Enhancing performance of Parallel Hybrid Electric Vehicles using Powell's Artificial Bee Colony method

Area of Science:

• Engineering
• Computer Science

Background:

• Hybrid Electric Vehicles (HEVs) offer better fuel efficiency and lower emissions than conventional cars.
• Optimizing HEV performance is crucial for environmental and economic benefits.

Purpose of the Study:

• To enhance Hybrid Electric Vehicle (HEV) performance using Powell's Artificial Bee Colony (ABC) heuristic approach.
• To evaluate the effectiveness of Powell's ABC on fuel efficiency and emissions across different driving cycles.

Main Methods:

• Implemented Powell's ABC heuristic for multi-parameter optimization of HEVs.
• Applied the approach to standard driving cycles: FTP, ECE-EUDC, and UDDS.
• Incorporated PNGV constraints and four objective function parameters.

Main Results:

• Significant improvements observed across all driving cycles.
• ECE-EUDC: 10.03% fuel efficiency, 18.4% emissions reduction, 11.1% overall efficiency.
• UDDS: 18.2% fuel efficiency, 43.24% emissions reduction, 10.1% overall efficiency.
• FTP: 39.98% fuel economy, 43.75% emissions reduction, 11.6% overall efficiency.

Conclusions:

• Powell's ABC approach significantly enhances HEV fuel efficiency and reduces emissions.
• The method demonstrates faster convergence and more precise solutions compared to conventional techniques.
• Powell's ABC is effective across diverse driving conditions for HEV optimization.