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

• Published in: Energy and AI 2024-05, Vol.16, p.100355, Article 100355
• Main Authors: Vila, Daniel, Hornberger, Elisabeth, Toigo, Christina
• Format: Article
• Language: English
• Subjects: Battery; Electrolyzer; Energy storage; Machine learning; Sequence-to-sequence model; State of charge; ZZE
• ISSN: 2666-5468; 2666-5468
• DOI: 10.1016/j.egyai.2024.100355

•A novel technique for hybrid energy storage and hydrogen production is introduced.•Various machine learning models are studied for effectiveness in SOC prediction.•SOC prediction is crucial for future management software of ZZE systems.•LSTM neural network showed best performance for SOC-prediction with 3.21 % MAE. 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.