In this paper, the fault-tolerance capability of IM-drive is studied. The discussion on the faulttolerance of IM drives in the literature has mostly been on the conceptual level without any detailed analysis. Most of studies are only achieved experimentally. This paper provides an analytical tool to quickly analyze and predict the performance under fault conditions. Also, most of the presented results were machine specific and not general enough to be applicable as an evaluation tool. So, this paper will present a generalized method for predicting the post-fault performance of IM-drives after identifying the various faults that can occur. The fault analysis for IM in the motoring mode will be presented in this paper. The paper includes an analysis for different classifications of drive faults. The faults in an IM-drive -that will be studied-can be broadly classified as: machine fault, (i.e., one of stator windings is open or short, multiple phase open or short, bearings, and rotor bar is broken) and inverter-converter faults (i.e., phase switch open or short, multiple phase fault, and DC-link voltage drop). Briefly, a general-purpose software package for variety of IM-drive faults -is introduced. This package is very important in IM-fault diagnosis and detection using artificial intelligent techniques, wavelet and signal processing.

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The estimation of vehicle battery performance is typically addressed by testing the battery under specific operation conditions by using a model to represent the test results. Approaches for representing test results range from simple statistical models to neural networks to complex, physics-based models. Basing the model on test data could be problematical when testing becomes impractical with many years life time tests. So, real time estimation of battery performance, an important problem in automotive applications, falls into this area. In vehicles it is important to know the state of charge of the batteries in order to prevent vehicle stranding and to ensure that the full range of the vehicle operation is exploited.In this paper, several battery models have studied including analytical, electrical circuits, stochastic and electrochemical models. Valve Regulated Lead Acid "VRLA" battery has been modelled using electric circuit technique. This model is considered in the proposed Battery Monitoring System "BMS". The proposed BMS includes data acquisition, data analysis and prediction of battery performance under a hypothetical future loads. Based on these criteria, a microprocessor based BMS prototype had been built and tested in automotive Lab,. The tests show promising results that can be used in industrial applications

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