Application of multi-modal temporal neural network based on enhanced sparrow optimization in lithium battery life prediction

This paper introduces the DeNet-Mamba-DC-SCSSA network, an advanced solution for predicting the Remaining Useful Life (RUL) of lithium-ion batteries, crucial for the safety and efficiency management of electric vehicles. Combining the robust Denoising Enhancement Network (DeNet), the Improved Sparro...

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Vydáno v:Scientific reports Ročník 14; číslo 1; s. 27476 - 17
Hlavní autoři: Liu, Zeyu, Du, Xiaofang, Shi, Yuhai
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 11.11.2024
Nature Publishing Group
Nature Portfolio
Témata:
639/4077
639/4077/4079/891
Algorithms
Battery life degradation
Denoising autoencoder
Electric vehicles
Health feature extraction
Humanities and Social Sciences
Lithium
multidisciplinary
Neural networks
Science
Science (multidisciplinary)
Sparrow optimization algorithm
Temporal neural network
Sparrow optimization algorithm
Temporal neural network
Denoising autoencoder
Health feature extraction
Battery life degradation
ISSN:2045-2322, 2045-2322
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Shrnutí:This paper introduces the DeNet-Mamba-DC-SCSSA network, an advanced solution for predicting the Remaining Useful Life (RUL) of lithium-ion batteries, crucial for the safety and efficiency management of electric vehicles. Combining the robust Denoising Enhancement Network (DeNet), the Improved Sparrow Optimization Algorithm (SCSSA), the adept Mamba time-series model, and the proficient Dilated Convolution (DC), this model excels in precise noise handling and sophisticated feature extraction. DeNet diligently refines input data, mitigating noise interference, while Mamba skillfully captures sequential intricacies. DC, on the other hand, adeptly extracts features over varying time scales, ensuring meticulous RUL predictions.The model’s efficacy was rigorously tested on NASA and CALCE datasets and was benchmarked against cutting-edge algorithms. Remarkably, it reduced average RE and RMSE by 48.59% and 21.45%, respectively, showcasing its superior performance and accuracy. Further evaluation on the CALCE dataset against the latest methods affirmed its leading predictive precision and stability.The model’s robustness and practical applicability were further validated using real vehicle data from a new energy vehicle platform. In a challenging test, it accurately predicted the charging capacities corresponding to the mileage of four vehicles with minimal errors: 0.52 Ah, 1.03 Ah, 0.84 Ah, and 0.71 Ah. These results significantly surpassed those of other recent methods, highlighting the model’s exceptional generalizability and potential for real-world applications in electric vehicle battery management.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-78211-x