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This work presents a field oriented control (FOC) strategy (Fuzzy Logic (FL)) associated with PI controller applied to the control system of an permanent magnet synchronous motor (PMSM) powered by an inverter dedicated to electric vehicles, the major challenge of our research work is a control law for a permanent magnet synchronous motor more efficient in terms of rejection of disturbances; stability and robustness with respect to parametric uncertainties, A comparison of the performance of the proposed FOC with the FOC with the fuzzy-PI will be presented. The overall development scheme is summarized and an example illustrates features of the control approach performed on a 0.5 kW PMSM drive. The torque and the speed will be judged and compared for the two orders offered. As results, the behavior of the FOC based on fuzzy-PI controller is more efficient compared to the conventional vector control.

The Switched Reluctance Machine (SRM) is ideal for safety critical applications due to its superior fault-tolerance characteristics. The switched reluctance drive is known to be fault tolerant, but it is not fault free. Fault diagnosis of SRM in the critical applications is often a difficult and daunting task. Thus, finding efficient and reliable fault diagnostics methods especially for SR machines is extremely important. This paper focuses on the development, and application of modern statistical classifier method, namely Hidden Markov Model (HMM) associated with a smoothed ambiguity plane Time-Frequency Representation (RTF) for the diagnosis based classification of electrical faults in this particular machine. The RTF-HMM Technique is composed of two steps: the Feature Extraction step based on the smoothed ambiguity plane designed for maximizing the separability between classes using Fisher's discriminant ratio and Hidden Markov Model algorithm applied for the classification step. The algorithm of each step is well developed. Classifier development and training data is carried out by the HMM using a set of fault scenarios, between healthy, single and combined faults, in terms of torque at different load level in order to deduce the fault severity. Parameter training of Hidden Markov Models generally need huge a mounts of historical data. Experimental results proves that the use of RTF-HMM based approaches is a suitable strategy for the automatic classification of new sample independent from de type of fault signal.

In the case of one phase failure, the switched reluctance motor(SRM) will behave nearly the same, both in open circuit and in short circuit failure. This means, that the machine will understand the two faults in the same way which makes the SRM faults detection and diagnosis a more challenging task. This paper presents a diagnosis method based on pattern recognition analysis to detect and to classify automatically the electrical faults, short- and open-circuit under any level of load of the studied system: redundant three-phase power converter fed 6/4 SRM. The phases making a pattern recognition diagnosis of SRM, the training and the decision. The training phase consists in determining the pattern vector and the optimal kernels design (the separating classes) by Time-Frequency Representation (TFR). The training data is carried out using a set of fault scenarios, between healthy, single and combined faults, in terms of torque measurement at different load level, in order to deduce the fault severity. The second phase, consists in associating an unknown pattern with one of the defined classes, according to the "k-nearest neighbors" (knn) decision rule, associated with Kalman estimator to tracking of various operating modes and to predict the evolution of the call out of the knowledge database for a given operating mode in order to realize a preventive maintenance. The experimental results prove the efficiency of pattern recognition methods in condition monitoring of reluctance machine.

The most problem in electric vehicles is the detection of faults in the battery; in this paper we discuss a systematic data process for detecting and diagnosing faults in the battery and the application of the method of neural networks for the classification of the various faults of the Li-Ion battery dedicated to the electric vehicle. ; and for that we tried to create a fault classification algorithm using the neural network commands that exist in the MATLAB, we used the MATLAB/Simscape for battery modeling, the latter prepared physical models for use in different fields; and based on this model, we identified the battery parameters and we will apply some faults to classify them with neural networks; creating an algorithm takes a long time but when we use these commands we have to do the classification and the MATLAB gives us the algorithm., These algorithms have shown the efficiency of the application of pattern recognition to the diagnosis.