P1 State-of-Charge management algorithm and an adaptive version of ECMS based on driving pattern recognition are introduced. ECMS provides real-time near-optimal energy management decisions by minimizing the "equivalent" fuel consumption, which is a combination of the actual fuel consumption and electrical energy use. An equivalence factor converts electrical power consumption into fuel consumption, based on the average efficiency of the battery in discharge/recharge and the efficiencies of electric motors and other devices. A driving pattern recognition method is used to obtain better estimation of the equivalence factor. Eighteen standard driving cycles provided by the Environmental Protection Agency are analyzed. Twenty one different cycle-characterizing quantities, such as average, peak and rms velocity, are extracted. Using the ideas of Principal Component Analysis and of statistical clustering, 18 driving cycles are classified into four Representative Driving Patterns (RDP), such as urban and highway. While the vehicle is running, a time window of past driving conditions is analyzed periodically and recognized as one of the four RDPs. Periodically updating the control parameter according to the driving conditions yields more precise estimation of the equivalent fuel consumption cost, thus providing better fuel economy. Besides minimizing the instantaneous equivalent fuel consumption, the battery State of Charge (SOC) is also maintained by using a P1 controller to keep the SOC around a nominal value. Such control algorithm does not require the knowledge of future driving cycles and has a low additional computational burden. Results obtained in this research shows that the driving conditions can be successfully recognized and good performance can be achieved in various driving conditions while sustaining battery Soc within desired limits. chapter 6 focuses on how to convert the control algorithm applied in the simulator into real-time implementation in the microcontroller systems. A set of 6 dimensional maps is generated and stored for real-time application, according to the computation limitation of the microcontroller. Simulation results show that real-time solution based on look-up tables have similar results as those provided by instantaneous calculation. Therefore the microcontroller system version of supervisory control strategy is acceptable for implementation. The contributions of this thesis extend previous research conducted at the OSU center for Automotive Research, and include: the successful implementation of 3-way ECMS control strategy in the challenge x vehicle; the design of the new adaptive-EcMS; and the implementation of supervisory control strategy in the microcontroller systems. A PDF copy of this thesis with color figures is available from the center for Automotive Research, the Ohio State University. It is also available from gu.4Oosu.edu upon request.