Research Progress on Data-Driven Methods for Battery States Estimation of Electric Buses
Battery states are very important for the safe and reliable use of new energy vehicles. The estimation of power battery states has become a research hotspot in the development of electric buses and transportation safety management. This paper summarizes the basic workflow of battery states estimatio...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | World Electric Vehicle Journal |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2032-6653/14/6/145 |
| _version_ | 1797592241882005504 |
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| author | Dengfeng Zhao Haiyang Li Fang Zhou Yudong Zhong Guosheng Zhang Zhaohui Liu Junjian Hou |
| author_facet | Dengfeng Zhao Haiyang Li Fang Zhou Yudong Zhong Guosheng Zhang Zhaohui Liu Junjian Hou |
| author_sort | Dengfeng Zhao |
| collection | DOAJ |
| description | Battery states are very important for the safe and reliable use of new energy vehicles. The estimation of power battery states has become a research hotspot in the development of electric buses and transportation safety management. This paper summarizes the basic workflow of battery states estimation tasks, compares, and analyzes the advantages and disadvantages of three types of data sources for battery states estimation, summarizes the characteristics and research progress of the three main models used for estimating power battery states such as machine learning models, deep learning models, and hybrid models, and prospects the development trend of estimation methods. It can be concluded that there are many data sources used for battery states estimation, and the onboard sensor data under natural driving conditions has the characteristics of objectivity and authenticity, making it the main data source for accurate power battery states estimation; Artificial neural network promotes the rapid development of deep learning methods, and deep learning models are increasingly applied in power battery states estimation, demonstrating advantages in accuracy and robustness; Hybrid models estimate the states of power batteries more accurately and reliably by comprehensively utilizing the characteristics of different types of models, which is an important development trend of battery states estimation methods. Higher accuracy, real-time performance, and robustness are the development goals of power battery states estimation methods. |
| first_indexed | 2024-03-11T01:48:40Z |
| format | Article |
| id | doaj.art-051cf7758624480083fc7cfe1c3f18c6 |
| institution | Directory Open Access Journal |
| issn | 2032-6653 |
| language | English |
| last_indexed | 2024-03-11T01:48:40Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj.art-051cf7758624480083fc7cfe1c3f18c62023-11-18T13:06:25ZengMDPI AGWorld Electric Vehicle Journal2032-66532023-06-0114614510.3390/wevj14060145Research Progress on Data-Driven Methods for Battery States Estimation of Electric BusesDengfeng Zhao0Haiyang Li1Fang Zhou2Yudong Zhong3Guosheng Zhang4Zhaohui Liu5Junjian Hou6Key Laboratory of Operation Safety Technology on Transport Vehicles, PRC, Research Institute of Highway, Ministry of Transport, Beijing 100088, ChinaCollege of Mechanical and Electrical Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, ChinaCollege of Mechanical and Electrical Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, ChinaCollege of Mechanical and Electrical Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, ChinaKey Laboratory of Operation Safety Technology on Transport Vehicles, PRC, Research Institute of Highway, Ministry of Transport, Beijing 100088, ChinaYutong Bus Co., Ltd., Zhengzhou 450004, ChinaCollege of Mechanical and Electrical Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, ChinaBattery states are very important for the safe and reliable use of new energy vehicles. The estimation of power battery states has become a research hotspot in the development of electric buses and transportation safety management. This paper summarizes the basic workflow of battery states estimation tasks, compares, and analyzes the advantages and disadvantages of three types of data sources for battery states estimation, summarizes the characteristics and research progress of the three main models used for estimating power battery states such as machine learning models, deep learning models, and hybrid models, and prospects the development trend of estimation methods. It can be concluded that there are many data sources used for battery states estimation, and the onboard sensor data under natural driving conditions has the characteristics of objectivity and authenticity, making it the main data source for accurate power battery states estimation; Artificial neural network promotes the rapid development of deep learning methods, and deep learning models are increasingly applied in power battery states estimation, demonstrating advantages in accuracy and robustness; Hybrid models estimate the states of power batteries more accurately and reliably by comprehensively utilizing the characteristics of different types of models, which is an important development trend of battery states estimation methods. Higher accuracy, real-time performance, and robustness are the development goals of power battery states estimation methods.https://www.mdpi.com/2032-6653/14/6/145battery states estimation methoddata sources for battery statemachine learning modeldeep learning modelhybrid modeldata-driven method |
| spellingShingle | Dengfeng Zhao Haiyang Li Fang Zhou Yudong Zhong Guosheng Zhang Zhaohui Liu Junjian Hou Research Progress on Data-Driven Methods for Battery States Estimation of Electric Buses World Electric Vehicle Journal battery states estimation method data sources for battery state machine learning model deep learning model hybrid model data-driven method |
| title | Research Progress on Data-Driven Methods for Battery States Estimation of Electric Buses |
| title_full | Research Progress on Data-Driven Methods for Battery States Estimation of Electric Buses |
| title_fullStr | Research Progress on Data-Driven Methods for Battery States Estimation of Electric Buses |
| title_full_unstemmed | Research Progress on Data-Driven Methods for Battery States Estimation of Electric Buses |
| title_short | Research Progress on Data-Driven Methods for Battery States Estimation of Electric Buses |
| title_sort | research progress on data driven methods for battery states estimation of electric buses |
| topic | battery states estimation method data sources for battery state machine learning model deep learning model hybrid model data-driven method |
| url | https://www.mdpi.com/2032-6653/14/6/145 |
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