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...
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Autor*in:	Yongxing Wang [verfasserIn] Chaoru Lu [verfasserIn] Jun Bi [verfasserIn] Qiuyue Sai [verfasserIn] Yongzhi Zhang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Secondary cells Power engineering computing Transportation Machine learning (artificial intelligence)

Übergeordnetes Werk:	In: IET Intelligent Transport Systems - Wiley, 2021, 15(2021), 6, Seite 824-836
Übergeordnetes Werk:	volume:15 ; year:2021 ; number:6 ; pages:824-836

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.1049/itr2.12064

Katalog-ID:	DOAJ084389613

Internformat


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520			\|a 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.
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matchkey_str	article:17519578:2021----::nebeahnlannbsdrvnrnesiainorawrdlcrciyuebcni
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allfields	10.1049/itr2.12064 doi (DE-627)DOAJ084389613 (DE-599)DOAJ2c68b75e16914d5d95a1e03662e9f5d1 DE-627 ger DE-627 rakwb eng TA1001-1280 QA75.5-76.95 Yongxing Wang verfasserin aut Ensemble machine learning based driving range estimation for real‐world electric city buses by considering battery degradation levels 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Secondary cells Power engineering computing Transportation Machine learning (artificial intelligence) Transportation engineering Electronic computers. Computer science Chaoru Lu verfasserin aut Jun Bi verfasserin aut Qiuyue Sai verfasserin aut Yongzhi Zhang verfasserin aut In IET Intelligent Transport Systems Wiley, 2021 15(2021), 6, Seite 824-836 (DE-627)521693659 (DE-600)2264527-5 17519578 nnns volume:15 year:2021 number:6 pages:824-836 https://doi.org/10.1049/itr2.12064 kostenfrei https://doaj.org/article/2c68b75e16914d5d95a1e03662e9f5d1 kostenfrei https://doi.org/10.1049/itr2.12064 kostenfrei https://doaj.org/toc/1751-956X Journal toc kostenfrei https://doaj.org/toc/1751-9578 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 15 2021 6 824-836
spelling	10.1049/itr2.12064 doi (DE-627)DOAJ084389613 (DE-599)DOAJ2c68b75e16914d5d95a1e03662e9f5d1 DE-627 ger DE-627 rakwb eng TA1001-1280 QA75.5-76.95 Yongxing Wang verfasserin aut Ensemble machine learning based driving range estimation for real‐world electric city buses by considering battery degradation levels 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Secondary cells Power engineering computing Transportation Machine learning (artificial intelligence) Transportation engineering Electronic computers. Computer science Chaoru Lu verfasserin aut Jun Bi verfasserin aut Qiuyue Sai verfasserin aut Yongzhi Zhang verfasserin aut In IET Intelligent Transport Systems Wiley, 2021 15(2021), 6, Seite 824-836 (DE-627)521693659 (DE-600)2264527-5 17519578 nnns volume:15 year:2021 number:6 pages:824-836 https://doi.org/10.1049/itr2.12064 kostenfrei https://doaj.org/article/2c68b75e16914d5d95a1e03662e9f5d1 kostenfrei https://doi.org/10.1049/itr2.12064 kostenfrei https://doaj.org/toc/1751-956X Journal toc kostenfrei https://doaj.org/toc/1751-9578 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 15 2021 6 824-836
allfields_unstemmed	10.1049/itr2.12064 doi (DE-627)DOAJ084389613 (DE-599)DOAJ2c68b75e16914d5d95a1e03662e9f5d1 DE-627 ger DE-627 rakwb eng TA1001-1280 QA75.5-76.95 Yongxing Wang verfasserin aut Ensemble machine learning based driving range estimation for real‐world electric city buses by considering battery degradation levels 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Secondary cells Power engineering computing Transportation Machine learning (artificial intelligence) Transportation engineering Electronic computers. Computer science Chaoru Lu verfasserin aut Jun Bi verfasserin aut Qiuyue Sai verfasserin aut Yongzhi Zhang verfasserin aut In IET Intelligent Transport Systems Wiley, 2021 15(2021), 6, Seite 824-836 (DE-627)521693659 (DE-600)2264527-5 17519578 nnns volume:15 year:2021 number:6 pages:824-836 https://doi.org/10.1049/itr2.12064 kostenfrei https://doaj.org/article/2c68b75e16914d5d95a1e03662e9f5d1 kostenfrei https://doi.org/10.1049/itr2.12064 kostenfrei https://doaj.org/toc/1751-956X Journal toc kostenfrei https://doaj.org/toc/1751-9578 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 15 2021 6 824-836
allfieldsGer	10.1049/itr2.12064 doi (DE-627)DOAJ084389613 (DE-599)DOAJ2c68b75e16914d5d95a1e03662e9f5d1 DE-627 ger DE-627 rakwb eng TA1001-1280 QA75.5-76.95 Yongxing Wang verfasserin aut Ensemble machine learning based driving range estimation for real‐world electric city buses by considering battery degradation levels 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Secondary cells Power engineering computing Transportation Machine learning (artificial intelligence) Transportation engineering Electronic computers. Computer science Chaoru Lu verfasserin aut Jun Bi verfasserin aut Qiuyue Sai verfasserin aut Yongzhi Zhang verfasserin aut In IET Intelligent Transport Systems Wiley, 2021 15(2021), 6, Seite 824-836 (DE-627)521693659 (DE-600)2264527-5 17519578 nnns volume:15 year:2021 number:6 pages:824-836 https://doi.org/10.1049/itr2.12064 kostenfrei https://doaj.org/article/2c68b75e16914d5d95a1e03662e9f5d1 kostenfrei https://doi.org/10.1049/itr2.12064 kostenfrei https://doaj.org/toc/1751-956X Journal toc kostenfrei https://doaj.org/toc/1751-9578 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 15 2021 6 824-836
allfieldsSound	10.1049/itr2.12064 doi (DE-627)DOAJ084389613 (DE-599)DOAJ2c68b75e16914d5d95a1e03662e9f5d1 DE-627 ger DE-627 rakwb eng TA1001-1280 QA75.5-76.95 Yongxing Wang verfasserin aut Ensemble machine learning based driving range estimation for real‐world electric city buses by considering battery degradation levels 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Secondary cells Power engineering computing Transportation Machine learning (artificial intelligence) Transportation engineering Electronic computers. Computer science Chaoru Lu verfasserin aut Jun Bi verfasserin aut Qiuyue Sai verfasserin aut Yongzhi Zhang verfasserin aut In IET Intelligent Transport Systems Wiley, 2021 15(2021), 6, Seite 824-836 (DE-627)521693659 (DE-600)2264527-5 17519578 nnns volume:15 year:2021 number:6 pages:824-836 https://doi.org/10.1049/itr2.12064 kostenfrei https://doaj.org/article/2c68b75e16914d5d95a1e03662e9f5d1 kostenfrei https://doi.org/10.1049/itr2.12064 kostenfrei https://doaj.org/toc/1751-956X Journal toc kostenfrei https://doaj.org/toc/1751-9578 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 15 2021 6 824-836
language	English
source	In IET Intelligent Transport Systems 15(2021), 6, Seite 824-836 volume:15 year:2021 number:6 pages:824-836
sourceStr	In IET Intelligent Transport Systems 15(2021), 6, Seite 824-836 volume:15 year:2021 number:6 pages:824-836
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Secondary cells Power engineering computing Transportation Machine learning (artificial intelligence) Transportation engineering Electronic computers. Computer science
isfreeaccess_bool	true
container_title	IET Intelligent Transport Systems
authorswithroles_txt_mv	Yongxing Wang @@aut@@ Chaoru Lu @@aut@@ Jun Bi @@aut@@ Qiuyue Sai @@aut@@ Yongzhi Zhang @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	521693659
id	DOAJ084389613
language_de	englisch
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callnumber-first	T - Technology
author	Yongxing Wang
spellingShingle	Yongxing Wang misc TA1001-1280 misc QA75.5-76.95 misc Secondary cells misc Power engineering computing misc Transportation misc Machine learning (artificial intelligence) misc Transportation engineering misc Electronic computers. Computer science Ensemble machine learning based driving range estimation for real‐world electric city buses by considering battery degradation levels
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topic_title	TA1001-1280 QA75.5-76.95 Ensemble machine learning based driving range estimation for real‐world electric city buses by considering battery degradation levels Secondary cells Power engineering computing Transportation Machine learning (artificial intelligence)
topic	misc TA1001-1280 misc QA75.5-76.95 misc Secondary cells misc Power engineering computing misc Transportation misc Machine learning (artificial intelligence) misc Transportation engineering misc Electronic computers. Computer science
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title	Ensemble machine learning based driving range estimation for real‐world electric city buses by considering battery degradation levels
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title_full	Ensemble machine learning based driving range estimation for real‐world electric city buses by considering battery degradation levels
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author_browse	Yongxing Wang Chaoru Lu Jun Bi Qiuyue Sai Yongzhi Zhang
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title_sort	ensemble machine learning based driving range estimation for real‐world electric city buses by considering battery degradation levels
callnumber	TA1001-1280
title_auth	Ensemble machine learning based driving range estimation for real‐world electric city buses by considering battery degradation levels
abstract	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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	Ensemble machine learning based driving range estimation for real‐world electric city buses by considering battery degradation levels
url	https://doi.org/10.1049/itr2.12064 https://doaj.org/article/2c68b75e16914d5d95a1e03662e9f5d1 https://doaj.org/toc/1751-956X https://doaj.org/toc/1751-9578
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doi_str	10.1049/itr2.12064
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up_date	2024-07-03T22:44:41.671Z
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Ensemble machine learning based driving range estimation for real‐world electric city buses by considering battery degradation levels

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