The primary objective of this work is to optimize the induction motor rotor flux so that maximum efficiency is attained in the facets of parameter and load variations. The conventional approaches based on loss model are sensitive to modelling accuracy and parameter variations. The problem is further aggravated due to nonlinear motor parameters in different speed regions. Therefore, this work introduces an adaptive neuro-fuzzy inference system-based rotor flux estimator for electric vehicle. The proposed estimator is an amalgamation of fuzzy inference system and artificial neural network, in which fuzzy inference system is designed using artificial neural network. The training data for neuro-fuzzy estimator is generated offline by acquiring rotor flux for different values of torque. The conventional fuzzy logic and differential calculation methods are also developed for comparative analysis. The efficacy of developed system is established by analyzing it under varying load conditions. It is revealed from the results that suggested methodology provides an improved efficiency i.e. 94.51% in comparison to 82.68% for constant flux operation.

中文翻译：

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基于神经模糊的电动汽车转子磁通估计

这项工作的主要目标是优化感应电机转子磁通，以便在参数和负载变化方面获得最大效率。基于损失模型的传统方法对建模精度和参数变化很敏感。由于不同速度区域的非线性电机参数，问题进一步恶化。因此，这项工作为电动汽车引入了一种基于自适应神经模糊推理系统的转子磁通估计器。所提出的估计器是模糊推理系统和人工神经网络的融合，其中使用人工神经网络设计模糊推理系统。通过获取不同扭矩值的转子​​磁通，离线生成神经模糊估计器的训练数据。传统的模糊逻辑和微分计算方法也被开发用于比较分析。所开发系统的功效是通过在不同负载条件下对其进行分析来确定的。结果表明，与恒定通量操作的 82.68% 相比，建议的方法提供了改进的效率，即 94.51%。