Lithium Battery Life Prediction for Electric Vehicles Using Enhanced TCN and SVN Quantile Regression
The need for lithium battery life forecast algorithms has increased due to the global acceptance of electric vehicles. To further enhance the forecast’s precision and resilience about lithium batteries’ remaining life, this study implements quantile regression with in support v...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10836734/ |
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| author | Xinyue Li Jiangwei Chu |
| author_facet | Xinyue Li Jiangwei Chu |
| author_sort | Xinyue Li |
| collection | DOAJ |
| description | The need for lithium battery life forecast algorithms has increased due to the global acceptance of electric vehicles. To further enhance the forecast’s precision and resilience about lithium batteries’ remaining life, this study implements quantile regression with in support vector networks to evaluate battery health conditions. Furthermore, it proceeds to integrate self-coding neural networks with temporal convolutional networks for the purpose of processing and extracting battery life data, and finally proposes a novel prediction model. The outcomes of the experiment demonstrate that when the width parameter is 0.75 and the penalty coefficient is 1, the battery health prediction accuracy of this new model is up to 88%, the remaining life prediction accuracy is up to 95.41%, and the number of battery capacity degradation times is up to 340, which is up to 45 times more than the number of the same type of model. In addition, the minimum difference in temperature prediction of battery charging under this model is close to 0.2°C, the minimum difference in temperature prediction during discharge is 0.3°C, and the battery capacity fidelity test findings’ average value is 91.08%. It is evident that the study’s suggested model offers a considerable advantage in estimating lithium battery lifespan for electric vehicles. Additionally, the study’s findings provide a quicker, more precise, and more flexible reference for estimating the lithium batteries’ condition and remaining life. |
| format | Article |
| id | doaj-art-ce7e344ff44c4777afa47b8df5fd53bb |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-ce7e344ff44c4777afa47b8df5fd53bb2025-01-25T00:01:49ZengIEEEIEEE Access2169-35362025-01-0113125811259510.1109/ACCESS.2025.352831410836734Lithium Battery Life Prediction for Electric Vehicles Using Enhanced TCN and SVN Quantile RegressionXinyue Li0https://orcid.org/0000-0001-6709-648XJiangwei Chu1https://orcid.org/0000-0003-4781-5023College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, ChinaCollege of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, ChinaThe need for lithium battery life forecast algorithms has increased due to the global acceptance of electric vehicles. To further enhance the forecast’s precision and resilience about lithium batteries’ remaining life, this study implements quantile regression with in support vector networks to evaluate battery health conditions. Furthermore, it proceeds to integrate self-coding neural networks with temporal convolutional networks for the purpose of processing and extracting battery life data, and finally proposes a novel prediction model. The outcomes of the experiment demonstrate that when the width parameter is 0.75 and the penalty coefficient is 1, the battery health prediction accuracy of this new model is up to 88%, the remaining life prediction accuracy is up to 95.41%, and the number of battery capacity degradation times is up to 340, which is up to 45 times more than the number of the same type of model. In addition, the minimum difference in temperature prediction of battery charging under this model is close to 0.2°C, the minimum difference in temperature prediction during discharge is 0.3°C, and the battery capacity fidelity test findings’ average value is 91.08%. It is evident that the study’s suggested model offers a considerable advantage in estimating lithium battery lifespan for electric vehicles. Additionally, the study’s findings provide a quicker, more precise, and more flexible reference for estimating the lithium batteries’ condition and remaining life.https://ieeexplore.ieee.org/document/10836734/Lithium batteryremaining lifesupport vector networktime convolution networkquantile regressionstate of health |
| spellingShingle | Xinyue Li Jiangwei Chu Lithium Battery Life Prediction for Electric Vehicles Using Enhanced TCN and SVN Quantile Regression IEEE Access Lithium battery remaining life support vector network time convolution network quantile regression state of health |
| title | Lithium Battery Life Prediction for Electric Vehicles Using Enhanced TCN and SVN Quantile Regression |
| title_full | Lithium Battery Life Prediction for Electric Vehicles Using Enhanced TCN and SVN Quantile Regression |
| title_fullStr | Lithium Battery Life Prediction for Electric Vehicles Using Enhanced TCN and SVN Quantile Regression |
| title_full_unstemmed | Lithium Battery Life Prediction for Electric Vehicles Using Enhanced TCN and SVN Quantile Regression |
| title_short | Lithium Battery Life Prediction for Electric Vehicles Using Enhanced TCN and SVN Quantile Regression |
| title_sort | lithium battery life prediction for electric vehicles using enhanced tcn and svn quantile regression |
| topic | Lithium battery remaining life support vector network time convolution network quantile regression state of health |
| url | https://ieeexplore.ieee.org/document/10836734/ |
| work_keys_str_mv | AT xinyueli lithiumbatterylifepredictionforelectricvehiclesusingenhancedtcnandsvnquantileregression AT jiangweichu lithiumbatterylifepredictionforelectricvehiclesusingenhancedtcnandsvnquantileregression |