Controlling the speed of electric vehicles (EVs) is essential. Electric motors power the electric vehicles, and the power of electric vehicles is provided by Permanent Magnet Synchronous Motors (PMSMs). This paper proposes a control unit with Honey Bees Algorithm (HBA) optimized fuzzy PID controller. This paper introduces the availability of using PMSM software to simulate electrical power systems and machines. In this paper, a PMSM model is simulated to serve as an EV propelling powertrain application. Due to their importance in various fields, such as electric vehicles, PMSM drive systems are discussed and evaluated. Simulation results of the proposed HBA-based Fuzzy PID control scheme combined with a current controller are given at two different loads, one below the rated load and one above it. This work shows that the model can run in transient and steady states. The simulation results show that acceptable, logical values are not exceeded, and stability is maintained within changing working conditions depending on the current driving and performance conditions.

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Honey Bees Algorithm Based Fuzzy PID Controller to Drive a Synchronous Motor in an Electric Vehicle

  • Bashar Reda,
  • Rosy Pradhan

摘要

Controlling the speed of electric vehicles (EVs) is essential. Electric motors power the electric vehicles, and the power of electric vehicles is provided by Permanent Magnet Synchronous Motors (PMSMs). This paper proposes a control unit with Honey Bees Algorithm (HBA) optimized fuzzy PID controller. This paper introduces the availability of using PMSM software to simulate electrical power systems and machines. In this paper, a PMSM model is simulated to serve as an EV propelling powertrain application. Due to their importance in various fields, such as electric vehicles, PMSM drive systems are discussed and evaluated. Simulation results of the proposed HBA-based Fuzzy PID control scheme combined with a current controller are given at two different loads, one below the rated load and one above it. This work shows that the model can run in transient and steady states. The simulation results show that acceptable, logical values are not exceeded, and stability is maintained within changing working conditions depending on the current driving and performance conditions.