The use of PEVs (Plug-in Electric Vehicles) is fast expanding due to their low energy cost and low environmental pollution. However, a big hurdle is that PEVs have a short driving range and long battery charging time even when using supercharging stations. Therefore, better queuing models are necessary to improve the quality of services using public charging stations. This thesis develops an approach for estimating various discharging profiles of PEV batteries considering different regional driving cycles. Each driving cycle generates a unique discharging profile. These discharging profiles were employed in a computer model to study recharging process of PEVs in public charging stations. Moreover, a unique utility function is construed which is optimized to minimize the overall waiting time for consumers and harmonize the queue size in each charging station. This model uses Toronto downtown area as a case study.
<span>Today power electronics play an important role in the electric industry. Power electronic converters are an inseparable component in power systems. One of these converters is DC/AC inverter that is widely used in power systems, industrial applications, electric motor drive and electric vehicles. Due to the tense situation with the complexity that exists in these applications, inverters are exposed to failure. The fault occurring in inverter can cause disturbance and damaging harmonics, cut some industrial processes to in the power system or in the case of electric vehicles, causing irreparable damage. For this reason, detecting faults in the inverter is very important. In this paper, open circuit fault of IGBT in an electric vehicle has been examined. We use three-phase current and wavelet transform to identify the state of the system and we can extract current waveform characteristics. We use neural network algorithm for fault detection and classification. An electric vehicle in 5 different speeds and 5 different torque and a total of 220 failure modes have been studied and tested. The results show the method has been succeeded to detection all forms of defined faults</span>
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