Rapid diagnosis of power battery faults in new energy vehicles based on improved boosting algorithm and big data
Abstract In recent years, the new energy vehicle industry has developed rapidly. A fast diagnostic method based on Boosting and big data is proposed to address the low accuracy and efficiency of fault diagnosis in new energy vehicle power batteries. Boosting is a machine learning technique that comb...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-12-01
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| Series: | Energy Informatics |
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| Online Access: | https://doi.org/10.1186/s42162-024-00439-8 |
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| author | Jiali Wang Jia Chen |
| author_facet | Jiali Wang Jia Chen |
| author_sort | Jiali Wang |
| collection | DOAJ |
| description | Abstract In recent years, the new energy vehicle industry has developed rapidly. A fast diagnostic method based on Boosting and big data is proposed to address the low accuracy and efficiency of fault diagnosis in new energy vehicle power batteries. Boosting is a machine learning technique that combines multiple weak learners into a strong learner. Big data refers to large-scale, complex datasets that exceed traditional data processing capabilities. Firstly, analyze and preprocess the big data uploaded by the battery. Subsequently, the importance of indicators in the data was analyzed using the Random Forest algorithm (RF). Finally, three improved Boosting algorithms were proposed, namely Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting Tree (XGBoost), and Gradient Boosting Decision Tree (CatBoost). The experimental results indicate that the LightGBM model effectively detects anomalies in battery big data. The accuracy values of XGBoost, CatBoost, and LightGBM are 97.84%, 98.57%, and 99.16%, respectively. The recall rates of XGBoost, CatBoost, and LightGBM models are all 1. The F1 values of GBoost, CatBoost, and LightGBM are 0.873, 0.983, and 0.985, respectively. The power battery is the core component of new energy vehicles, and its safety performance directly affects the operational safety of the vehicle. Timely identification and diagnosis of battery faults can effectively reduce potential accidents such as battery overheating and short circuits. Research can achieve real-time monitoring and timely reminders of potential faults. By early detection of issues such as battery overheating and voltage imbalance, this method can effectively reduce the risk of serious safety accidents and improve the overall operational reliability of new energy vehicles during driving. |
| format | Article |
| id | doaj-art-bf1c4b76f4d94999bd1c48a3a8ffbca1 |
| institution | Kabale University |
| issn | 2520-8942 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Energy Informatics |
| spelling | doaj-art-bf1c4b76f4d94999bd1c48a3a8ffbca12024-12-22T12:50:43ZengSpringerOpenEnergy Informatics2520-89422024-12-017112510.1186/s42162-024-00439-8Rapid diagnosis of power battery faults in new energy vehicles based on improved boosting algorithm and big dataJiali Wang0Jia Chen1School of Intelligent Manufacturing and Information Engineering, Shanghai Institute of Commerce & Foreign LanguagesZhejiang Lingxiao Energy Technology Co., Ltd, Lingxiao Energy Technology (Wuyi) Co., LtdAbstract In recent years, the new energy vehicle industry has developed rapidly. A fast diagnostic method based on Boosting and big data is proposed to address the low accuracy and efficiency of fault diagnosis in new energy vehicle power batteries. Boosting is a machine learning technique that combines multiple weak learners into a strong learner. Big data refers to large-scale, complex datasets that exceed traditional data processing capabilities. Firstly, analyze and preprocess the big data uploaded by the battery. Subsequently, the importance of indicators in the data was analyzed using the Random Forest algorithm (RF). Finally, three improved Boosting algorithms were proposed, namely Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting Tree (XGBoost), and Gradient Boosting Decision Tree (CatBoost). The experimental results indicate that the LightGBM model effectively detects anomalies in battery big data. The accuracy values of XGBoost, CatBoost, and LightGBM are 97.84%, 98.57%, and 99.16%, respectively. The recall rates of XGBoost, CatBoost, and LightGBM models are all 1. The F1 values of GBoost, CatBoost, and LightGBM are 0.873, 0.983, and 0.985, respectively. The power battery is the core component of new energy vehicles, and its safety performance directly affects the operational safety of the vehicle. Timely identification and diagnosis of battery faults can effectively reduce potential accidents such as battery overheating and short circuits. Research can achieve real-time monitoring and timely reminders of potential faults. By early detection of issues such as battery overheating and voltage imbalance, this method can effectively reduce the risk of serious safety accidents and improve the overall operational reliability of new energy vehicles during driving.https://doi.org/10.1186/s42162-024-00439-8Boosting algorithmRFNew energy vehiclesPower batteryFault diagnosis |
| spellingShingle | Jiali Wang Jia Chen Rapid diagnosis of power battery faults in new energy vehicles based on improved boosting algorithm and big data Energy Informatics Boosting algorithm RF New energy vehicles Power battery Fault diagnosis |
| title | Rapid diagnosis of power battery faults in new energy vehicles based on improved boosting algorithm and big data |
| title_full | Rapid diagnosis of power battery faults in new energy vehicles based on improved boosting algorithm and big data |
| title_fullStr | Rapid diagnosis of power battery faults in new energy vehicles based on improved boosting algorithm and big data |
| title_full_unstemmed | Rapid diagnosis of power battery faults in new energy vehicles based on improved boosting algorithm and big data |
| title_short | Rapid diagnosis of power battery faults in new energy vehicles based on improved boosting algorithm and big data |
| title_sort | rapid diagnosis of power battery faults in new energy vehicles based on improved boosting algorithm and big data |
| topic | Boosting algorithm RF New energy vehicles Power battery Fault diagnosis |
| url | https://doi.org/10.1186/s42162-024-00439-8 |
| work_keys_str_mv | AT jialiwang rapiddiagnosisofpowerbatteryfaultsinnewenergyvehiclesbasedonimprovedboostingalgorithmandbigdata AT jiachen rapiddiagnosisofpowerbatteryfaultsinnewenergyvehiclesbasedonimprovedboostingalgorithmandbigdata |