In the manufacturing process of lithium-ion batteries, capacity grading is a crucial step. Precisely anticipating the capacity of lithium-ion batteries exhibits significant importance in saving production time, reducing production costs, and conserving energy. Therefore, this paper proposes a lithium-ion battery capacity prediction method based on partial voltage curve and autoencoder. Using the partial voltage curve of the grading process, multiple dynamic features (DF) are extracted based on the dynamic changes of the lithium-ion battery voltage. A CLAE feature extraction model based on autoencoder (AE) is proposed, which is composed of convolutional neural network (CNN), long-short-term memory (LSTM) and AE. It can extract spatial features and temporal features of voltage curves. Finally, a support vector regression (SVR) capacity prediction model optimized by differential evolution algorithm (DE) is established. Compared with other capacity prediction methods, the DF-CLAE-DE-SVR prediction model outlined in this study exhibits superior prediction performance for battery capacity, especially for low-capacity batteries.
Lithium-Ion Battery Capacity Prediction Based on Partial Voltage Curve and Autoencoder
31.07.2024
1340449 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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