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Research on energy optimization control strategy for parallel hybrid tractor based on AIPSO.
Xiaohui Liu1, Yiwei Wu1,2, Jingyun Zhang1,2
1College of Vehicle and Traffic Engineering, Henan University of Science and Technology, Luoyang, Henan, China.
Plos One
|February 13, 2025
Summary
A new adaptive immune particle swarm optimization fuzzy control strategy (AIPSOFCS) significantly improves fuel economy and operational efficiency in parallel hybrid tractors. This advanced control method offers substantial benefits for agricultural machinery.
Area of Science:
- Agricultural Engineering
- Control Systems
- Energy Systems
Background:
- Torque is a critical factor for fuel economy and efficiency in parallel hybrid tractors.
- Optimizing energy control is essential for sustainable agricultural development.
- Existing control strategies may not fully leverage the potential of hybrid tractor technology.
Purpose of the Study:
- To develop and evaluate an advanced energy optimization control strategy for parallel hybrid tractors.
- To enhance fuel economy and operational efficiency through intelligent control.
- To investigate the impact of demand torque on tractor performance.
Main Methods:
- Design of power system parameters and a dynamic model for the tractor.
- Proposal of an adaptive immune particle swarm optimization fuzzy control strategy (AIPSOFCS).
- Simulation analysis under plowing and rotary tillage conditions, comparing AIPSOFCS with PFCS and FCS.
- Hardware-in-the-loop (HIL) testing for controller validation.
Main Results:
- AIPSOFCS demonstrated superior fuel economy and operational efficiency compared to PFCS and FCS.
- Fuel economy improvements of 8.45% (plowing) and 2.40% (rotary tillage) over PFCS.
- Fuel economy improvements of 2.93% (plowing) and 4.07% (rotary tillage) over FCS.
- HIL testing confirmed the practical effectiveness of the AIPSOFCS controller.
Conclusions:
- The proposed AIPSOFCS offers significant advancements in energy optimization for parallel hybrid tractors.
- This strategy enhances both fuel economy and operational efficiency, crucial for modern agriculture.
- The research provides valuable theoretical support for future developments in intelligent agricultural machinery.
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