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• This paper exposes two special AC machines adaptable for electric vehicle application. • Their mathematical models are described and defined. • Their corresponding field oriented vector control topology was designed and verified through simulation steps. There has been a rapid adoption of electric vehicles (EVs), and the industry has been looking for the most enhanced traction system to improve performance and reliability. Such specifications are mostly dependent on the magnetic materials for brushless DC or AC machines, and typically EVs are manufactured with those machines. In the past two decades, there have also been improved performance ratings of power electronic devices. On one hand, making possible AC motors more suitable for EVs. On the other hand, it allows further advanced topology circuits to fit machines with two rotors or two stators. This paper shows two configurations of electrical machine control for EVs, discuss the pros and cons, and corresponding mathematical models have been developed in order to enhance a Field Oriented Control (FOC) strategy, which has been analyzed and studied through MATLAB based simulations, allowing novel design based on the analytical models developed by the authors. [Display omitted] [ABSTRACT FROM AUTHOR]

The access of large-scale electric vehicles (EVs) will increase the network loss of medium voltage distribution network, which can be alleviated by adjusting the network structure and orderly charging for EVs. However, it is difficult to accurately evaluate the charging efficiency in the orderly charging of electric vehicle (EV), which will cause the scheduling model to be insufficiently accurate. Therefore, this paper proposes an EV double-layer scheduling model based on the isolated bidirectional DC–DC (IBDC) converter optimal efficiency model, and establishes the hierarchical and partitioned optimization model with feeder–branch–load layer. Firstly, based on the actual topology of medium voltage distribution network, a dynamic reconfiguration model between switching stations is established with the goal of load balancing. Secondly, with the goal of minimizing the branch layer network loss, a dynamic reconstruction model under the switch station is established, and the chaotic niche particle swarm optimization is proposed to improve the global search capability and iteration speed. Finally, the power transmission loss model of IBDC converter is established, and the optimal phase shift parameter is determined to formulate the double-layer collaborative optimization operation strategy of electric vehicles. The example verifies that the above model can improve the system load balancing degree and reduce the operation loss of medium voltage distribution network. [ABSTRACT FROM AUTHOR]

An increase in demand for renewable energy resources, energy storage technologies, and electric vehicles requires high-power level DC-DC converters. The DC-DC converter that is suitable for high-power conversion applications (i.e., resonant, full-bridge or the dual-active bridge) requires magnetic transformer coupling between input and output stage. However, transformer design in these converters remains a challenging problem, with several non-linear scaling issues that need to be simultaneously optimized to reduce losses and maintain acceptable performance. In this paper, a new transformer-less high step-up boost converter with a charge pump capacitorand capacitor-inductor-diode CLD cell is proposed using dynamic modeling. The experimental and simulation results of the proposed converter are carried out in a laboratory and through Matlab Simulink, where 10 V is given as an input voltage, and at the output, 100 V achieved in the proposed converter. A comparative analysis of the proposed converter has also been done with a conventional quadratic converter that has similar parameters. The results suggest that the proposed converter can obtain high voltage gain without operating at the maximum duty cycle and is more efficient than the conventional converter. [ABSTRACT FROM AUTHOR]