A Moment-of-Inertia-Driven Engine Start-Up Method Based on Adaptive Model Predictive Control for Hybrid Electric Vehicles With Drivability Optimization

During the deceleration phase of a hybrid electric vehicle (HEV), a moment-of-inertia-driven (MoI-driven) engine start-up process can provide a potential economic benefit because it reduces the energy consumption of the starting device. During this start-up process, it is important to maintain driva...
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Autor*in:	Jianbo Wang [verfasserIn] Xianjun Hou [verfasserIn] Changqing Du [verfasserIn] Hongming Xu [verfasserIn] Quan Zhou [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Adaptive model predictive controller drivability engine start-up hybrid electrical vehicle moment of inertia

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 8(2020), Seite 133063-133075
Übergeordnetes Werk:	volume:8 ; year:2020 ; pages:133063-133075

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2020.3010528

Katalog-ID:	DOAJ047787805

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allfields	10.1109/ACCESS.2020.3010528 doi (DE-627)DOAJ047787805 (DE-599)DOAJa9f080a893714ffc82ac2de00e56949a DE-627 ger DE-627 rakwb eng TK1-9971 Jianbo Wang verfasserin aut A Moment-of-Inertia-Driven Engine Start-Up Method Based on Adaptive Model Predictive Control for Hybrid Electric Vehicles With Drivability Optimization 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier During the deceleration phase of a hybrid electric vehicle (HEV), a moment-of-inertia-driven (MoI-driven) engine start-up process can provide a potential economic benefit because it reduces the energy consumption of the starting device. During this start-up process, it is important to maintain drivability by enabling a quick start-up, low driveline vibration, and fast response to torque demand. The wheel rolling distance control should also be considered. This paper proposes electrical motor (EM) participation in an MoI-driven engine start-up process and studies an adaptive model-based predictive optimization method for the drivability control of P2 parallel hybrid vehicles. Based on a new triple mass-spring-system model, an adaptive model predictive controller (MPC) is designed with EM torque set points, clutch friction torque set points, and engine torque set points as manipulated inputs and engine speed, torsion speed, and wheel rolling distance as the measured outputs. A predicted torque demand is introduced to enhance the torque response performance. By considering the constraints of power source components, an optimization algorithm is developed. A simulation is conducted to verify the control strategy on the HEV powertrain and on vehicle dynamics models. The results show that under the same level of start-up, the torsion speed can be reduced by up to 50% with an improved wheel rolling distance and low torque demand error during a certain deceleration. Adaptive model predictive controller drivability engine start-up hybrid electrical vehicle moment of inertia Electrical engineering. Electronics. Nuclear engineering Xianjun Hou verfasserin aut Changqing Du verfasserin aut Hongming Xu verfasserin aut Quan Zhou verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 133063-133075 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:133063-133075 https://doi.org/10.1109/ACCESS.2020.3010528 kostenfrei https://doaj.org/article/a9f080a893714ffc82ac2de00e56949a kostenfrei https://ieeexplore.ieee.org/document/9144597/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 133063-133075
spelling	10.1109/ACCESS.2020.3010528 doi (DE-627)DOAJ047787805 (DE-599)DOAJa9f080a893714ffc82ac2de00e56949a DE-627 ger DE-627 rakwb eng TK1-9971 Jianbo Wang verfasserin aut A Moment-of-Inertia-Driven Engine Start-Up Method Based on Adaptive Model Predictive Control for Hybrid Electric Vehicles With Drivability Optimization 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier During the deceleration phase of a hybrid electric vehicle (HEV), a moment-of-inertia-driven (MoI-driven) engine start-up process can provide a potential economic benefit because it reduces the energy consumption of the starting device. During this start-up process, it is important to maintain drivability by enabling a quick start-up, low driveline vibration, and fast response to torque demand. The wheel rolling distance control should also be considered. This paper proposes electrical motor (EM) participation in an MoI-driven engine start-up process and studies an adaptive model-based predictive optimization method for the drivability control of P2 parallel hybrid vehicles. Based on a new triple mass-spring-system model, an adaptive model predictive controller (MPC) is designed with EM torque set points, clutch friction torque set points, and engine torque set points as manipulated inputs and engine speed, torsion speed, and wheel rolling distance as the measured outputs. A predicted torque demand is introduced to enhance the torque response performance. By considering the constraints of power source components, an optimization algorithm is developed. A simulation is conducted to verify the control strategy on the HEV powertrain and on vehicle dynamics models. The results show that under the same level of start-up, the torsion speed can be reduced by up to 50% with an improved wheel rolling distance and low torque demand error during a certain deceleration. Adaptive model predictive controller drivability engine start-up hybrid electrical vehicle moment of inertia Electrical engineering. Electronics. Nuclear engineering Xianjun Hou verfasserin aut Changqing Du verfasserin aut Hongming Xu verfasserin aut Quan Zhou verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 133063-133075 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:133063-133075 https://doi.org/10.1109/ACCESS.2020.3010528 kostenfrei https://doaj.org/article/a9f080a893714ffc82ac2de00e56949a kostenfrei https://ieeexplore.ieee.org/document/9144597/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 133063-133075
allfields_unstemmed	10.1109/ACCESS.2020.3010528 doi (DE-627)DOAJ047787805 (DE-599)DOAJa9f080a893714ffc82ac2de00e56949a DE-627 ger DE-627 rakwb eng TK1-9971 Jianbo Wang verfasserin aut A Moment-of-Inertia-Driven Engine Start-Up Method Based on Adaptive Model Predictive Control for Hybrid Electric Vehicles With Drivability Optimization 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier During the deceleration phase of a hybrid electric vehicle (HEV), a moment-of-inertia-driven (MoI-driven) engine start-up process can provide a potential economic benefit because it reduces the energy consumption of the starting device. During this start-up process, it is important to maintain drivability by enabling a quick start-up, low driveline vibration, and fast response to torque demand. The wheel rolling distance control should also be considered. This paper proposes electrical motor (EM) participation in an MoI-driven engine start-up process and studies an adaptive model-based predictive optimization method for the drivability control of P2 parallel hybrid vehicles. Based on a new triple mass-spring-system model, an adaptive model predictive controller (MPC) is designed with EM torque set points, clutch friction torque set points, and engine torque set points as manipulated inputs and engine speed, torsion speed, and wheel rolling distance as the measured outputs. A predicted torque demand is introduced to enhance the torque response performance. By considering the constraints of power source components, an optimization algorithm is developed. A simulation is conducted to verify the control strategy on the HEV powertrain and on vehicle dynamics models. The results show that under the same level of start-up, the torsion speed can be reduced by up to 50% with an improved wheel rolling distance and low torque demand error during a certain deceleration. Adaptive model predictive controller drivability engine start-up hybrid electrical vehicle moment of inertia Electrical engineering. Electronics. Nuclear engineering Xianjun Hou verfasserin aut Changqing Du verfasserin aut Hongming Xu verfasserin aut Quan Zhou verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 133063-133075 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:133063-133075 https://doi.org/10.1109/ACCESS.2020.3010528 kostenfrei https://doaj.org/article/a9f080a893714ffc82ac2de00e56949a kostenfrei https://ieeexplore.ieee.org/document/9144597/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 133063-133075
allfieldsGer	10.1109/ACCESS.2020.3010528 doi (DE-627)DOAJ047787805 (DE-599)DOAJa9f080a893714ffc82ac2de00e56949a DE-627 ger DE-627 rakwb eng TK1-9971 Jianbo Wang verfasserin aut A Moment-of-Inertia-Driven Engine Start-Up Method Based on Adaptive Model Predictive Control for Hybrid Electric Vehicles With Drivability Optimization 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier During the deceleration phase of a hybrid electric vehicle (HEV), a moment-of-inertia-driven (MoI-driven) engine start-up process can provide a potential economic benefit because it reduces the energy consumption of the starting device. During this start-up process, it is important to maintain drivability by enabling a quick start-up, low driveline vibration, and fast response to torque demand. The wheel rolling distance control should also be considered. This paper proposes electrical motor (EM) participation in an MoI-driven engine start-up process and studies an adaptive model-based predictive optimization method for the drivability control of P2 parallel hybrid vehicles. Based on a new triple mass-spring-system model, an adaptive model predictive controller (MPC) is designed with EM torque set points, clutch friction torque set points, and engine torque set points as manipulated inputs and engine speed, torsion speed, and wheel rolling distance as the measured outputs. A predicted torque demand is introduced to enhance the torque response performance. By considering the constraints of power source components, an optimization algorithm is developed. A simulation is conducted to verify the control strategy on the HEV powertrain and on vehicle dynamics models. The results show that under the same level of start-up, the torsion speed can be reduced by up to 50% with an improved wheel rolling distance and low torque demand error during a certain deceleration. Adaptive model predictive controller drivability engine start-up hybrid electrical vehicle moment of inertia Electrical engineering. Electronics. Nuclear engineering Xianjun Hou verfasserin aut Changqing Du verfasserin aut Hongming Xu verfasserin aut Quan Zhou verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 133063-133075 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:133063-133075 https://doi.org/10.1109/ACCESS.2020.3010528 kostenfrei https://doaj.org/article/a9f080a893714ffc82ac2de00e56949a kostenfrei https://ieeexplore.ieee.org/document/9144597/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 133063-133075
allfieldsSound	10.1109/ACCESS.2020.3010528 doi (DE-627)DOAJ047787805 (DE-599)DOAJa9f080a893714ffc82ac2de00e56949a DE-627 ger DE-627 rakwb eng TK1-9971 Jianbo Wang verfasserin aut A Moment-of-Inertia-Driven Engine Start-Up Method Based on Adaptive Model Predictive Control for Hybrid Electric Vehicles With Drivability Optimization 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier During the deceleration phase of a hybrid electric vehicle (HEV), a moment-of-inertia-driven (MoI-driven) engine start-up process can provide a potential economic benefit because it reduces the energy consumption of the starting device. During this start-up process, it is important to maintain drivability by enabling a quick start-up, low driveline vibration, and fast response to torque demand. The wheel rolling distance control should also be considered. This paper proposes electrical motor (EM) participation in an MoI-driven engine start-up process and studies an adaptive model-based predictive optimization method for the drivability control of P2 parallel hybrid vehicles. Based on a new triple mass-spring-system model, an adaptive model predictive controller (MPC) is designed with EM torque set points, clutch friction torque set points, and engine torque set points as manipulated inputs and engine speed, torsion speed, and wheel rolling distance as the measured outputs. A predicted torque demand is introduced to enhance the torque response performance. By considering the constraints of power source components, an optimization algorithm is developed. A simulation is conducted to verify the control strategy on the HEV powertrain and on vehicle dynamics models. The results show that under the same level of start-up, the torsion speed can be reduced by up to 50% with an improved wheel rolling distance and low torque demand error during a certain deceleration. Adaptive model predictive controller drivability engine start-up hybrid electrical vehicle moment of inertia Electrical engineering. Electronics. Nuclear engineering Xianjun Hou verfasserin aut Changqing Du verfasserin aut Hongming Xu verfasserin aut Quan Zhou verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 133063-133075 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:133063-133075 https://doi.org/10.1109/ACCESS.2020.3010528 kostenfrei https://doaj.org/article/a9f080a893714ffc82ac2de00e56949a kostenfrei https://ieeexplore.ieee.org/document/9144597/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 133063-133075
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authorswithroles_txt_mv	Jianbo Wang @@aut@@ Xianjun Hou @@aut@@ Changqing Du @@aut@@ Hongming Xu @@aut@@ Quan Zhou @@aut@@
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callnumber-first	T - Technology
author	Jianbo Wang
spellingShingle	Jianbo Wang misc TK1-9971 misc Adaptive model predictive controller misc drivability misc engine start-up misc hybrid electrical vehicle misc moment of inertia misc Electrical engineering. Electronics. Nuclear engineering A Moment-of-Inertia-Driven Engine Start-Up Method Based on Adaptive Model Predictive Control for Hybrid Electric Vehicles With Drivability Optimization
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topic_title	TK1-9971 A Moment-of-Inertia-Driven Engine Start-Up Method Based on Adaptive Model Predictive Control for Hybrid Electric Vehicles With Drivability Optimization Adaptive model predictive controller drivability engine start-up hybrid electrical vehicle moment of inertia
topic	misc TK1-9971 misc Adaptive model predictive controller misc drivability misc engine start-up misc hybrid electrical vehicle misc moment of inertia misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Adaptive model predictive controller misc drivability misc engine start-up misc hybrid electrical vehicle misc moment of inertia misc Electrical engineering. Electronics. Nuclear engineering
topic_browse	misc TK1-9971 misc Adaptive model predictive controller misc drivability misc engine start-up misc hybrid electrical vehicle misc moment of inertia misc Electrical engineering. Electronics. Nuclear engineering
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title	A Moment-of-Inertia-Driven Engine Start-Up Method Based on Adaptive Model Predictive Control for Hybrid Electric Vehicles With Drivability Optimization
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title_full	A Moment-of-Inertia-Driven Engine Start-Up Method Based on Adaptive Model Predictive Control for Hybrid Electric Vehicles With Drivability Optimization
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author_browse	Jianbo Wang Xianjun Hou Changqing Du Hongming Xu Quan Zhou
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title_sort	moment-of-inertia-driven engine start-up method based on adaptive model predictive control for hybrid electric vehicles with drivability optimization
callnumber	TK1-9971
title_auth	A Moment-of-Inertia-Driven Engine Start-Up Method Based on Adaptive Model Predictive Control for Hybrid Electric Vehicles With Drivability Optimization
abstract	During the deceleration phase of a hybrid electric vehicle (HEV), a moment-of-inertia-driven (MoI-driven) engine start-up process can provide a potential economic benefit because it reduces the energy consumption of the starting device. During this start-up process, it is important to maintain drivability by enabling a quick start-up, low driveline vibration, and fast response to torque demand. The wheel rolling distance control should also be considered. This paper proposes electrical motor (EM) participation in an MoI-driven engine start-up process and studies an adaptive model-based predictive optimization method for the drivability control of P2 parallel hybrid vehicles. Based on a new triple mass-spring-system model, an adaptive model predictive controller (MPC) is designed with EM torque set points, clutch friction torque set points, and engine torque set points as manipulated inputs and engine speed, torsion speed, and wheel rolling distance as the measured outputs. A predicted torque demand is introduced to enhance the torque response performance. By considering the constraints of power source components, an optimization algorithm is developed. A simulation is conducted to verify the control strategy on the HEV powertrain and on vehicle dynamics models. The results show that under the same level of start-up, the torsion speed can be reduced by up to 50% with an improved wheel rolling distance and low torque demand error during a certain deceleration.
abstractGer	During the deceleration phase of a hybrid electric vehicle (HEV), a moment-of-inertia-driven (MoI-driven) engine start-up process can provide a potential economic benefit because it reduces the energy consumption of the starting device. During this start-up process, it is important to maintain drivability by enabling a quick start-up, low driveline vibration, and fast response to torque demand. The wheel rolling distance control should also be considered. This paper proposes electrical motor (EM) participation in an MoI-driven engine start-up process and studies an adaptive model-based predictive optimization method for the drivability control of P2 parallel hybrid vehicles. Based on a new triple mass-spring-system model, an adaptive model predictive controller (MPC) is designed with EM torque set points, clutch friction torque set points, and engine torque set points as manipulated inputs and engine speed, torsion speed, and wheel rolling distance as the measured outputs. A predicted torque demand is introduced to enhance the torque response performance. By considering the constraints of power source components, an optimization algorithm is developed. A simulation is conducted to verify the control strategy on the HEV powertrain and on vehicle dynamics models. The results show that under the same level of start-up, the torsion speed can be reduced by up to 50% with an improved wheel rolling distance and low torque demand error during a certain deceleration.
abstract_unstemmed	During the deceleration phase of a hybrid electric vehicle (HEV), a moment-of-inertia-driven (MoI-driven) engine start-up process can provide a potential economic benefit because it reduces the energy consumption of the starting device. During this start-up process, it is important to maintain drivability by enabling a quick start-up, low driveline vibration, and fast response to torque demand. The wheel rolling distance control should also be considered. This paper proposes electrical motor (EM) participation in an MoI-driven engine start-up process and studies an adaptive model-based predictive optimization method for the drivability control of P2 parallel hybrid vehicles. Based on a new triple mass-spring-system model, an adaptive model predictive controller (MPC) is designed with EM torque set points, clutch friction torque set points, and engine torque set points as manipulated inputs and engine speed, torsion speed, and wheel rolling distance as the measured outputs. A predicted torque demand is introduced to enhance the torque response performance. By considering the constraints of power source components, an optimization algorithm is developed. A simulation is conducted to verify the control strategy on the HEV powertrain and on vehicle dynamics models. The results show that under the same level of start-up, the torsion speed can be reduced by up to 50% with an improved wheel rolling distance and low torque demand error during a certain deceleration.
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title_short	A Moment-of-Inertia-Driven Engine Start-Up Method Based on Adaptive Model Predictive Control for Hybrid Electric Vehicles With Drivability Optimization
url	https://doi.org/10.1109/ACCESS.2020.3010528 https://doaj.org/article/a9f080a893714ffc82ac2de00e56949a https://ieeexplore.ieee.org/document/9144597/ https://doaj.org/toc/2169-3536
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up_date	2024-07-03T14:08:19.680Z
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A simulation is conducted to verify the control strategy on the HEV powertrain and on vehicle dynamics models. The results show that under the same level of start-up, the torsion speed can be reduced by up to 50% with an improved wheel rolling distance and low torque demand error during a certain deceleration.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Adaptive model predictive controller</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">drivability</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">engine start-up</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">hybrid electrical vehicle</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">moment of inertia</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Electrical engineering. Electronics. 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A Moment-of-Inertia-Driven Engine Start-Up Method Based on Adaptive Model Predictive Control for Hybrid Electric Vehicles With Drivability Optimization

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