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Closed Loop Throttle Opening Angle Estimation Strategy by Considering Torque Demands from SI Engine
Technical Paper
2018-28-0079
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Electronic throttle control is extensively preferred to vary the air intake in the engine manifold for regulating the torque in order to obtain the better vehicle response, high performance in terms of improving the fuel economy and trim down the emissions of the spark ignition engines. For such type of the engine control systems the throttle angle is estimation is accomplished either by pedal follower or torque based method. This work aims to develop a throttle opening angle estimation strategy in a closed loop manner using fuzzy logic approach by considering real time internal system and driver torque demands for controlling the SI engine. In present work the torque demand from internal system such as catalyst heating, cold start assist and battery voltage compensation is estimated using fuzzy logic strategy. Such intelligent system aims to replace the lookup tables associated with those systems and reduces the calibration effort. For the estimated throttle angle the electronic throttle body is evaluated in an engine test bed simulation on Matlab Simulink platform for the various accelerator pedal inputs conditions. Also, the external or auxiliary torque demand input given in the form of ramp signal and assumed torque cycle. The outcome curves based on simulation for engine torque, speed and power are compared against the open loop method. The simulation result of the proposed approach shows betterment than the existing method.. Angular error of the proposed closed loop method is in lower range of 5.3 deg and 4.1 deg for ramp and step based driving cycle inputs respectively. The error value for open loop method is in the higher side of 7.4 deg and 6.7 deg.
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B, A., Denis Ashok, S., and Kavitha, C., "Closed Loop Throttle Opening Angle Estimation Strategy by Considering Torque Demands from SI Engine," SAE Technical Paper 2018-28-0079, 2018, https://doi.org/10.4271/2018-28-0079.Data Sets - Support Documents
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