Operating Point Optimization of Agricultural Tractor–Implement Combinations as Constraint Optimization Problem
Increasing the process efficiency of agricultural tasks is a key measure to decrease overall costs and CO2 emissions. However, optimizing tractor–implement combinations is challenging due to the variety of processes and implements and the complexity of the powertrains in modern tractors. In addition...
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| Published in | Eng (Basel, Switzerland) Vol. 6; no. 2; p. 27 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Basel
MDPI AG
01.02.2025
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2673-4117 2673-4117 |
| DOI | 10.3390/eng6020027 |
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| Abstract | Increasing the process efficiency of agricultural tasks is a key measure to decrease overall costs and CO2 emissions. However, optimizing tractor–implement combinations is challenging due to the variety of processes and implements and the complexity of the powertrains in modern tractors. In addition, overall process efficiency is an ambiguous optimization objective in agricultural processes as it relates resource consumption to harvest yields, which are only known at the end of a harvest season. The presented approach defines process constraints, ensuring optimization does not negatively affect harvest yield. These constraints allow for the formulation of explicit objective functions that are observable during the operation. The method establishes a mathematical foundation for the optimization of agricultural processes. The mathematical principles of the theoretical framework and the techniques used to define control constraints are explored, whereby the applicability to alternative objectives like optimizing the overall process cost is highlighted. To demonstrate the practical utility of the proposed approach, an optimization cycle is applied to a real-world scenario: adapting the working speed during the tillage process using a cultivator to maximize energy efficiency. The approach simplifies the optimization problem by formulation as a constraint optimization problem, allowing for improving the operating point of tractor–implement combinations with respect to observable process objective functions. The results underline the importance of advanced control strategies in agricultural machinery, advancing precision agriculture and promoting sustainable farming practices. |
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| AbstractList | Increasing the process efficiency of agricultural tasks is a key measure to decrease overall costs and CO2 emissions. However, optimizing tractor–implement combinations is challenging due to the variety of processes and implements and the complexity of the powertrains in modern tractors. In addition, overall process efficiency is an ambiguous optimization objective in agricultural processes as it relates resource consumption to harvest yields, which are only known at the end of a harvest season. The presented approach defines process constraints, ensuring optimization does not negatively affect harvest yield. These constraints allow for the formulation of explicit objective functions that are observable during the operation. The method establishes a mathematical foundation for the optimization of agricultural processes. The mathematical principles of the theoretical framework and the techniques used to define control constraints are explored, whereby the applicability to alternative objectives like optimizing the overall process cost is highlighted. To demonstrate the practical utility of the proposed approach, an optimization cycle is applied to a real-world scenario: adapting the working speed during the tillage process using a cultivator to maximize energy efficiency. The approach simplifies the optimization problem by formulation as a constraint optimization problem, allowing for improving the operating point of tractor–implement combinations with respect to observable process objective functions. The results underline the importance of advanced control strategies in agricultural machinery, advancing precision agriculture and promoting sustainable farming practices. |
| Author | Michiels, Lukas Kazenwadel, Benjamin Graf, Marina Geimer, Marcus |
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| Cites_doi | 10.1016/j.atech.2023.100363 10.1515/auto-2023-0042 10.51202/9783181023952-55 10.1016/0022-4898(73)90014-1 10.51202/9783181023747-361 10.36198/9783838551982 10.1007/978-3-030-32804-7 10.1017/9781108980647 10.4271/9780768094329 10.51202/9783181024447-195 10.51202/9783181023327-223 10.51202/9783181024270-519 |
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| Copyright | 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| References | Kazenwadel (ref_12) 2023; 71 Wismer (ref_18) 1973; 10 Boysen (ref_11) 2023; 6 ref_14 ref_13 ref_23 ref_22 ref_10 ref_21 ref_20 ref_1 ref_3 ref_2 ref_19 ref_17 ref_16 ref_15 ref_9 ref_8 ref_5 ref_4 ref_7 ref_6 |
| References_xml | – volume: 6 start-page: 100363 year: 2023 ident: ref_11 article-title: Modeling the soil-machine response of secondary tillage: A deep learning approach publication-title: Smart Agric. Technol. doi: 10.1016/j.atech.2023.100363 – volume: 71 start-page: 979 year: 2023 ident: ref_12 article-title: Aligning process quality and efficiency in agricultural soil tillage publication-title: at-Automatisierungstechnik doi: 10.1515/auto-2023-0042 – ident: ref_6 – ident: ref_8 doi: 10.51202/9783181023952-55 – volume: 10 start-page: 49 year: 1973 ident: ref_18 article-title: Off-road traction prediction for wheeled vehicles publication-title: J. Terramech. doi: 10.1016/0022-4898(73)90014-1 – ident: ref_2 – ident: ref_10 – ident: ref_4 doi: 10.51202/9783181023747-361 – ident: ref_16 doi: 10.36198/9783838551982 – ident: ref_15 – ident: ref_3 doi: 10.1007/978-3-030-32804-7 – ident: ref_13 doi: 10.1017/9781108980647 – ident: ref_1 doi: 10.4271/9780768094329 – ident: ref_7 doi: 10.51202/9783181024447-195 – ident: ref_14 – ident: ref_17 – ident: ref_19 – ident: ref_22 – ident: ref_23 – ident: ref_21 – ident: ref_5 doi: 10.51202/9783181023327-223 – ident: ref_20 – ident: ref_9 doi: 10.51202/9783181024270-519 |
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| SubjectTerms | Agricultural equipment agricultural processes Agricultural production Agricultural vehicles Agriculture Constraints Costs Energy consumption Energy efficiency Gardening Harvest operating point optimization Optimization Optimization techniques Powertrain Productivity Rentals Tractors |
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| Title | Operating Point Optimization of Agricultural Tractor–Implement Combinations as Constraint Optimization Problem |
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