Abstract
This paper presents a Transformer-based State of Health(SoH) estimation algorithm that is applied to publicly available NASA data. Data preprocessing involves the application of a moving average to reduce noise and utilize wavelet transformers to extract features. The preprocessed data serves as input to the Transformer model in estimating SoH. Next, a comparative analysis with an LSTM-based model is conducted by using the Root Mean Square Error(RMSE) metric. The proposed model demonstrates a superior SoH estimation accuracy, surpassing the LSTM-based model by up to 39.58 %.
| Translated title of the contribution | Novel SoH Estimation of Electric Vehicle Battery Using Transformer Model |
|---|---|
| Original language | Korean |
| Pages (from-to) | 471-476 |
| Number of pages | 6 |
| Journal | Transactions of the Korean Society of Automotive Engineers |
| Volume | 32 |
| Issue number | 5 |
| DOIs | |
| State | Published - May 2024 |
Keywords
- Electric vehicle
- Lithium-ion battery
- Long short-term memory
- State of health estimation
- Transformer
