Czasopismo | PRZEGLĄD ELEKTROTECHNICZNY | Rocznik 2023 - zeszyt 8

Power Management for a Fuel cell/Battery and Supercapacitor based on Artificial Neural Networks for Electric Vehicles

Zarządzanie energią dla ogniwa paliwowego/akumulatora i superkondensatora w oparciu o sztuczne sieci neuronowe dla pojazdów elektrycznych)

10.15199/48.2023.08.29

nr katalogowy: 144436
10.15199/48.2023.08.29

Streszczenie

Hydrogen electric vehicles are environmentally friendly and highly efficient. They derive their energy from fuel cell as a main component in addition to lithium-ion battery and supercapacitor as auxiliary elements. However, there are problems in securing the required power and the optimal power control strategy with different operating conditions. In order to solve these problems, we seek in our work to improve energy economy and continuity, make use of some of the energy that is often lost as heat, and increase system life. To with considering various operating restrictions. So, we adopted this hybrid energy storage system. A specialized strategy is designed for optimal control of energy sources. Therefore, an artificial neural network was trained using Matlab/Simulink software. The obtained results showed the effectiveness and accuracy of the proposed system. Which can be used in practice.

Abstract

Pojazdy napędzane wodorem są przyjazne dla środowiska i bardzo wydajne. Energię czerpią z ogniwa paliwowego jako głównego elementu, oprócz akumulatora litowo-jonowego i superkondensatora jako elementów pomocniczych. Istnieją jednak problemy z zapewnieniem wymaganej mocy i optymalną strategią sterowania mocą przy różnych warunkach pracy. Aby rozwiązać te problemy, w naszej pracy staramy się poprawić ekonomię i ciągłość energii, wykorzystać część energii, która często jest tracona w postaci ciepła, oraz wydłużyć żywotność systemu. Aby wziąć pod uwagę różne ograniczenia operacyjne. Dlatego przyjęliśmy ten hybrydowy system magazynowania energii. Specjalistyczna strategia ma na celu optymalne sterowanie źródłami energii. Dlatego sztuczna sieć neuronowa została przeszkolona przy użyciu oprogramowania Matlab/Simulink. Uzyskane wyniki wykazały skuteczność i dokładność proponowanego systemu. Które można wykorzystać w praktyce.

Słowa kluczowe

Keywords

Bibliografia

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