Development of a power management strategy to minimize the fuel consumption of a heavy-duty series hybrid electric vehicle

Abstract This paper presents a power management strategy to minimize the fuel consumption of a heavy-duty series hybrid electric vehicle. The vehicle model, which consists of a CNG engine, a generator, two driving motors and a battery, is developed and validated with experimental data. Dynamic progr...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Yun, Seungjae [verfasserIn] Lee, Kangwon Yi, Kyongsu

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2015

Schlagwörter:	Series hybrid electric vehicle Dynamic programming Rule-based control algorithm

Anmerkung:	© The Korean Society of Mechanical Engineers and Springer-Verlag Berlin Heidelberg 2015

Übergeordnetes Werk:	Enthalten in: Journal of mechanical science and technology - Berlin : Springer, 2005, 29(2015), 10 vom: Okt., Seite 4399-4406
Übergeordnetes Werk:	volume:29 ; year:2015 ; number:10 ; month:10 ; pages:4399-4406

Links:	Volltext

DOI / URN:	10.1007/s12206-015-0938-8

Katalog-ID:	SPR025315609

Internformat


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520			\|a Abstract This paper presents a power management strategy to minimize the fuel consumption of a heavy-duty series hybrid electric vehicle. The vehicle model, which consists of a CNG engine, a generator, two driving motors and a battery, is developed and validated with experimental data. Dynamic programming (DP) is applied to design a control algorithm optimizing fuel consumption of the series hybrid electric vehicle. By imitating the behavior of the DP control signal, a rule-based control algorithm is proposed. The controller actuates the CNG engine, generator, and battery to minimize fuel consumption and maintain the battery state of charge within a proper region. Simulation studies and experiments are conducted for typical driving modes to investigate the performance of the proposed power management strategy. Results indicate that fuel economy can be enhanced by approximately 30% using the proposed power management strategy to the series hybrid electric vehicle over the conventional vehicle.
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912			\|a GBV_ILN_213
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912			\|a GBV_ILN_281
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_636
912			\|a GBV_ILN_702
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912			\|a GBV_ILN_2003
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912			\|a GBV_ILN_2005
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912			\|a GBV_ILN_2007
912			\|a GBV_ILN_2008
912			\|a GBV_ILN_2009
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912			\|a GBV_ILN_2026
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912			\|a GBV_ILN_2037
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912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
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Indexfelder

author_variant	s y sy k l kl k y ky
matchkey_str	article:19763824:2015----::eeomnoaoemngmnsrtgtmnmzteulosmtooaevdts
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publishDate	2015
allfields	10.1007/s12206-015-0938-8 doi (DE-627)SPR025315609 (SPR)s12206-015-0938-8-e DE-627 ger DE-627 rakwb eng Yun, Seungjae verfasserin aut Development of a power management strategy to minimize the fuel consumption of a heavy-duty series hybrid electric vehicle 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Society of Mechanical Engineers and Springer-Verlag Berlin Heidelberg 2015 Abstract This paper presents a power management strategy to minimize the fuel consumption of a heavy-duty series hybrid electric vehicle. The vehicle model, which consists of a CNG engine, a generator, two driving motors and a battery, is developed and validated with experimental data. Dynamic programming (DP) is applied to design a control algorithm optimizing fuel consumption of the series hybrid electric vehicle. By imitating the behavior of the DP control signal, a rule-based control algorithm is proposed. The controller actuates the CNG engine, generator, and battery to minimize fuel consumption and maintain the battery state of charge within a proper region. Simulation studies and experiments are conducted for typical driving modes to investigate the performance of the proposed power management strategy. Results indicate that fuel economy can be enhanced by approximately 30% using the proposed power management strategy to the series hybrid electric vehicle over the conventional vehicle. Series hybrid electric vehicle (dpeaa)DE-He213 Dynamic programming (dpeaa)DE-He213 Rule-based control algorithm (dpeaa)DE-He213 Lee, Kangwon aut Yi, Kyongsu aut Enthalten in Journal of mechanical science and technology Berlin : Springer, 2005 29(2015), 10 vom: Okt., Seite 4399-4406 (DE-627)58714016X (DE-600)2467571-4 1976-3824 nnns volume:29 year:2015 number:10 month:10 pages:4399-4406 https://dx.doi.org/10.1007/s12206-015-0938-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 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_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 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_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 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_4246 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_4393 GBV_ILN_4700 AR 29 2015 10 10 4399-4406
spelling	10.1007/s12206-015-0938-8 doi (DE-627)SPR025315609 (SPR)s12206-015-0938-8-e DE-627 ger DE-627 rakwb eng Yun, Seungjae verfasserin aut Development of a power management strategy to minimize the fuel consumption of a heavy-duty series hybrid electric vehicle 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Society of Mechanical Engineers and Springer-Verlag Berlin Heidelberg 2015 Abstract This paper presents a power management strategy to minimize the fuel consumption of a heavy-duty series hybrid electric vehicle. The vehicle model, which consists of a CNG engine, a generator, two driving motors and a battery, is developed and validated with experimental data. Dynamic programming (DP) is applied to design a control algorithm optimizing fuel consumption of the series hybrid electric vehicle. By imitating the behavior of the DP control signal, a rule-based control algorithm is proposed. The controller actuates the CNG engine, generator, and battery to minimize fuel consumption and maintain the battery state of charge within a proper region. Simulation studies and experiments are conducted for typical driving modes to investigate the performance of the proposed power management strategy. Results indicate that fuel economy can be enhanced by approximately 30% using the proposed power management strategy to the series hybrid electric vehicle over the conventional vehicle. Series hybrid electric vehicle (dpeaa)DE-He213 Dynamic programming (dpeaa)DE-He213 Rule-based control algorithm (dpeaa)DE-He213 Lee, Kangwon aut Yi, Kyongsu aut Enthalten in Journal of mechanical science and technology Berlin : Springer, 2005 29(2015), 10 vom: Okt., Seite 4399-4406 (DE-627)58714016X (DE-600)2467571-4 1976-3824 nnns volume:29 year:2015 number:10 month:10 pages:4399-4406 https://dx.doi.org/10.1007/s12206-015-0938-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 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_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 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_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 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_4246 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_4393 GBV_ILN_4700 AR 29 2015 10 10 4399-4406
allfields_unstemmed	10.1007/s12206-015-0938-8 doi (DE-627)SPR025315609 (SPR)s12206-015-0938-8-e DE-627 ger DE-627 rakwb eng Yun, Seungjae verfasserin aut Development of a power management strategy to minimize the fuel consumption of a heavy-duty series hybrid electric vehicle 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Society of Mechanical Engineers and Springer-Verlag Berlin Heidelberg 2015 Abstract This paper presents a power management strategy to minimize the fuel consumption of a heavy-duty series hybrid electric vehicle. The vehicle model, which consists of a CNG engine, a generator, two driving motors and a battery, is developed and validated with experimental data. Dynamic programming (DP) is applied to design a control algorithm optimizing fuel consumption of the series hybrid electric vehicle. By imitating the behavior of the DP control signal, a rule-based control algorithm is proposed. The controller actuates the CNG engine, generator, and battery to minimize fuel consumption and maintain the battery state of charge within a proper region. Simulation studies and experiments are conducted for typical driving modes to investigate the performance of the proposed power management strategy. Results indicate that fuel economy can be enhanced by approximately 30% using the proposed power management strategy to the series hybrid electric vehicle over the conventional vehicle. Series hybrid electric vehicle (dpeaa)DE-He213 Dynamic programming (dpeaa)DE-He213 Rule-based control algorithm (dpeaa)DE-He213 Lee, Kangwon aut Yi, Kyongsu aut Enthalten in Journal of mechanical science and technology Berlin : Springer, 2005 29(2015), 10 vom: Okt., Seite 4399-4406 (DE-627)58714016X (DE-600)2467571-4 1976-3824 nnns volume:29 year:2015 number:10 month:10 pages:4399-4406 https://dx.doi.org/10.1007/s12206-015-0938-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 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_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 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_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 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_4246 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_4393 GBV_ILN_4700 AR 29 2015 10 10 4399-4406
allfieldsGer	10.1007/s12206-015-0938-8 doi (DE-627)SPR025315609 (SPR)s12206-015-0938-8-e DE-627 ger DE-627 rakwb eng Yun, Seungjae verfasserin aut Development of a power management strategy to minimize the fuel consumption of a heavy-duty series hybrid electric vehicle 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Society of Mechanical Engineers and Springer-Verlag Berlin Heidelberg 2015 Abstract This paper presents a power management strategy to minimize the fuel consumption of a heavy-duty series hybrid electric vehicle. The vehicle model, which consists of a CNG engine, a generator, two driving motors and a battery, is developed and validated with experimental data. Dynamic programming (DP) is applied to design a control algorithm optimizing fuel consumption of the series hybrid electric vehicle. By imitating the behavior of the DP control signal, a rule-based control algorithm is proposed. The controller actuates the CNG engine, generator, and battery to minimize fuel consumption and maintain the battery state of charge within a proper region. Simulation studies and experiments are conducted for typical driving modes to investigate the performance of the proposed power management strategy. Results indicate that fuel economy can be enhanced by approximately 30% using the proposed power management strategy to the series hybrid electric vehicle over the conventional vehicle. Series hybrid electric vehicle (dpeaa)DE-He213 Dynamic programming (dpeaa)DE-He213 Rule-based control algorithm (dpeaa)DE-He213 Lee, Kangwon aut Yi, Kyongsu aut Enthalten in Journal of mechanical science and technology Berlin : Springer, 2005 29(2015), 10 vom: Okt., Seite 4399-4406 (DE-627)58714016X (DE-600)2467571-4 1976-3824 nnns volume:29 year:2015 number:10 month:10 pages:4399-4406 https://dx.doi.org/10.1007/s12206-015-0938-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 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_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 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_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 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_4246 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_4393 GBV_ILN_4700 AR 29 2015 10 10 4399-4406
allfieldsSound	10.1007/s12206-015-0938-8 doi (DE-627)SPR025315609 (SPR)s12206-015-0938-8-e DE-627 ger DE-627 rakwb eng Yun, Seungjae verfasserin aut Development of a power management strategy to minimize the fuel consumption of a heavy-duty series hybrid electric vehicle 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Society of Mechanical Engineers and Springer-Verlag Berlin Heidelberg 2015 Abstract This paper presents a power management strategy to minimize the fuel consumption of a heavy-duty series hybrid electric vehicle. The vehicle model, which consists of a CNG engine, a generator, two driving motors and a battery, is developed and validated with experimental data. Dynamic programming (DP) is applied to design a control algorithm optimizing fuel consumption of the series hybrid electric vehicle. By imitating the behavior of the DP control signal, a rule-based control algorithm is proposed. The controller actuates the CNG engine, generator, and battery to minimize fuel consumption and maintain the battery state of charge within a proper region. Simulation studies and experiments are conducted for typical driving modes to investigate the performance of the proposed power management strategy. Results indicate that fuel economy can be enhanced by approximately 30% using the proposed power management strategy to the series hybrid electric vehicle over the conventional vehicle. Series hybrid electric vehicle (dpeaa)DE-He213 Dynamic programming (dpeaa)DE-He213 Rule-based control algorithm (dpeaa)DE-He213 Lee, Kangwon aut Yi, Kyongsu aut Enthalten in Journal of mechanical science and technology Berlin : Springer, 2005 29(2015), 10 vom: Okt., Seite 4399-4406 (DE-627)58714016X (DE-600)2467571-4 1976-3824 nnns volume:29 year:2015 number:10 month:10 pages:4399-4406 https://dx.doi.org/10.1007/s12206-015-0938-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 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_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 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_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 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_4246 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_4393 GBV_ILN_4700 AR 29 2015 10 10 4399-4406
language	English
source	Enthalten in Journal of mechanical science and technology 29(2015), 10 vom: Okt., Seite 4399-4406 volume:29 year:2015 number:10 month:10 pages:4399-4406
sourceStr	Enthalten in Journal of mechanical science and technology 29(2015), 10 vom: Okt., Seite 4399-4406 volume:29 year:2015 number:10 month:10 pages:4399-4406
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Series hybrid electric vehicle Dynamic programming Rule-based control algorithm
isfreeaccess_bool	false
container_title	Journal of mechanical science and technology
authorswithroles_txt_mv	Yun, Seungjae @@aut@@ Lee, Kangwon @@aut@@ Yi, Kyongsu @@aut@@
publishDateDaySort_date	2015-10-01T00:00:00Z
hierarchy_top_id	58714016X
id	SPR025315609
language_de	englisch
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author	Yun, Seungjae
spellingShingle	Yun, Seungjae misc Series hybrid electric vehicle misc Dynamic programming misc Rule-based control algorithm Development of a power management strategy to minimize the fuel consumption of a heavy-duty series hybrid electric vehicle
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topic_title	Development of a power management strategy to minimize the fuel consumption of a heavy-duty series hybrid electric vehicle Series hybrid electric vehicle (dpeaa)DE-He213 Dynamic programming (dpeaa)DE-He213 Rule-based control algorithm (dpeaa)DE-He213
topic	misc Series hybrid electric vehicle misc Dynamic programming misc Rule-based control algorithm
topic_unstemmed	misc Series hybrid electric vehicle misc Dynamic programming misc Rule-based control algorithm
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title	Development of a power management strategy to minimize the fuel consumption of a heavy-duty series hybrid electric vehicle
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title_full	Development of a power management strategy to minimize the fuel consumption of a heavy-duty series hybrid electric vehicle
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author_browse	Yun, Seungjae Lee, Kangwon Yi, Kyongsu
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doi_str_mv	10.1007/s12206-015-0938-8
title_sort	development of a power management strategy to minimize the fuel consumption of a heavy-duty series hybrid electric vehicle
title_auth	Development of a power management strategy to minimize the fuel consumption of a heavy-duty series hybrid electric vehicle
abstract	Abstract This paper presents a power management strategy to minimize the fuel consumption of a heavy-duty series hybrid electric vehicle. The vehicle model, which consists of a CNG engine, a generator, two driving motors and a battery, is developed and validated with experimental data. Dynamic programming (DP) is applied to design a control algorithm optimizing fuel consumption of the series hybrid electric vehicle. By imitating the behavior of the DP control signal, a rule-based control algorithm is proposed. The controller actuates the CNG engine, generator, and battery to minimize fuel consumption and maintain the battery state of charge within a proper region. Simulation studies and experiments are conducted for typical driving modes to investigate the performance of the proposed power management strategy. Results indicate that fuel economy can be enhanced by approximately 30% using the proposed power management strategy to the series hybrid electric vehicle over the conventional vehicle. © The Korean Society of Mechanical Engineers and Springer-Verlag Berlin Heidelberg 2015
abstractGer	Abstract This paper presents a power management strategy to minimize the fuel consumption of a heavy-duty series hybrid electric vehicle. The vehicle model, which consists of a CNG engine, a generator, two driving motors and a battery, is developed and validated with experimental data. Dynamic programming (DP) is applied to design a control algorithm optimizing fuel consumption of the series hybrid electric vehicle. By imitating the behavior of the DP control signal, a rule-based control algorithm is proposed. The controller actuates the CNG engine, generator, and battery to minimize fuel consumption and maintain the battery state of charge within a proper region. Simulation studies and experiments are conducted for typical driving modes to investigate the performance of the proposed power management strategy. Results indicate that fuel economy can be enhanced by approximately 30% using the proposed power management strategy to the series hybrid electric vehicle over the conventional vehicle. © The Korean Society of Mechanical Engineers and Springer-Verlag Berlin Heidelberg 2015
abstract_unstemmed	Abstract This paper presents a power management strategy to minimize the fuel consumption of a heavy-duty series hybrid electric vehicle. The vehicle model, which consists of a CNG engine, a generator, two driving motors and a battery, is developed and validated with experimental data. Dynamic programming (DP) is applied to design a control algorithm optimizing fuel consumption of the series hybrid electric vehicle. By imitating the behavior of the DP control signal, a rule-based control algorithm is proposed. The controller actuates the CNG engine, generator, and battery to minimize fuel consumption and maintain the battery state of charge within a proper region. Simulation studies and experiments are conducted for typical driving modes to investigate the performance of the proposed power management strategy. Results indicate that fuel economy can be enhanced by approximately 30% using the proposed power management strategy to the series hybrid electric vehicle over the conventional vehicle. © The Korean Society of Mechanical Engineers and Springer-Verlag Berlin Heidelberg 2015
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title_short	Development of a power management strategy to minimize the fuel consumption of a heavy-duty series hybrid electric vehicle
url	https://dx.doi.org/10.1007/s12206-015-0938-8
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up_date	2024-07-03T15:14:05.039Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR025315609</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230403065431.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2015 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s12206-015-0938-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR025315609</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s12206-015-0938-8-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Yun, Seungjae</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Development of a power management strategy to minimize the fuel consumption of a heavy-duty series hybrid electric vehicle</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2015</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Korean Society of Mechanical Engineers and Springer-Verlag Berlin Heidelberg 2015</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract This paper presents a power management strategy to minimize the fuel consumption of a heavy-duty series hybrid electric vehicle. The vehicle model, which consists of a CNG engine, a generator, two driving motors and a battery, is developed and validated with experimental data. Dynamic programming (DP) is applied to design a control algorithm optimizing fuel consumption of the series hybrid electric vehicle. By imitating the behavior of the DP control signal, a rule-based control algorithm is proposed. The controller actuates the CNG engine, generator, and battery to minimize fuel consumption and maintain the battery state of charge within a proper region. Simulation studies and experiments are conducted for typical driving modes to investigate the performance of the proposed power management strategy. 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in</subfield><subfield code="t">Journal of mechanical science and technology</subfield><subfield code="d">Berlin : Springer, 2005</subfield><subfield code="g">29(2015), 10 vom: Okt., Seite 4399-4406</subfield><subfield code="w">(DE-627)58714016X</subfield><subfield code="w">(DE-600)2467571-4</subfield><subfield code="x">1976-3824</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:29</subfield><subfield code="g">year:2015</subfield><subfield code="g">number:10</subfield><subfield code="g">month:10</subfield><subfield code="g">pages:4399-4406</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s12206-015-0938-8</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield 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Development of a power management strategy to minimize the fuel consumption of a heavy-duty series hybrid electric vehicle

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