Optimizing Automatic Transmission Double-Transition Shift Process Based on Multi-Objective Genetic Algorithm

In order to improve fuel economy, the number of gears in the hydraulic automatic transmission of heavy-duty mining trucks is continuously increasing. Compared with single-transition shifts, double-transition shifts can optimize the structure of multi-speed transmissions, but the difficulty of contro...
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Autor*in:	Heng Zhang [verfasserIn] Xinxin Zhao [verfasserIn] Jue Yang [verfasserIn] Wenming Zhang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	automatic transmission double-transition shift multi-objective optimization shift jerk clutch energy loss

Übergeordnetes Werk:	In: Applied Sciences - MDPI AG, 2012, 10(2020), 21, p 7794
Übergeordnetes Werk:	volume:10 ; year:2020 ; number:21, p 7794

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/app10217794

Katalog-ID:	DOAJ013627449

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520			\|a In order to improve fuel economy, the number of gears in the hydraulic automatic transmission of heavy-duty mining trucks is continuously increasing. Compared with single-transition shifts, double-transition shifts can optimize the structure of multi-speed transmissions, but the difficulty of control will also increase. In this paper, a dynamic model of a 6 + 2 speed automatic transmission and vehicle powertrain system are built based on the Lagrange method, and the dynamic analysis of the two sets of clutches that make up the double-transition shift are carried out. Since a simulation model of the double-transition shift process is built-in MATLAB/Simulink, the shift jerk and clutch energy loss are used as multi-objective, and the genetic algorithm is used to optimize the simulation. Five strategies for the overlapping time of the clutches are proposed, and simulation experiments and Pareto optimal analysis are carried out, respectively. The simulation results show that the non-overlapping of the two sets of clutch inertia phases in the double-transition shift can effectively reduce the shift jerk. The overlapping of the torque phase and the inertia phase of the other clutch set can control the clutch energy loss at a low level due to using less shift time.
650		4	\|a automatic transmission
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650		4	\|a clutch energy loss
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allfields	10.3390/app10217794 doi (DE-627)DOAJ013627449 (DE-599)DOAJ49921c2d822148389930785d6e1789ef DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Heng Zhang verfasserin aut Optimizing Automatic Transmission Double-Transition Shift Process Based on Multi-Objective Genetic Algorithm 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In order to improve fuel economy, the number of gears in the hydraulic automatic transmission of heavy-duty mining trucks is continuously increasing. Compared with single-transition shifts, double-transition shifts can optimize the structure of multi-speed transmissions, but the difficulty of control will also increase. In this paper, a dynamic model of a 6 + 2 speed automatic transmission and vehicle powertrain system are built based on the Lagrange method, and the dynamic analysis of the two sets of clutches that make up the double-transition shift are carried out. Since a simulation model of the double-transition shift process is built-in MATLAB/Simulink, the shift jerk and clutch energy loss are used as multi-objective, and the genetic algorithm is used to optimize the simulation. Five strategies for the overlapping time of the clutches are proposed, and simulation experiments and Pareto optimal analysis are carried out, respectively. The simulation results show that the non-overlapping of the two sets of clutch inertia phases in the double-transition shift can effectively reduce the shift jerk. The overlapping of the torque phase and the inertia phase of the other clutch set can control the clutch energy loss at a low level due to using less shift time. automatic transmission double-transition shift multi-objective optimization shift jerk clutch energy loss Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Xinxin Zhao verfasserin aut Jue Yang verfasserin aut Wenming Zhang verfasserin aut In Applied Sciences MDPI AG, 2012 10(2020), 21, p 7794 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:10 year:2020 number:21, p 7794 https://doi.org/10.3390/app10217794 kostenfrei https://doaj.org/article/49921c2d822148389930785d6e1789ef kostenfrei https://www.mdpi.com/2076-3417/10/21/7794 kostenfrei https://doaj.org/toc/2076-3417 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_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_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 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 10 2020 21, p 7794
spelling	10.3390/app10217794 doi (DE-627)DOAJ013627449 (DE-599)DOAJ49921c2d822148389930785d6e1789ef DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Heng Zhang verfasserin aut Optimizing Automatic Transmission Double-Transition Shift Process Based on Multi-Objective Genetic Algorithm 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In order to improve fuel economy, the number of gears in the hydraulic automatic transmission of heavy-duty mining trucks is continuously increasing. Compared with single-transition shifts, double-transition shifts can optimize the structure of multi-speed transmissions, but the difficulty of control will also increase. In this paper, a dynamic model of a 6 + 2 speed automatic transmission and vehicle powertrain system are built based on the Lagrange method, and the dynamic analysis of the two sets of clutches that make up the double-transition shift are carried out. Since a simulation model of the double-transition shift process is built-in MATLAB/Simulink, the shift jerk and clutch energy loss are used as multi-objective, and the genetic algorithm is used to optimize the simulation. Five strategies for the overlapping time of the clutches are proposed, and simulation experiments and Pareto optimal analysis are carried out, respectively. The simulation results show that the non-overlapping of the two sets of clutch inertia phases in the double-transition shift can effectively reduce the shift jerk. The overlapping of the torque phase and the inertia phase of the other clutch set can control the clutch energy loss at a low level due to using less shift time. automatic transmission double-transition shift multi-objective optimization shift jerk clutch energy loss Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Xinxin Zhao verfasserin aut Jue Yang verfasserin aut Wenming Zhang verfasserin aut In Applied Sciences MDPI AG, 2012 10(2020), 21, p 7794 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:10 year:2020 number:21, p 7794 https://doi.org/10.3390/app10217794 kostenfrei https://doaj.org/article/49921c2d822148389930785d6e1789ef kostenfrei https://www.mdpi.com/2076-3417/10/21/7794 kostenfrei https://doaj.org/toc/2076-3417 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_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_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 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 10 2020 21, p 7794
allfields_unstemmed	10.3390/app10217794 doi (DE-627)DOAJ013627449 (DE-599)DOAJ49921c2d822148389930785d6e1789ef DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Heng Zhang verfasserin aut Optimizing Automatic Transmission Double-Transition Shift Process Based on Multi-Objective Genetic Algorithm 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In order to improve fuel economy, the number of gears in the hydraulic automatic transmission of heavy-duty mining trucks is continuously increasing. Compared with single-transition shifts, double-transition shifts can optimize the structure of multi-speed transmissions, but the difficulty of control will also increase. In this paper, a dynamic model of a 6 + 2 speed automatic transmission and vehicle powertrain system are built based on the Lagrange method, and the dynamic analysis of the two sets of clutches that make up the double-transition shift are carried out. Since a simulation model of the double-transition shift process is built-in MATLAB/Simulink, the shift jerk and clutch energy loss are used as multi-objective, and the genetic algorithm is used to optimize the simulation. Five strategies for the overlapping time of the clutches are proposed, and simulation experiments and Pareto optimal analysis are carried out, respectively. The simulation results show that the non-overlapping of the two sets of clutch inertia phases in the double-transition shift can effectively reduce the shift jerk. The overlapping of the torque phase and the inertia phase of the other clutch set can control the clutch energy loss at a low level due to using less shift time. automatic transmission double-transition shift multi-objective optimization shift jerk clutch energy loss Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Xinxin Zhao verfasserin aut Jue Yang verfasserin aut Wenming Zhang verfasserin aut In Applied Sciences MDPI AG, 2012 10(2020), 21, p 7794 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:10 year:2020 number:21, p 7794 https://doi.org/10.3390/app10217794 kostenfrei https://doaj.org/article/49921c2d822148389930785d6e1789ef kostenfrei https://www.mdpi.com/2076-3417/10/21/7794 kostenfrei https://doaj.org/toc/2076-3417 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_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_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 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 10 2020 21, p 7794
allfieldsGer	10.3390/app10217794 doi (DE-627)DOAJ013627449 (DE-599)DOAJ49921c2d822148389930785d6e1789ef DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Heng Zhang verfasserin aut Optimizing Automatic Transmission Double-Transition Shift Process Based on Multi-Objective Genetic Algorithm 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In order to improve fuel economy, the number of gears in the hydraulic automatic transmission of heavy-duty mining trucks is continuously increasing. Compared with single-transition shifts, double-transition shifts can optimize the structure of multi-speed transmissions, but the difficulty of control will also increase. In this paper, a dynamic model of a 6 + 2 speed automatic transmission and vehicle powertrain system are built based on the Lagrange method, and the dynamic analysis of the two sets of clutches that make up the double-transition shift are carried out. Since a simulation model of the double-transition shift process is built-in MATLAB/Simulink, the shift jerk and clutch energy loss are used as multi-objective, and the genetic algorithm is used to optimize the simulation. Five strategies for the overlapping time of the clutches are proposed, and simulation experiments and Pareto optimal analysis are carried out, respectively. The simulation results show that the non-overlapping of the two sets of clutch inertia phases in the double-transition shift can effectively reduce the shift jerk. The overlapping of the torque phase and the inertia phase of the other clutch set can control the clutch energy loss at a low level due to using less shift time. automatic transmission double-transition shift multi-objective optimization shift jerk clutch energy loss Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Xinxin Zhao verfasserin aut Jue Yang verfasserin aut Wenming Zhang verfasserin aut In Applied Sciences MDPI AG, 2012 10(2020), 21, p 7794 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:10 year:2020 number:21, p 7794 https://doi.org/10.3390/app10217794 kostenfrei https://doaj.org/article/49921c2d822148389930785d6e1789ef kostenfrei https://www.mdpi.com/2076-3417/10/21/7794 kostenfrei https://doaj.org/toc/2076-3417 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_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_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 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 10 2020 21, p 7794
allfieldsSound	10.3390/app10217794 doi (DE-627)DOAJ013627449 (DE-599)DOAJ49921c2d822148389930785d6e1789ef DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Heng Zhang verfasserin aut Optimizing Automatic Transmission Double-Transition Shift Process Based on Multi-Objective Genetic Algorithm 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In order to improve fuel economy, the number of gears in the hydraulic automatic transmission of heavy-duty mining trucks is continuously increasing. Compared with single-transition shifts, double-transition shifts can optimize the structure of multi-speed transmissions, but the difficulty of control will also increase. In this paper, a dynamic model of a 6 + 2 speed automatic transmission and vehicle powertrain system are built based on the Lagrange method, and the dynamic analysis of the two sets of clutches that make up the double-transition shift are carried out. Since a simulation model of the double-transition shift process is built-in MATLAB/Simulink, the shift jerk and clutch energy loss are used as multi-objective, and the genetic algorithm is used to optimize the simulation. Five strategies for the overlapping time of the clutches are proposed, and simulation experiments and Pareto optimal analysis are carried out, respectively. The simulation results show that the non-overlapping of the two sets of clutch inertia phases in the double-transition shift can effectively reduce the shift jerk. The overlapping of the torque phase and the inertia phase of the other clutch set can control the clutch energy loss at a low level due to using less shift time. automatic transmission double-transition shift multi-objective optimization shift jerk clutch energy loss Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Xinxin Zhao verfasserin aut Jue Yang verfasserin aut Wenming Zhang verfasserin aut In Applied Sciences MDPI AG, 2012 10(2020), 21, p 7794 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:10 year:2020 number:21, p 7794 https://doi.org/10.3390/app10217794 kostenfrei https://doaj.org/article/49921c2d822148389930785d6e1789ef kostenfrei https://www.mdpi.com/2076-3417/10/21/7794 kostenfrei https://doaj.org/toc/2076-3417 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_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_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 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 10 2020 21, p 7794
language	English
source	In Applied Sciences 10(2020), 21, p 7794 volume:10 year:2020 number:21, p 7794
sourceStr	In Applied Sciences 10(2020), 21, p 7794 volume:10 year:2020 number:21, p 7794
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	automatic transmission double-transition shift multi-objective optimization shift jerk clutch energy loss Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry
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container_title	Applied Sciences
authorswithroles_txt_mv	Heng Zhang @@aut@@ Xinxin Zhao @@aut@@ Jue Yang @@aut@@ Wenming Zhang @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
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callnumber-first	T - Technology
author	Heng Zhang
spellingShingle	Heng Zhang misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc automatic transmission misc double-transition shift misc multi-objective optimization misc shift jerk misc clutch energy loss misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry Optimizing Automatic Transmission Double-Transition Shift Process Based on Multi-Objective Genetic Algorithm
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topic_title	TA1-2040 QH301-705.5 QC1-999 QD1-999 Optimizing Automatic Transmission Double-Transition Shift Process Based on Multi-Objective Genetic Algorithm automatic transmission double-transition shift multi-objective optimization shift jerk clutch energy loss
topic	misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc automatic transmission misc double-transition shift misc multi-objective optimization misc shift jerk misc clutch energy loss misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry
topic_unstemmed	misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc automatic transmission misc double-transition shift misc multi-objective optimization misc shift jerk misc clutch energy loss misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry
topic_browse	misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc automatic transmission misc double-transition shift misc multi-objective optimization misc shift jerk misc clutch energy loss misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry
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title	Optimizing Automatic Transmission Double-Transition Shift Process Based on Multi-Objective Genetic Algorithm
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title_full	Optimizing Automatic Transmission Double-Transition Shift Process Based on Multi-Objective Genetic Algorithm
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title_sort	optimizing automatic transmission double-transition shift process based on multi-objective genetic algorithm
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title_auth	Optimizing Automatic Transmission Double-Transition Shift Process Based on Multi-Objective Genetic Algorithm
abstract	In order to improve fuel economy, the number of gears in the hydraulic automatic transmission of heavy-duty mining trucks is continuously increasing. Compared with single-transition shifts, double-transition shifts can optimize the structure of multi-speed transmissions, but the difficulty of control will also increase. In this paper, a dynamic model of a 6 + 2 speed automatic transmission and vehicle powertrain system are built based on the Lagrange method, and the dynamic analysis of the two sets of clutches that make up the double-transition shift are carried out. Since a simulation model of the double-transition shift process is built-in MATLAB/Simulink, the shift jerk and clutch energy loss are used as multi-objective, and the genetic algorithm is used to optimize the simulation. Five strategies for the overlapping time of the clutches are proposed, and simulation experiments and Pareto optimal analysis are carried out, respectively. The simulation results show that the non-overlapping of the two sets of clutch inertia phases in the double-transition shift can effectively reduce the shift jerk. The overlapping of the torque phase and the inertia phase of the other clutch set can control the clutch energy loss at a low level due to using less shift time.
abstractGer	In order to improve fuel economy, the number of gears in the hydraulic automatic transmission of heavy-duty mining trucks is continuously increasing. Compared with single-transition shifts, double-transition shifts can optimize the structure of multi-speed transmissions, but the difficulty of control will also increase. In this paper, a dynamic model of a 6 + 2 speed automatic transmission and vehicle powertrain system are built based on the Lagrange method, and the dynamic analysis of the two sets of clutches that make up the double-transition shift are carried out. Since a simulation model of the double-transition shift process is built-in MATLAB/Simulink, the shift jerk and clutch energy loss are used as multi-objective, and the genetic algorithm is used to optimize the simulation. Five strategies for the overlapping time of the clutches are proposed, and simulation experiments and Pareto optimal analysis are carried out, respectively. The simulation results show that the non-overlapping of the two sets of clutch inertia phases in the double-transition shift can effectively reduce the shift jerk. The overlapping of the torque phase and the inertia phase of the other clutch set can control the clutch energy loss at a low level due to using less shift time.
abstract_unstemmed	In order to improve fuel economy, the number of gears in the hydraulic automatic transmission of heavy-duty mining trucks is continuously increasing. Compared with single-transition shifts, double-transition shifts can optimize the structure of multi-speed transmissions, but the difficulty of control will also increase. In this paper, a dynamic model of a 6 + 2 speed automatic transmission and vehicle powertrain system are built based on the Lagrange method, and the dynamic analysis of the two sets of clutches that make up the double-transition shift are carried out. Since a simulation model of the double-transition shift process is built-in MATLAB/Simulink, the shift jerk and clutch energy loss are used as multi-objective, and the genetic algorithm is used to optimize the simulation. Five strategies for the overlapping time of the clutches are proposed, and simulation experiments and Pareto optimal analysis are carried out, respectively. The simulation results show that the non-overlapping of the two sets of clutch inertia phases in the double-transition shift can effectively reduce the shift jerk. The overlapping of the torque phase and the inertia phase of the other clutch set can control the clutch energy loss at a low level due to using less shift time.
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title_short	Optimizing Automatic Transmission Double-Transition Shift Process Based on Multi-Objective Genetic Algorithm
url	https://doi.org/10.3390/app10217794 https://doaj.org/article/49921c2d822148389930785d6e1789ef https://www.mdpi.com/2076-3417/10/21/7794 https://doaj.org/toc/2076-3417
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Optimizing Automatic Transmission Double-Transition Shift Process Based on Multi-Objective Genetic Algorithm

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