Optimal selection for an air suspension system on buses through a unique high level parameter in genetic algorithms

Area of Science:

• Mechanical Engineering
• Automotive Engineering
• Control Systems

Background:

• Air suspension systems are crucial for ground vehicles, especially buses.
• The air spring is the key component enabling the superior performance of these systems.
• Optimizing air spring parameters is essential for balancing ride comfort and safety.

Purpose of the Study:

• To apply genetic algorithms for optimizing air spring parameters in bus air suspension systems.
• To develop a comprehensive bus model with an air spring element based on the Gensys model.
• To determine optimal air spring parameters that balance road safety and ride comfort.

Main Methods:

• Utilized genetic algorithms to optimize seven key parameters of the air spring element.
• Developed a full bus model incorporating an air suspension system with an air spring, based on the Gensys model.
• Determined parameter bounds through real-world experiments and validated optimal parameters via time-domain simulations using ISO 8608 random road profiles.

Main Results:

• Identified optimal air spring parameters through a single alpha (α) value, allowing customization for specific criteria.
• Achieved a 15% improvement in ride comfort with optimal air spring parameters (α = 0.5).
• Demonstrated that optimal parameter selection guarantees road safety while enhancing ride comfort.

Conclusions:

• Genetic algorithms provide an effective method for optimizing air spring parameters in bus air suspension.
• The proposed optimization strategy successfully balances ride comfort and road safety.
• The findings offer a practical approach for enhancing bus suspension performance.