Equivalent consumption minimization strategy based on global optimization of equivalent factor for hybrid tractor.
Energy management
Equivalent factor optimization
Genetic algorithm
Plug-in hybrid tractor
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
05 Jun 2024
05 Jun 2024
Historique:
received:
10
03
2024
accepted:
31
05
2024
medline:
6
6
2024
pubmed:
6
6
2024
entrez:
5
6
2024
Statut:
epublish
Résumé
Due to the increase in emission requirements for non-road vehicles in many countries and the reduction of agricultural personnel, tractors are developing towards high horsepower and electrification. According to the working conditions of high-horsepower tractors, a hydromechanical continuously variable transmission (HMCVT) is designed for hybrid tractors. Taking a tractor equipped with this transmission as the research object, an equivalent factor global optimization model was established and a genetic algorithm was used to optimize the equivalent factor S offline to obtain the optimal equivalent factor of the tractor under different operating mileage and the initial state of charge (SOC) of battery. By using the optimized equivalent factor, the tractor can be in the charge depleting (CD) mode for a longer time on the premise of making full use of the energy in the battery, so as to improve the auxiliary ability of the motor in the whole operation cycle to reduce the fuel consumption of the tractor. The effectiveness of the control strategy is verified by MATLAB/Simulink and hardware in the loop (HIL) tests, and the fuel economy of tractors is improved by 2.939% and 3.909% respectively in the two tests.
Identifiants
pubmed: 38839857
doi: 10.1038/s41598-024-63770-w
pii: 10.1038/s41598-024-63770-w
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
12911Subventions
Organisme : China Postdoctoral Science Foundation
ID : 2023M731370
Organisme : State Key Laboratory of Fluid Power and Mechatronic Systems
ID : GZKF-202214
Organisme : National Natural Science Foundation of China
ID : 52272435,52225212, U20A20333, U20A20331, 51875255
Organisme : Jiangsu Provincial Key Research and Development Program
ID : BE2020083-3, BE2019010-2
Informations de copyright
© 2024. The Author(s).
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