A Novel A-ECMS Energy Management Strategy Based on Dragonfly Algorithm for Plug-in FCEVs

Area of Science:

• Automotive Engineering
• Sustainable Energy Systems
• Control Theory

Background:

• Managing energy in 4-wheel-drive plug-in fuel cell electric vehicles (PFCEVs) is complex due to interconnected powertrain components.
• Optimizing energy management strategies (EMSs) is crucial for improving PFCEV efficiency and operational adaptability.

Purpose of the Study:

• To develop a novel adaptive equivalent consumption minimization strategy (A-ECMS) for coordinated control of PFCEV powertrain components.
• To enhance the energy-saving capabilities and adaptability of PFCEVs to various operating conditions.

Main Methods:

• Utilized the dragonfly algorithm (DA) to design the A-ECMS for precise power distribution between front and rear motors, fuel cell system, and battery.
• Implemented a hierarchical energy management framework integrating A-ECMS with the base equivalent consumption minimization strategy (ECMS).
• Simulated and evaluated the performance of the proposed A-ECMS against conventional methods.

Main Results:

• The A-ECMS demonstrated significant energy-saving advantages in PFCEVs.
• The proposed strategy showed enhanced adaptability to different operating conditions.
• Simulation results validated the superior energy-saving performance and optimal control of A-ECMS.

Conclusions:

• The novel A-ECMS effectively manages energy in complex PFCEV powertrains.
• This strategy offers state-of-the-art energy-saving performance for multi-degree-of-freedom systems.
• The A-ECMS provides a robust solution for optimizing PFCEV efficiency and adaptability.