NDLI: Rule-Based Fuzzy Gain-Scheduling PI Controller to Improve Engine Speed and Power Behavior in a Power-split Hybrid Electric Vehicle

Content Provider	IEEE Xplore Digital Library
Author	Syed, F.U. Hao Ying Ming Kuang Okubo, S. Smith, M.
Copyright Year	2006
Description	Author affiliation: Sustainable Mobility Technol. & Hybrid Programs, Ford Motor Co., Dearborn, MI (Syed, F.U.)
Abstract	Environmental awareness has resulted in greater emphasis on developing more environmentally friendly and fuel efficient vehicles. Hybrid electric vehicles (HEVs) have been considered a viable option towards achieving these goals. Ford Motor Company developed a full hybrid electric vehicle with an e-CVT (electronically controlled continuously variable transmission) or power-split hybrid powertrain with an integrated motor and generator. The power-split hybrid system uses planetary gear sets to connect an engine, a generator, and a motor. This HEV powertrain exhibits great potential to improve fuel economy by enabling the engine to operate at its most efficient region independent of the vehicle speed. To achieve fuel economy improvements of the power-split hybrid system, high-voltage (HV) battery power management is critical. To control actual HV battery power in such vehicles, a sophisticated control system is essential which controls engine power and thereby engine speed to achieve the desired HV battery maintenance power. Conventional approaches use proportional-integral (PI) control systems to control the actual HV battery power in power-split hybrid system, which can sometimes result in either overshoots of engine speed and power or degraded response and settling times due to the nonlinearity of the power-split hybrid system. Such an overshoot is often objectionable to customers, which see engine speed overshoots as disconnect between the driver's request and the engine response. This issue comes from the fact that a complete high fidelity mathematical model for the power-split HEV system along with the environmental effects cannot be accurately modeled inside the controller. Therefore, a controller adaptable to nonlinear behavior and not requiring detailed knowledge of mathematical model of the plant is required to address such issues. Fuzzy control approaches can provide a way to cope with the limitations of the conventional controllers. We have developed a fuzzy control approach with minimal rules to intelligently control engine power and speed behavior in a powersplit HEV. This approach uses selective minimal rule-based fuzzy gain-scheduling to determine appropriate gains for the PI controller based on the system's operating conditions. The improvements result in the reduction of the overshoots without compromising system's response and settling times in comparison with the conventional linear PI controller. This paper describes the power-split hybrid vehicle's powertrain system and key subsystems. It also describes minimal rule-based fuzzy gain-scheduling PI controller and the formulation of minimal fuzzy rules required to achieve the desired behavior. This minimal rule-based fuzzy controller was implemented in a Ford Escape hybrid vehicle and was evaluated in the vehicle test environment for a comparative analysis of the results to show its effectiveness. The results clearly demonstrate that the designed minimal rule based fuzzy gains scheduling controller is capable of significantly improving the engine speed and power behavior in a power-split HEV without compromising the system's response and settling times
Starting Page	284
Ending Page	289
File Size	3852469
Page Count	6
File Format	PDF
ISBN	1424403626
DOI	10.1109/NAFIPS.2006.365423
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-03
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy control Engines Hybrid electric vehicles Control systems Mechanical power transmission Batteries Time factors Mathematical model Power generation Hybrid power systems
Content Type	Text
Resource Type	Article

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