Fazal Syed - Academia.edu (original) (raw)

Papers by Fazal Syed

NAFIPS 2005 - 2005 Annual Meeting of the North American Fuzzy Information Processing Society, 2000

ABSTRACT With the recent emphasis on developing more environmentally friendly and fuel-efficient ... more ABSTRACT With the recent emphasis on developing more environmentally friendly and fuel-efficient vehicles, Ford Motor Company developed a full hybrid electric vehicle (HEV) with a power-split hybrid powertrain consisting of an integrated motor and a generator. The power-split hybrid consists of two powertrains; an engine and an electric drive system. This powertrain provides a great potential to improve fuel economy in part due to its ability to operate engine at efficient regions independent of the vehicle speed. The engine speed determination in such a system depends on the desired high voltage (HV) battery power and the driver demand (driver torque/power request). Clearly, in order to control HV battery power to a desired power, a sophisticated controls system is essential which controls engine power to achieve the desired HV battery power. The desired engine power in turn determines the desired engine speed. It is essential that engine speed operation is smooth and stable with an acceptable response. Use of a classical proportional-integral (PI) based control system to control HV battery power is limited due to the nonlinear behavior of the powertrain, and results either in an undesired engine speed stability behavior under certain driving conditions or degraded response time. This paper presents a new nonlinear controls scheme based on a fuzzy controller to resolve the undesired engine speed behavior while achieving desired engine speed response and improved high-voltage battery power controls. Simulations are conducted with this controller and results show that the proposed fuzzy controller improves HV battery power controls and thereby the engine speed behavior and response time (e.g., no overshoots, improved settling time, and uncompromised rise time).

NAFIPS 2008 - 2008 Annual Meeting of the North American Fuzzy Information Processing Society, 2008

ABSTRACT In this paper, we present an improved version of the advisory system for fuel economy im... more ABSTRACT In this paper, we present an improved version of the advisory system for fuel economy improvement in a hybrid electric vehicle [11]. We address the competing requirements for improved fuel economy, while maintaining performance that is close to the current driving style and driver behavior. This is done by introducing a multiple-input, multiple-output rule base with a fuzzy reasoning mechanism that decomposes the space of the main factors that affect vehicle fuel economy and performance - instantaneous fuel consumption, acceleration, speed, and accelerator pedal position. This approach allows us to properly assign the boundaries of the desired accelerator pedal position that correspond to each of the specific areas, which are defined by the rules' antecedents. The system was developed and validated on the Ford (INSERT YEAR and make like SE) HEV Escape vehicle.

NAFIPS 2007 - 2007 Annual Meeting of the North American Fuzzy Information Processing Society, 2007

ABSTRACT Environmental awareness has resulted in greater emphasis on developing more environmenta... more ABSTRACT Environmental awareness has resulted in greater emphasis on developing more environmentally friendly and fuel efficient vehicles in automotive field. Hybrid electric vehicles (HEVs) are a viable option towards achieving these goals. Ford Motor Company developed a full HEV with an e-CVT (electronically controlled continuously variable transmission), which is a power-split hybrid system power train with an integrated motor and generator. This power train exhibits great potential to improve fuel economy. Achievement of high fuel economy, however, significantly depends on the driver behavior which plays a crucial role in the full utilization of the advantages of the HEV technology. In this paper we discuss an intelligent fuzzy advisory system called the "Fuzzy Rule-Based Driver Advisory System" for Fuel Economy Improvement that automatically identifies driver's style, intentions, and preferences and provides guidance to the driver for selecting the optimal driving strategy that results in maximal fuel economy. The proposed advisory system consists of two fuzzy logic controllers (FLCs) that determine the maximal driver demand corresponding to a desired fuel economy level under current operating conditions. The output of the controller is the dynamically calculated upper bounds of the driver demand that is continually conveyed to the driver. The system serves as an automatic advisor guiding the driver to a performance that maximizes the fuel economy without significantly reducing the vehicle's speed. This Fuzzy Rule-Based Driver Advisory System for Fuel Economy Improvement was tested in a simulation environment for a Ford Escape Hybrid. Simulations results demonstrated that the proposed driver advisory system improves the overall fuel economy of the HEV by up to 3.5% without significantly compromising vehicle performance.

NAFIPS 2009 - 2009 Annual Meeting of the North American Fuzzy Information Processing Society, 2009

ABSTRACT In this paper, we present a fuzzy logic based adaptive algorithm with a learning mechani... more ABSTRACT In this paper, we present a fuzzy logic based adaptive algorithm with a learning mechanism that estimates driver's long term and short term preferences. The algorithm represents a significant advancement to the capability of our previous non-adaptive real-time fuel economy advisory system that was implemented in a Ford Escape Hybrid [8][9]. This real-time advisory system proposed in [8][9]achieved improved fuel economy by providing visual and haptic feedbacks to the driver to change his or her driving style or behavior for a given vehicle condition. It was tuned to maximize fuel economy without significantly impacting the performance of the vehicle. Some drivers may perceive it's feedback to be intrusive on one extreme while some other drivers may feel it ineffective on another extreme, depending on the driver's driving styles. The new adaptive algorithm learns driver's intentions by monitoring their driving styles and behaviors, and addresses the issues of intrusiveness of the advisory feedback. This proposed adaptive algorithm balances the competing requirements for improved fuel economy and drivability by maintaining vehicle performance that is acceptable to the current driver's driving style and behavior while providing mechanism to improve fuel economy. This system was developed and validated on the Ford Escape Hybrid vehicle. Experimental results show that the proposed adaptive algorithm is capable of improving driver's behavior and style without being perceived as ineffective or intrusive and achieves fuel economy improvements.

NAFIPS 2006 - 2006 Annual Meeting of the North American Fuzzy Information Processing Society, 2006

Abstract Environmental awareness has resulted in greater emphasis on developing more environmenta... more 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 ...

SAE Technical Paper Series, 2004

IEEE Transactions on Vehicular Technology, 2000