Abstract
This article deals with an intelligent adaptive neural network (ANN) controller for a direct torque controlled (DTC) electric vehicle (EV) propulsion system. With the realization of artificial intelligence (AI) conferred adaptive controllers, the torque control of an electric car (eCAR) propulsion motor can be achieved by estimating the stator reference flux voltage used to synthesize the space vector pulse width modulation (SVPWM) for a DTC scheme. The proposed ANN tool optimizes the parameters of a proportional integral (PI) controller with real-time data and offers splendid dynamic stability. The response of an ANN controller is examined over standard drive cycles to validate the performance of an eCAR in terms of drive range and energy efficiency using MATLAB simulation software.
Highlights
IntroductionThe automotive market has been dealt a lethal blow in recent years but will soon reach its former prominence
Intelligent Adaptive Neural NetworkThe automotive market has been dealt a lethal blow in recent years but will soon reach its former prominence
In [4], a sensorless direct torque controlled (DTC) scheme with an online adaptive neural network (ANN) speed estimator based on a constant V/F control technique for a three phase induction motor (IM) drive was proposed
Summary
The automotive market has been dealt a lethal blow in recent years but will soon reach its former prominence. In [4], a sensorless DTC scheme with an online adaptive neural network (ANN) speed estimator based on a constant V/F control technique for a three phase IM drive was proposed 2021, 12, 44 sensorless DTC scheme with an online adaptive neural network (ANN) speed estimator based on a constant V/F control technique for a three phase IM drive was proposed. Based scheme was integrated with a space vector pulse width modulation (SVPWM) based DTC scheme wasimplemented three phase induction motor as as an an eCAR propulsion system with road load.
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