Improving performance of electric vehicle drive system based a five-phase PMSM under fault using ANN and MPC
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces an advanced speed tracking control for electric vehicle (EV) drive systems, effectively managing open-phase motor faults. The novel strategy enhances efficiency and reliability while reducing torque ripple and energy consumption.
Area Of Science
- Electrical Engineering
- Automotive Engineering
- Control Systems
Background
- Electric vehicle (EV) drive systems require robust control strategies to ensure reliability during motor faults.
- Open-phase faults in EV motors can degrade performance and necessitate adaptive control solutions.
- Five-phase interior permanent magnet synchronous motors offer advantages in efficiency, power density, and fault tolerance.
Purpose Of The Study
- To develop and evaluate an advanced speed tracking control strategy for EV drive systems experiencing open-phase motor faults.
- To enhance the fault tolerance and performance of five-phase interior permanent magnet synchronous motors.
- To minimize torque ripple, current harmonics, and energy consumption under fault conditions.
Main Methods
- A five-phase interior permanent magnet synchronous motor model was used for simulations.
- A multi-layer perceptron artificial neural network was employed for online tuning of proportional-integral controller gains.
- Model predictive control with a tailored cost function was implemented to minimize harmonics and torque ripple.
- The proposed strategy was benchmarked against the Honey Badger optimization algorithm.
Main Results
- The proposed control strategy significantly reduced motor torque ripples and speed percentage overshoot compared to conventional methods.
- Lower values for mean square error and integral absolute error were achieved across two distinct drive cycles.
- The method resulted in lower total harmonic distortion (THD) and demonstrated energy savings.
Conclusions
- The advanced speed tracking control strategy effectively handles open-phase motor faults in EV drive systems.
- The integration of artificial neural networks and model predictive control provides adaptive and optimized performance.
- The proposed method offers a promising solution for improving the efficiency, reliability, and fault tolerance of EV powertrains.
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