Energy optimization control of extended-range hybrid combine harvesters based on quasi-cycle power demand estimation
This study develops an energy management strategy (EMS) for hybrid combine harvesters to address fluctuating power demands in agricultural operations. By segmenting harvesting processes into quasi-periodic cycles linked to machine dynamics, the method integrates component-specific power models (hea...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
PAGEPress Publications
2025-05-01
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| Series: | Journal of Agricultural Engineering |
| Subjects: | |
| Online Access: | https://www.agroengineering.org/jae/article/view/1819 |
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| Summary: | This study develops an energy management strategy (EMS) for hybrid combine harvesters to address fluctuating power demands in agricultural operations. By segmenting harvesting processes into quasi-periodic cycles linked to machine dynamics, the method integrates component-specific power models (header, conveyor, drum) for accurate energy estimation. Real-time feed rate adjustments are achieved through dynamic responses of critical components, optimizing cycle duration and power allocation. A genetic algorithm synchronizes energy distribution and cycle timing to minimize fuel consumption. Validated via AMESim/Simulink co-simulation with dual engine models, the strategy reduces fuel use by 21.1% compared to conventional systems. Key innovations include quasi-periodic load segmentation, component-response-based feed rate prediction, and GA-driven multi-objective optimization. The approach enhances adaptability to variable harvesting conditions, offering a scalable framework for energy-efficient electrification in agriculture. Results demonstrate significant potential for hybrid systems in reducing operational costs and emissions while maintaining productivity under dynamic workloads.
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| ISSN: | 1974-7071 2239-6268 |