Multi-objective optimization of design parameters for tractor hydro-mechanical continuously variable transmissions

Area of Science:

• Agricultural Engineering
• Mechanical Engineering
• Automotive Engineering

Background:

• Tractor transmission design parameters critically influence vehicle dynamics and fuel economy.
• Optimizing these parameters is challenging due to complex working conditions and operating modes.
• Hydro-mechanical continuously variable transmissions (CVTs) offer potential for improved performance.

Purpose of the Study:

• To optimize the design parameters of an independently developed hydro-mechanical CVT for tractors.
• To enhance transmission matching based on the tractor's whole life-cycle speed usage rate.
• To investigate the trade-offs between fuel consumption and hill-climbing ability.

Main Methods:

• Utilized a Multi-Objective Genetic Algorithm (MOGA) for optimization.
• Defined fuel consumption rate and hill climbing degree as objective functions.
• Selected influential parameters as design variables and established constraints.
• Employed a Pareto optimality principle combined with experimental design in modeFrontier for global optimization.

Main Results:

• A Pareto optimal solution set was obtained, reflecting the conflict between power and fuel economy.
• The optimized design variables satisfied transmission matching requirements effectively.
• The study identified optimal solutions within the defined constraints.

Conclusions:

• The MOGA approach successfully optimized hydro-mechanical CVT design parameters for tractors.
• Whole life-cycle speed usage rate is a crucial factor for effective transmission optimization.
• The findings provide valuable insights into balancing performance and efficiency in tractor transmissions.