Energy management strategy for methanol hybrid commercial vehicles based on improved dung beetle algorithm...

Area of Science:

• Automotive Engineering
• Control Systems
• Artificial Intelligence

Background:

• Rule-based energy management strategies (EMS) in hybrid commercial vehicles often exhibit poor adaptability and robustness, leading to suboptimal fuel economy.
• Traditional methods struggle to balance the complex operational demands of internal combustion engines and battery power systems.

Purpose of the Study:

• To develop an improved dung beetle algorithm (DBO) optimized multi-fuzzy control EMS for hybrid commercial vehicles.
• To enhance the adaptability, robustness, and economic performance of vehicle energy management.

Main Methods:

• Established a rule-based EMS by defining efficient operating zones for the methanol engine and power battery.
• Integrated cosine, Lévy flight, and Cauchy Gaussian mutation strategies with Tent chaotic mapping to improve the DBO's global search capability and avoid local optima.
• Designed fuzzy controllers for driving-charging and hybrid modes, optimizing them using the enhanced DBO with fuel consumption and battery SOC fluctuation as objectives.

Main Results:

• The enhanced DBO-optimized fuzzy control EMS demonstrated superior performance compared to traditional rule-based strategies.
• Achieved a 9.07% reduction in overall vehicle fuel consumption.
• Reduced battery state of charge (SOC) fluctuations by 3.43%.

Conclusions:

• The proposed improved DBO-optimized fuzzy control EMS offers enhanced adaptability and robustness for hybrid commercial vehicles.
• Continuous real-time torque distribution adjustment between the engine and motor significantly improves vehicle economy.
• This approach provides a viable solution for optimizing energy management in hybrid commercial vehicles.