Improving the operation of electric vehicles (EVs) under
fault is a very important subject because it increases their ability
to operate in case of an emergency. This paper proposes a faulttolerant control methodology for an EV drive system under open
phase fault (OPF). The 5-ph interior permanent magnet
synchronous motor (IPMSM) is employed in the drive system
because it has several merits, such as high efficiency and
reliability. The proposed technique is based on utilizing a
reinforcement learning (RL) control algorithm, based twindelayed deep deterministic policy gradient (TD3) algorithm, to
operate the motor at maximum torque per ampere under OPF.
This technique is compared with the PI-online tuning method that
employs fuzzy logic control (FLC). The torque ripples are
minimized using the model predictive control with a constructed
cost function. The European drive cycle (ECE-15) is used to test
the performance of the proposed technique. This technique is
verified by obtaining simulation results using the MATLAB
Simulink package. The results of the simulation indicate that the
proposed control strategy results in superior performance as it
achieved lower values of mean square error, integral square error,
and percentage overshoot compared with the classical PI online
tuning using FLC. |
