Machine learning based state-of-charge prediction of electrochemical green hydrogen production:Zink-Zwischenschritt-Elektrolyseur(ZZE)  

作　　者：Daniel Vila Elisabeth Hornberger Christina Toigo 

机构地区：[1]STOFF2 GmbH–Gebäude H/Flughafen Tegel 1,13405 Berlin,Germany [2]University of Applied Sciences Upper Austria,Stelzhamerstraße 23,A-4600 Wels,Austria [3]Centre for Economic Research on Inclusivity and Sustainability at the University of Galway,University Road,Galway,Ireland

出　　处：《Energy and AI》2024年第2期269-276,共8页能源与人工智能（英文）

基　　金：supported by the German Federal Ministry of Education(grant number 01LY2111A);the German Federal Ministry of Economics and Climate Action(grant number 03EI3092A).

摘　　要：The intermittency of renewable energy is a key limiting factor for the successful decarbonization of both energy producing and consuming sectors. Green hydrogen has the potential to act as the central energy vector connecting hard-to-abate sectors to renewable power. However, combining energy storage and conversion for a holistic electrolyzer system remains challenging. Here, we show the innovative Zink-Zwischenschritt Elektrolyseur (ZZE), or Zinc Intermediate step Electrolyzer in English, that temporarily decouples the water splitting reaction and uses zinc to store electrical energy in chemical form. To perform optimal operation of a ZZE system, machine learning models were applied to predict the state of charge of a lab scale ZZE system. Using various models, we were able to determine the effectiveness of the prediction and contrast it to state of charge predictions of other energy storage systems. We show that a bi-directional long short-term memory neural network approach has the lowest error within the testing environment. This work serves to perform further ZZE development as well as state of charge prediction for other novel energy storage technologies.

关 键 词：ELECTROLYZER Battery ZZE Energy storage State of charge Machine learning Sequence-to-sequence model 

分 类 号：TK91[动力工程及工程热物理]