University of Twente Student Theses
Applying a Machine Learning Model to Estimate the Current State of Charge of Energy Storage Devices
Salce, Miss Yasmin (2023) Applying a Machine Learning Model to Estimate the Current State of Charge of Energy Storage Devices.
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| Abstract: | This research examines the role of applying machine learning to estimate the state of charge (SoC) of energy storage devices. SoC is a vital parameter that can be used to reflect the performance of the energy storage device and is key in the management of these energy storage systems, such as batteries or a hot water buffer. It is used to optimize the performance and to extend the lifetime of storage systems. A simple artificial neural network (ANN) and a time-series long short term memory (LSTM) neural network is investigated to create a model that can be used on multiple forms of energy storage devices. The simple ANN achieved results of over 80% accuracy for the Yuasa and Multipower batteries but did poorly at 25% accuracy for the Conrad battery. The LSTM achieved an accuracy of over 90% for 4 batteries and a hot water buffer. The LSTM model is then altered so that it is able to estimate the SoC in real time for the Conrad battery, this achieved a lower accuracy of 70.1%. |
| Item Type: | Essay (Master) |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 54 computer science |
| Programme: | Embedded Systems MSc (60331) |
| Link to this item: | https://purl.utwente.nl/essays/94482 |
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