Ensemble machine learning based driving range estimation for real‐world electric city buses by considering battery degradation levels
Abstract Battery electric buses (BEBs) have been regarded as effective options to address the congestion and pollution problems in the field of urban transportation. However, since the limited driving range of BEBs brings challenges for their promotion, the accurate estimation of the driving range w...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-06-01
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| Series: | IET Intelligent Transport Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/itr2.12064 |
| _version_ | 1828104834705784832 |
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| author | Yongxing Wang Chaoru Lu Jun Bi Qiuyue Sai Yongzhi Zhang |
| author_facet | Yongxing Wang Chaoru Lu Jun Bi Qiuyue Sai Yongzhi Zhang |
| author_sort | Yongxing Wang |
| collection | DOAJ |
| description | Abstract Battery electric buses (BEBs) have been regarded as effective options to address the congestion and pollution problems in the field of urban transportation. However, since the limited driving range of BEBs brings challenges for their promotion, the accurate estimation of the driving range with limited available information has become a critical issue for public transport operators. The real‐world data collected from 50 BEBs operated in two different cities is used to develop the driving range estimation method by considering the battery degradation effects. Firstly, the incremental capacity analysis method is introduced to characterize the battery performance, and the battery degradation levels under different charging modes are recognized. Afterward, four types of ensemble machine learning (EML) methods are adopted to model the driving range estimation. The BEB driving data, weather condition data and battery degradation levels are used to train and test the models with consideration of 17 impact factors together with two different charging modes. The results indicate that the ensemble machine learning methods have good performance overall, of which the random forest has the highest accuracy. Furthermore, the importance of influencing factors is analysed, and the relevant insights are discussed. |
| first_indexed | 2024-04-11T09:53:49Z |
| format | Article |
| id | doaj.art-2c68b75e16914d5d95a1e03662e9f5d1 |
| institution | Directory Open Access Journal |
| issn | 1751-956X 1751-9578 |
| language | English |
| last_indexed | 2024-04-11T09:53:49Z |
| publishDate | 2021-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Intelligent Transport Systems |
| spelling | doaj.art-2c68b75e16914d5d95a1e03662e9f5d12022-12-22T04:30:43ZengWileyIET Intelligent Transport Systems1751-956X1751-95782021-06-0115682483610.1049/itr2.12064Ensemble machine learning based driving range estimation for real‐world electric city buses by considering battery degradation levelsYongxing Wang0Chaoru Lu1Jun Bi2Qiuyue Sai3Yongzhi Zhang4Department of Civil and Environmental Engineering Norwegian University of Science and Technology Trondheim NorwayDepartment of Civil Engineering and Energy Technology Oslo Metropolitan University Oslo NorwaySchool of Traffic and Transportation Beijing Jiaotong University Beijing ChinaSchool of Traffic and Transportation Beijing Jiaotong University Beijing ChinaSchool of Automotive Engineering Chongqing University Chongqing ChinaAbstract Battery electric buses (BEBs) have been regarded as effective options to address the congestion and pollution problems in the field of urban transportation. However, since the limited driving range of BEBs brings challenges for their promotion, the accurate estimation of the driving range with limited available information has become a critical issue for public transport operators. The real‐world data collected from 50 BEBs operated in two different cities is used to develop the driving range estimation method by considering the battery degradation effects. Firstly, the incremental capacity analysis method is introduced to characterize the battery performance, and the battery degradation levels under different charging modes are recognized. Afterward, four types of ensemble machine learning (EML) methods are adopted to model the driving range estimation. The BEB driving data, weather condition data and battery degradation levels are used to train and test the models with consideration of 17 impact factors together with two different charging modes. The results indicate that the ensemble machine learning methods have good performance overall, of which the random forest has the highest accuracy. Furthermore, the importance of influencing factors is analysed, and the relevant insights are discussed.https://doi.org/10.1049/itr2.12064Secondary cellsPower engineering computingSecondary cellsTransportationMachine learning (artificial intelligence) |
| spellingShingle | Yongxing Wang Chaoru Lu Jun Bi Qiuyue Sai Yongzhi Zhang Ensemble machine learning based driving range estimation for real‐world electric city buses by considering battery degradation levels IET Intelligent Transport Systems Secondary cells Power engineering computing Secondary cells Transportation Machine learning (artificial intelligence) |
| title | Ensemble machine learning based driving range estimation for real‐world electric city buses by considering battery degradation levels |
| title_full | Ensemble machine learning based driving range estimation for real‐world electric city buses by considering battery degradation levels |
| title_fullStr | Ensemble machine learning based driving range estimation for real‐world electric city buses by considering battery degradation levels |
| title_full_unstemmed | Ensemble machine learning based driving range estimation for real‐world electric city buses by considering battery degradation levels |
| title_short | Ensemble machine learning based driving range estimation for real‐world electric city buses by considering battery degradation levels |
| title_sort | ensemble machine learning based driving range estimation for real world electric city buses by considering battery degradation levels |
| topic | Secondary cells Power engineering computing Secondary cells Transportation Machine learning (artificial intelligence) |
| url | https://doi.org/10.1049/itr2.12064 |
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