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- W4362709318 abstract "Traditional research on the residual life of lithium batteries mainly uses algorithms such as support vector machine (SVM) and deep learning long short-term memory (LSTM) to build models. The above models all have the problem of low prediction precision. In order to improve the prediction precision of the residual life of lithium batteries, this paper uses the NASA public data set as the data sample. It uses the XGBoost algorithm and the LightGBM algorithm to model on the python platform and imports the data set into the model for prediction experiments. To increase the precision of the prediction, the model parameters are optimized, and the ensemble learning method is used to predict the lifetime of the lithium battery. Comparing the prediction precision of the two models with the previously commonly used LSTM model, both XGBoost and LightGBM models have obtained higher prediction precision, and LightGBM has better prediction accuracy." @default.
- W4362709318 created "2023-04-09" @default.
- W4362709318 creator A5066531406 @default.
- W4362709318 date "2022-10-21" @default.
- W4362709318 modified "2023-09-25" @default.
- W4362709318 title "Prediction Method of Remaining Service Life of Li-ion Batteries Based on XGBoost and LightGBM" @default.
- W4362709318 cites W2201263372 @default.
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- W4362709318 doi "https://doi.org/10.1109/ahpcai57455.2022.10087857" @default.
- W4362709318 hasPublicationYear "2022" @default.
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