Encoder-decoder model for multistep prediction of lithium-ion battery state of health (SOH)
This paper presents a Bidirectional Long Short-Term Memory (BiLSTM) model for the multistep prediction of lithium-ion battery State of Health (SOH). The proposed model employs a sequence-to-sequence approach, where the encoder receives the input sequence of battery data, and the decoder predicts the...
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| 主要作者: | |
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| 格式: | Final Year Project |
| 語言: | English |
| 出版: |
Nanyang Technological University
2023
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/167218 |
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| 總結: | This paper presents a Bidirectional Long Short-Term Memory (BiLSTM) model for the multistep prediction of lithium-ion battery State of Health (SOH). The proposed model employs a sequence-to-sequence approach, where the encoder receives the input sequence of battery data, and the decoder predicts the future states of health for multiple time steps.
Using experimental results to train and test the BiLSTM model, it can predict the SOH of lithium-ion batteries with high accuracy, making it a promising tool for predicting the remaining useful life (RUL) of batteries in real-world applications such as electric vehicles and renewable energy systems. |
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