AI Article Synopsis
- Accurately predicting the charging state of lithium-ion batteries can prevent overcharging and overdischarging, ultimately extending battery life.
- A new prediction method combines convolutional layers, multi-head attention, and gated cycle units to capture data features across different spatial and temporal dimensions.
- Tests conducted using the University of Maryland dataset showed this model achieved low mean absolute error (0.53%), root mean square error (0.67%), and maximum prediction error (0.4%), outperforming other predictive models.
Article Abstract
If the charging state of the lithium-ion battery can be accurately predicted, overcharge and overdischarge of the battery can be avoided, and the service life of the battery can be improved. In order to improve the prediction accuracy of SOC, a prediction method combined with convolutional layer, multi-head attention mechanism and gated cycle unit is proposed to extract data feature information from different dimensions of space and time. Using the data set of the University of Maryland, we simulated the battery in real vehicle operating conditions at different temperatures (0 °C, 25 °C, 45 °C). The test results showed that the mean absolute error, root mean square error and maximum prediction error of the model were 0.53%, 0.67% and 0.4% respectively. The results show that the model can predict SOC accurately. At the same time, the comparison with other prediction models shows that the prediction accuracy of this model is the highest.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10545700 | PMC |
| http://dx.doi.org/10.1038/s41598-023-43858-5 | DOI Listing |
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