A Long Short-Term Memory-Based Deep Learning Digital Twin of a Li-Ion Cell for Battery SOC Estimation
This study aims to implement the digital twin of a Li-ion battery by using real measurement data and to create a deep learning-based SOC (state of charge) estimation solution. In the case of the SOC estimator, a special type of deep learning, so-called long short-term memory (LSTM), was used to incr...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | Engineering Proceedings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4591/79/1/16 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | This study aims to implement the digital twin of a Li-ion battery by using real measurement data and to create a deep learning-based SOC (state of charge) estimation solution. In the case of the SOC estimator, a special type of deep learning, so-called long short-term memory (LSTM), was used to increase the capabilities of the estimator. The digital twin and the SOC estimator were created by using MATLAB and MATLAB/Simulink. As a result, the implemented system can accurately simulate the non-linearities of the Li-ion battery and provide a satisfactory estimation of the SOC of the battery. |
|---|---|
| ISSN: | 2673-4591 |