基于能量优化方案的感应电机驱动电动汽车无传感器速度估计策略

Abhisek Pal, Sukanta Das
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引用次数: 11

摘要

本文旨在提出一种适用于电动汽车的感应电机(IM)驱动的无传感器速度控制技术,该技术采用模型参考自适应系统(MRAS),并采用黄金分割法作为基本能量优化方案。所提出的MRAS是通过在参考模型和自适应模型中分别使用虚拟电阻(R)的瞬时值和稳态值来开发的。该优化方案产生了机器运行的最优转子磁链水平,并通过降低IM铁心损耗提高了驱动系统的效率。所开发的整体方案不受定子电阻变化的影响。此外,MRAS的独特结构完全消除了任何通量估计的需要。因此,该方法对与积分器相关的问题不敏感,如漂移和饱和,即使从零到额定速度也能准确估计速度。在此背景下,所有的相关研究都是在MATLAB/Simulink中完成的。最后给出了一些实验结果来验证仿真结果。
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A new sensorless speed estimation strategy for induction motor driven electric vehicle with energy optimization scheme
This paper aims to propose a new sensorless speed control technique for induction motor (IM) drive suitable for electric vehicle applications using model reference adaptive system (MRAS) with a basic energy optimization scheme known as golden section method. The proposed MRAS is developed by using instantaneous and steady state values of a fictitious resistance (R) in the reference and adaptive models respectively. The optimization scheme generates the optimal level of rotor flux for machine operation and improves the efficiency of the drive system by bringing down the IM core loss. The developed overall scheme is immune to the stator resistance variation. Moreover, the unique formation of the MRAS eliminates completely the requirement of any flux estimation. Thus, the method is insensitive to the integrator-related problems like drift and saturation enabling an accurate estimation of speed even from zero to the rated speed. In this context, all the relevant studies are done in MATLAB/Simulink. A few experimental results are also presented to validate the simulation results.
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