Optimization of fuel consumption and NO%$_{{\varvec{x}}}%$ emission for mild HEV via hierarchical model predictive control

Abstract In this paper, we consider the fuel economy optimization problem for a mild hybrid electric vehicle (HEV) using hierarchical model predictive control. In the proposed algorithm, two problems are addressed: eco-driving and torque distribution. In the eco-driving problem, vehicle speed was co...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Umezawa, Yuka [verfasserIn] Yamauchi, Ken Seto, Hiroki Imamura, Toshiro Namerikawa, Toru

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Mild HEV Energy management Model predictive control Hierarchical control

Anmerkung:	© The Author(s), under exclusive licence to South China University of Technology and Academy of Mathematics and Systems Science, Chinese Academy of Sciences 2022

Übergeordnetes Werk:	Enthalten in: Journal of control theory and applications - Guangzhou, 2003, 20(2022), 2 vom: Mai, Seite 221-234
Übergeordnetes Werk:	volume:20 ; year:2022 ; number:2 ; month:05 ; pages:221-234

Links:	Volltext

DOI / URN:	10.1007/s11768-022-00097-9

Katalog-ID:	SPR047450266

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allfields	10.1007/s11768-022-00097-9 doi (DE-627)SPR047450266 (SPR)s11768-022-00097-9-e DE-627 ger DE-627 rakwb eng Umezawa, Yuka verfasserin aut Optimization of fuel consumption and NO%$_{{\varvec{x}}}%$ emission for mild HEV via hierarchical model predictive control 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to South China University of Technology and Academy of Mathematics and Systems Science, Chinese Academy of Sciences 2022 Abstract In this paper, we consider the fuel economy optimization problem for a mild hybrid electric vehicle (HEV) using hierarchical model predictive control. In the proposed algorithm, two problems are addressed: eco-driving and torque distribution. In the eco-driving problem, vehicle speed was controlled. Considering the reduction in fuel consumption and NO%$_x%$ emissions, the torque required to follow the target speed was calculated. Subsequently, in the torque distribution problem, the distribution between the engine and motor torques were calculated. In this phase, engine characteristics were considered. These problems differ in terms of time scales; therefore, a hierarchical model predictive control is proposed. Lastly, the numerical simulation results demonstrated the efficacy of this research. Mild HEV (dpeaa)DE-He213 Energy management (dpeaa)DE-He213 Model predictive control (dpeaa)DE-He213 Hierarchical control (dpeaa)DE-He213 Yamauchi, Ken aut Seto, Hiroki aut Imamura, Toshiro aut Namerikawa, Toru aut Enthalten in Journal of control theory and applications Guangzhou, 2003 20(2022), 2 vom: Mai, Seite 221-234 (DE-627)529093456 (DE-600)2299595-X 1993-0623 nnns volume:20 year:2022 number:2 month:05 pages:221-234 https://dx.doi.org/10.1007/s11768-022-00097-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_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_121 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2548 GBV_ILN_2700 GBV_ILN_2817 AR 20 2022 2 05 221-234
spelling	10.1007/s11768-022-00097-9 doi (DE-627)SPR047450266 (SPR)s11768-022-00097-9-e DE-627 ger DE-627 rakwb eng Umezawa, Yuka verfasserin aut Optimization of fuel consumption and NO%$_{{\varvec{x}}}%$ emission for mild HEV via hierarchical model predictive control 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to South China University of Technology and Academy of Mathematics and Systems Science, Chinese Academy of Sciences 2022 Abstract In this paper, we consider the fuel economy optimization problem for a mild hybrid electric vehicle (HEV) using hierarchical model predictive control. In the proposed algorithm, two problems are addressed: eco-driving and torque distribution. In the eco-driving problem, vehicle speed was controlled. Considering the reduction in fuel consumption and NO%$_x%$ emissions, the torque required to follow the target speed was calculated. Subsequently, in the torque distribution problem, the distribution between the engine and motor torques were calculated. In this phase, engine characteristics were considered. These problems differ in terms of time scales; therefore, a hierarchical model predictive control is proposed. Lastly, the numerical simulation results demonstrated the efficacy of this research. Mild HEV (dpeaa)DE-He213 Energy management (dpeaa)DE-He213 Model predictive control (dpeaa)DE-He213 Hierarchical control (dpeaa)DE-He213 Yamauchi, Ken aut Seto, Hiroki aut Imamura, Toshiro aut Namerikawa, Toru aut Enthalten in Journal of control theory and applications Guangzhou, 2003 20(2022), 2 vom: Mai, Seite 221-234 (DE-627)529093456 (DE-600)2299595-X 1993-0623 nnns volume:20 year:2022 number:2 month:05 pages:221-234 https://dx.doi.org/10.1007/s11768-022-00097-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_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_121 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2548 GBV_ILN_2700 GBV_ILN_2817 AR 20 2022 2 05 221-234
allfields_unstemmed	10.1007/s11768-022-00097-9 doi (DE-627)SPR047450266 (SPR)s11768-022-00097-9-e DE-627 ger DE-627 rakwb eng Umezawa, Yuka verfasserin aut Optimization of fuel consumption and NO%$_{{\varvec{x}}}%$ emission for mild HEV via hierarchical model predictive control 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to South China University of Technology and Academy of Mathematics and Systems Science, Chinese Academy of Sciences 2022 Abstract In this paper, we consider the fuel economy optimization problem for a mild hybrid electric vehicle (HEV) using hierarchical model predictive control. In the proposed algorithm, two problems are addressed: eco-driving and torque distribution. In the eco-driving problem, vehicle speed was controlled. Considering the reduction in fuel consumption and NO%$_x%$ emissions, the torque required to follow the target speed was calculated. Subsequently, in the torque distribution problem, the distribution between the engine and motor torques were calculated. In this phase, engine characteristics were considered. These problems differ in terms of time scales; therefore, a hierarchical model predictive control is proposed. Lastly, the numerical simulation results demonstrated the efficacy of this research. Mild HEV (dpeaa)DE-He213 Energy management (dpeaa)DE-He213 Model predictive control (dpeaa)DE-He213 Hierarchical control (dpeaa)DE-He213 Yamauchi, Ken aut Seto, Hiroki aut Imamura, Toshiro aut Namerikawa, Toru aut Enthalten in Journal of control theory and applications Guangzhou, 2003 20(2022), 2 vom: Mai, Seite 221-234 (DE-627)529093456 (DE-600)2299595-X 1993-0623 nnns volume:20 year:2022 number:2 month:05 pages:221-234 https://dx.doi.org/10.1007/s11768-022-00097-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_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_121 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2548 GBV_ILN_2700 GBV_ILN_2817 AR 20 2022 2 05 221-234
allfieldsGer	10.1007/s11768-022-00097-9 doi (DE-627)SPR047450266 (SPR)s11768-022-00097-9-e DE-627 ger DE-627 rakwb eng Umezawa, Yuka verfasserin aut Optimization of fuel consumption and NO%$_{{\varvec{x}}}%$ emission for mild HEV via hierarchical model predictive control 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to South China University of Technology and Academy of Mathematics and Systems Science, Chinese Academy of Sciences 2022 Abstract In this paper, we consider the fuel economy optimization problem for a mild hybrid electric vehicle (HEV) using hierarchical model predictive control. In the proposed algorithm, two problems are addressed: eco-driving and torque distribution. In the eco-driving problem, vehicle speed was controlled. Considering the reduction in fuel consumption and NO%$_x%$ emissions, the torque required to follow the target speed was calculated. Subsequently, in the torque distribution problem, the distribution between the engine and motor torques were calculated. In this phase, engine characteristics were considered. These problems differ in terms of time scales; therefore, a hierarchical model predictive control is proposed. Lastly, the numerical simulation results demonstrated the efficacy of this research. Mild HEV (dpeaa)DE-He213 Energy management (dpeaa)DE-He213 Model predictive control (dpeaa)DE-He213 Hierarchical control (dpeaa)DE-He213 Yamauchi, Ken aut Seto, Hiroki aut Imamura, Toshiro aut Namerikawa, Toru aut Enthalten in Journal of control theory and applications Guangzhou, 2003 20(2022), 2 vom: Mai, Seite 221-234 (DE-627)529093456 (DE-600)2299595-X 1993-0623 nnns volume:20 year:2022 number:2 month:05 pages:221-234 https://dx.doi.org/10.1007/s11768-022-00097-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_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_121 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2548 GBV_ILN_2700 GBV_ILN_2817 AR 20 2022 2 05 221-234
allfieldsSound	10.1007/s11768-022-00097-9 doi (DE-627)SPR047450266 (SPR)s11768-022-00097-9-e DE-627 ger DE-627 rakwb eng Umezawa, Yuka verfasserin aut Optimization of fuel consumption and NO%$_{{\varvec{x}}}%$ emission for mild HEV via hierarchical model predictive control 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to South China University of Technology and Academy of Mathematics and Systems Science, Chinese Academy of Sciences 2022 Abstract In this paper, we consider the fuel economy optimization problem for a mild hybrid electric vehicle (HEV) using hierarchical model predictive control. In the proposed algorithm, two problems are addressed: eco-driving and torque distribution. In the eco-driving problem, vehicle speed was controlled. Considering the reduction in fuel consumption and NO%$_x%$ emissions, the torque required to follow the target speed was calculated. Subsequently, in the torque distribution problem, the distribution between the engine and motor torques were calculated. In this phase, engine characteristics were considered. These problems differ in terms of time scales; therefore, a hierarchical model predictive control is proposed. Lastly, the numerical simulation results demonstrated the efficacy of this research. Mild HEV (dpeaa)DE-He213 Energy management (dpeaa)DE-He213 Model predictive control (dpeaa)DE-He213 Hierarchical control (dpeaa)DE-He213 Yamauchi, Ken aut Seto, Hiroki aut Imamura, Toshiro aut Namerikawa, Toru aut Enthalten in Journal of control theory and applications Guangzhou, 2003 20(2022), 2 vom: Mai, Seite 221-234 (DE-627)529093456 (DE-600)2299595-X 1993-0623 nnns volume:20 year:2022 number:2 month:05 pages:221-234 https://dx.doi.org/10.1007/s11768-022-00097-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_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_121 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2548 GBV_ILN_2700 GBV_ILN_2817 AR 20 2022 2 05 221-234
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author	Umezawa, Yuka
spellingShingle	Umezawa, Yuka misc Mild HEV misc Energy management misc Model predictive control misc Hierarchical control Optimization of fuel consumption and NO%$_{{\varvec{x}}}%$ emission for mild HEV via hierarchical model predictive control
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topic_title	Optimization of fuel consumption and NO%$_{{\varvec{x}}}%$ emission for mild HEV via hierarchical model predictive control Mild HEV (dpeaa)DE-He213 Energy management (dpeaa)DE-He213 Model predictive control (dpeaa)DE-He213 Hierarchical control (dpeaa)DE-He213
topic	misc Mild HEV misc Energy management misc Model predictive control misc Hierarchical control
topic_unstemmed	misc Mild HEV misc Energy management misc Model predictive control misc Hierarchical control
topic_browse	misc Mild HEV misc Energy management misc Model predictive control misc Hierarchical control
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title	Optimization of fuel consumption and NO%$_{{\varvec{x}}}%$ emission for mild HEV via hierarchical model predictive control
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title_full	Optimization of fuel consumption and NO%$_{{\varvec{x}}}%$ emission for mild HEV via hierarchical model predictive control
author_sort	Umezawa, Yuka
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author_browse	Umezawa, Yuka Yamauchi, Ken Seto, Hiroki Imamura, Toshiro Namerikawa, Toru
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doi_str_mv	10.1007/s11768-022-00097-9
title_sort	optimization of fuel consumption and no%$_{{\varvec{x}}}%$ emission for mild hev via hierarchical model predictive control
title_auth	Optimization of fuel consumption and NO%$_{{\varvec{x}}}%$ emission for mild HEV via hierarchical model predictive control
abstract	Abstract In this paper, we consider the fuel economy optimization problem for a mild hybrid electric vehicle (HEV) using hierarchical model predictive control. In the proposed algorithm, two problems are addressed: eco-driving and torque distribution. In the eco-driving problem, vehicle speed was controlled. Considering the reduction in fuel consumption and NO%$_x%$ emissions, the torque required to follow the target speed was calculated. Subsequently, in the torque distribution problem, the distribution between the engine and motor torques were calculated. In this phase, engine characteristics were considered. These problems differ in terms of time scales; therefore, a hierarchical model predictive control is proposed. Lastly, the numerical simulation results demonstrated the efficacy of this research. © The Author(s), under exclusive licence to South China University of Technology and Academy of Mathematics and Systems Science, Chinese Academy of Sciences 2022
abstractGer	Abstract In this paper, we consider the fuel economy optimization problem for a mild hybrid electric vehicle (HEV) using hierarchical model predictive control. In the proposed algorithm, two problems are addressed: eco-driving and torque distribution. In the eco-driving problem, vehicle speed was controlled. Considering the reduction in fuel consumption and NO%$_x%$ emissions, the torque required to follow the target speed was calculated. Subsequently, in the torque distribution problem, the distribution between the engine and motor torques were calculated. In this phase, engine characteristics were considered. These problems differ in terms of time scales; therefore, a hierarchical model predictive control is proposed. Lastly, the numerical simulation results demonstrated the efficacy of this research. © The Author(s), under exclusive licence to South China University of Technology and Academy of Mathematics and Systems Science, Chinese Academy of Sciences 2022
abstract_unstemmed	Abstract In this paper, we consider the fuel economy optimization problem for a mild hybrid electric vehicle (HEV) using hierarchical model predictive control. In the proposed algorithm, two problems are addressed: eco-driving and torque distribution. In the eco-driving problem, vehicle speed was controlled. Considering the reduction in fuel consumption and NO%$_x%$ emissions, the torque required to follow the target speed was calculated. Subsequently, in the torque distribution problem, the distribution between the engine and motor torques were calculated. In this phase, engine characteristics were considered. These problems differ in terms of time scales; therefore, a hierarchical model predictive control is proposed. Lastly, the numerical simulation results demonstrated the efficacy of this research. © The Author(s), under exclusive licence to South China University of Technology and Academy of Mathematics and Systems Science, Chinese Academy of Sciences 2022
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title_short	Optimization of fuel consumption and NO%$_{{\varvec{x}}}%$ emission for mild HEV via hierarchical model predictive control
url	https://dx.doi.org/10.1007/s11768-022-00097-9
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author2	Yamauchi, Ken Seto, Hiroki Imamura, Toshiro Namerikawa, Toru
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up_date	2024-07-04T03:09:53.621Z
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In the proposed algorithm, two problems are addressed: eco-driving and torque distribution. In the eco-driving problem, vehicle speed was controlled. Considering the reduction in fuel consumption and NO%$_x%$ emissions, the torque required to follow the target speed was calculated. Subsequently, in the torque distribution problem, the distribution between the engine and motor torques were calculated. In this phase, engine characteristics were considered. These problems differ in terms of time scales; therefore, a hierarchical model predictive control is proposed. 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Optimization of fuel consumption and NO%$_{{\varvec{x}}}%$ emission for mild HEV via hierarchical model predictive control

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