Application of multi-modal temporal neural network based on enhanced sparrow optimization in lithium battery life prediction
Abstract 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 Improv...
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Nature Portfolio
2024-11-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-78211-x |
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| author | Zeyu Liu Xiaofang Du Yuhai Shi |
| author_facet | Zeyu Liu Xiaofang Du Yuhai Shi |
| author_sort | Zeyu Liu |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-a4edfeb456f44337ad2c7c6f3f5934e7 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-a4edfeb456f44337ad2c7c6f3f5934e72024-11-17T12:24:02ZengNature PortfolioScientific Reports2045-23222024-11-0114111710.1038/s41598-024-78211-xApplication of multi-modal temporal neural network based on enhanced sparrow optimization in lithium battery life predictionZeyu Liu0Xiaofang Du1Yuhai Shi2School of Automotive Engineering, Wuhan University of TechnologySchool of Automotive Engineering, Wuhan University of TechnologyXiang Yang DA’AN Automobile Testing Center LimitedAbstract 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.https://doi.org/10.1038/s41598-024-78211-xBattery life degradationHealth feature extractionTemporal neural networkDenoising autoencoderSparrow optimization algorithm |
| spellingShingle | Zeyu Liu Xiaofang Du Yuhai Shi Application of multi-modal temporal neural network based on enhanced sparrow optimization in lithium battery life prediction Scientific Reports Battery life degradation Health feature extraction Temporal neural network Denoising autoencoder Sparrow optimization algorithm |
| title | Application of multi-modal temporal neural network based on enhanced sparrow optimization in lithium battery life prediction |
| title_full | Application of multi-modal temporal neural network based on enhanced sparrow optimization in lithium battery life prediction |
| title_fullStr | Application of multi-modal temporal neural network based on enhanced sparrow optimization in lithium battery life prediction |
| title_full_unstemmed | Application of multi-modal temporal neural network based on enhanced sparrow optimization in lithium battery life prediction |
| title_short | Application of multi-modal temporal neural network based on enhanced sparrow optimization in lithium battery life prediction |
| title_sort | application of multi modal temporal neural network based on enhanced sparrow optimization in lithium battery life prediction |
| topic | Battery life degradation Health feature extraction Temporal neural network Denoising autoencoder Sparrow optimization algorithm |
| url | https://doi.org/10.1038/s41598-024-78211-x |
| work_keys_str_mv | AT zeyuliu applicationofmultimodaltemporalneuralnetworkbasedonenhancedsparrowoptimizationinlithiumbatterylifeprediction AT xiaofangdu applicationofmultimodaltemporalneuralnetworkbasedonenhancedsparrowoptimizationinlithiumbatterylifeprediction AT yuhaishi applicationofmultimodaltemporalneuralnetworkbasedonenhancedsparrowoptimizationinlithiumbatterylifeprediction |