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Selection of electrical machine is a key issue in electrical and hybrid electrical vehicles (HEVs and EVs). As far as HEVs and EVs are concerned, mechanical ruggedness of electrical machine is of utmost importance. For these reasons, the application of switched reluctance (SR) machine in EVs and HEVs is a viable option. In this study, an SR generator (SRG) has been utilised to work as a battery charger in EVs. The current ripple of the SRG is the greatest issue when it works as a battery charger. Therefore, a power converter and a smart search control (SSC) approach are proposed in this study to decrease the current ripple when the output power has a maximum quantity. During the system operation, the SSC method determines the value of excitation angle, turn-on and turn-off angles for each phase of SRG. The simulation and experimental results demonstrate the eligibility of the SSC system and power converter to obtain the acceptable charging current in maximum generated power during battery charging.

In some applications such as electric vehicles, electric motors should operate in a wide torque and speed ranges. An efficiency map is the contour plot of the maximum efficiency of an electric machine in torque-speed plane. It is used to provide an overview on the performance of an electric machine when operates in different operating points. The electric machine losses in different torque and speed operating points play a prominent role in the efficiency of the machines. In this paper, an overview about the change of various loss components in torque-speed envelope of the electric machines is rendered to show the role and significance of each loss component in a wide range of torque and speeds. The research gaps and future research subjects based on the conducted review are reported. The role and possibility of the utilization of the computational intelligence-based modeling of the losses in improvement of the loss estimation is discussed.

The IEEE Publication Services & Products Board (PSPB) last revised its Operations Manual Section 8.1.9 on Electronic Information Dissemination (known familiarly as "author posting policy") on 7 December 2012.PSPB accepted the recommendations of an ad hoc committee, which reviewed the policy that had previously been revised in November 2010. The highlights of the current policy are as follows:• The policy reaffirms the principle that authors are free to post their own version of their IEEE periodical or conference articles on their personal Web sites, those of their employers, or their funding agencies for the purpose of meeting public availability requirements prescribed by their funding agencies. Authors may post their version of an article as accepted for publication in an IEEE periodical or conference proceedings. Posting of the final PDF, as published by IEEE Xplore®, continues to be prohibited, except for open-access journal articles supported by payment of an article processing charge (APC), whose authors may freely post the final version.• The policy provides that IEEE periodicals will make available to each author a preprint version of that person's article that includes the Digital Object Identifier, IEEE's copyright notice, and a notice showing the article has been accepted for publication. • The policy states that authors are allowed to post versions of their articles on approved third-party servers that are operated by not-for-profit organizations. Because IEEE policy provides that authors are free to follow public access mandates of government funding agencies, IEEE authors may follow requirements to deposit their accepted manuscripts in those government repositories.

The ability of artificial intelligence and machine learning techniques in classification and detection of the types of data in large datasets lead to their popularity among scientists and researchers. Because of the presence of different load at different times in power systems, it is hard to provide an accurate mathematical model for such systems. On the other hand, most of the available protection devices in power grids work based on the estimated mathematical models of the grid. For this reason, power system utilizers usually suffer from the low accuracy of the available protection systems in fault detection and diagnosis. In this paper, a reliable machine learning technique is proposed to detect and classify different faults of smart grids. The proposed technique benefits from the principal component analysis (PCA) and linear discriminant analysis (LDA). The PCA is used to reduce the size of the dataset matrixes. The applied PCA reduces the dataset sizes and eliminates the possible singularity of the datasets. The LDA method is applied to the outputs data of the PCA to minimize the within class distance of the dataset and maximize the distance between classes. Finally, the well-known K-nearest neighbor technique is applied to detect the fault and determine its classes. The paper results demonstrate the effectiveness and robustness of the proposed algorithm in the determination of the fault class in smart grids.

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