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Optimization of DC/DC converters for embedded systems including dynamic constraints

Abstract : To keep up with the new emission standards and improve efficiency and autonomy, the transportation sector has moved towards more electric solutions. Then, compactness, weight and efficiency are primary requirements in modern aircrafts or Electric/Hybrid vehicles. The power electronics units in the on-board electrical networks have to be designed accordingly, as it is essential to achieve very compact designs and energy conversion efficiencies very close to the unity. The wide bandgap semiconductor materials are considered as the future technology in the realization of high efficiency switched-mode power supplies. Such materials are featured by fast switching behaviour and low power losses, which allows reducing the size of the passive elements and the cooling unit of the power converter. Considering these criteria, the objective of this study concerns the development of a procedure for the optimal design of a DC/DC boost converter 350V/3kW. The converter has been designed specifically for a powertrain application. In order to ensure the proper interaction with the load (DC/AC inverter and motor) and the micro-grid on-board, the design must take into account the stability and dynamic behaviour of the converter during a possible variation of the operating point, then its control strategy. To deal with the manifold technical solutions and in order to reach the best trade-off, a Pareto front genetic approach is proposed. The developed routine consent to obtain the most convenient design solutions in terms of efficiency and compactness, which ensure the stability and the compliance with the design specification on both steady state and transients modes.
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Submitted on : Thursday, December 10, 2020 - 9:23:19 AM
Last modification on : Wednesday, November 3, 2021 - 7:56:25 AM
Long-term archiving on: : Thursday, March 11, 2021 - 6:43:00 PM


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  • HAL Id : tel-03049887, version 1



Davide Dell'Isola. Optimization of DC/DC converters for embedded systems including dynamic constraints. Electric power. Université de Lorraine, 2020. English. ⟨NNT : 2020LORR0124⟩. ⟨tel-03049887⟩



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