Efficiency of Intelligent Control Design for Dual-Stator Machine on the EV Traction System

PMSM Inter-Turn Short Circuit Fault Detection Using the Fuzzy-Extended Kalman Filter in Electric Vehicles

“…Thence, fuzzy logic is considered an appropriate intelligent approach to identify the optimal value of Rs and helps EKF to detect the ITSC. Figure 4 illustrates the fundamental structure of the fuzzy stator resistance estimator [17].…”

“…Thence, fuzzy logic is considered an appropriate intelligent approach to identify the optimal value of Rs and helps EKF to detect the ITSC. Figure 4 illustrates the fundamental structure of the fuzzy stator resistance estimator [17]. To predict the error of the stator resistance e, the discrepancy in the stator current vector between the estimated value I* and the reference value I is measured and denoted as the stator current vector error and defined as follows: 𝑒 = 𝐼 * − 𝐼 = (𝑖 * ) − (𝑖 * ) − 𝑖 − 𝑖 (15) The fuzzy estimator acquires the stator resistance error e and its derivative e' as inputs after they were normalized.…”

“…The utilization of triangular membership functions, as depicted in Figure 5, has facilitated the conversion of the clear-cut intake and the outturn variables into the corresponding fuzzy variables. The inputs e and e' exhibit identical distribution characteristics and possess identical membership functions [17]. To predict the error of the stator resistance e, the discrepancy in the stator current vector between the estimated value I* and the reference value I is measured and denoted as the stator current vector error and defined as follows:…”

“…The utilization of triangular membership functions, as depicted in Figure 5, has facilitated the conversion of the clear-cut intake and the outturn variables into the corresponding fuzzy variables. The inputs e and e exhibit identical distribution characteristics and possess identical membership functions [17].…”

“…Currently, the knowledge-data diagnostic techniques that have reached a higher level of development and sophistication are Support Vector Machine (SVM), expert systems, neural networks (NN) and fuzzy logic (FL), and deep learning [4,[16][17][18]. The efficacy and resilience of these methods have been demonstrated in detecting faults [19][20][21].…”