Research on energy-saving control of agricultural hybrid tractors integrating working condition prediction

Copyright: © 2024 Feng et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited..

To address the issues of tractors using too much fuel and not being energy efficient, a predictive control strategy based on Pontryagin's minimum principle integrating working condition prediction is proposed for agricultural hybrid tractors. The Dongfanghong 1804 tractor is being used for research. Firstly, the main parameters of the hybrid drive system are determined and modeled. Secondly, based on the adaptive cubic exponential forecasting method, the working condition information for a period of time in the future is predicted through historical working condition information. Furthermore, combining the predicted working conditions information, the goal is to minimize the total energy consumption cost of the entire machine. Motor power and diesel engine power are control variables. The battery state of charge is a state variable. Subsequently, a predictive control strategy based on Pontryagin's minimum principle integrating working condition prediction is proposed. Finally, the simulation test is carried out based on the MATLAB simulation platform. Research indicates: under plowing conditions, compared with the power following control strategy, the proposed predictive control strategy can effectively manage the performance of the diesel engine and motor, ensuring they operate at their most efficient level. The total energy consumption costs of the power following control and predictive control strategies are 37.17 China Yuan (CNY) and 33.67 CNY, respectively. The cost of energy used is decreased by 9. 42%, which helps make tractor field plowing more efficient and economical.

Medienart:

E-Artikel

Erscheinungsjahr:

2024

Erschienen:

2024

Enthalten in:

Zur Gesamtaufnahme - volume:19

Enthalten in:

PloS one - 19(2024), 3 vom: 07., Seite e0299658

Sprache:

Englisch

Beteiligte Personen:

Feng, Ganghui [VerfasserIn]
Zhang, Junjiang [VerfasserIn]
Yan, Xianghai [VerfasserIn]
Dong, Chunhong [VerfasserIn]
Liu, Mengnan [VerfasserIn]
Xu, Liyou [VerfasserIn]

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Journal Article

Anmerkungen:

Date Completed 11.03.2024

Date Revised 11.03.2024

published: Electronic-eCollection

Citation Status MEDLINE

doi:

10.1371/journal.pone.0299658

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM369417968