Enhancing the performance of 5ph-IPMSM control plays a crucial role in advancing various innovative applications such as electric vehicles. This paper proposes a new reinforcement learning (RL) control algorithm based twin-delayed deep deterministic policy gradient (TD3) algorithm to tune two cascaded PI controllers in a five-phase interior permanent magnet synchronous motor (5ph-IPMSM) drive system based model predictive control (MPC). The main purpose of the control methodology is to optimize the 5ph-IPMSM speed response either in constant torque region or constant power region. The speed responses obtained using RL control algorithm are compared with those obtained using four of the most recent metaheuristic optimization techniques (MHOT) which are Transit Search (TS), Honey Badger Algorithm (HBA), Dwarf Mongoose (DM), and Dandelion-Optimizer (DO) optimization techniques. The speed response are compared in terms of the settling time, rise time, maximum time and maximum overshoot percentage. It is found that the suggested RL based TD3 give minimum settling time and relatively low values for the rise time, max time and overshoot percentage which makes the RL provide superior speed responses compared with those obtained from the four MHOT. The drive system speed responses are obtained in the constant torque region and constant power region using MATLAB SIMULINK package.

This study presents an advanced speed tracking control strategy designed to handle open-phase motor faults in EV drive systems. The proposed control strategy is evaluated using two distinct drive cycles. A five-phase interior permanent magnet synchronous motor is employed due to its notable advantages, including high efficiency, reliability, power density, and inherent fault tolerance. The control strategy leverages a multi-layer perceptron artificial neural network for online tuning of proportional-integral controller gains, enabling adaptive performance. This approach is benchmarked against a recent metaheuristic optimization algorithm known as Honey Badger. To further enhance performance, model predictive control is applied using a tailored cost function to minimize current harmonics and torque ripple. Simulation results, conducted in MATLAB Simulink, validate the effectiveness of the proposed method. Compared to conventional technique, the new approach achieves lower values for motor torque ripples and speed percentage overshoot, mean square error and integral absolute error across both test drive cycles. Additionally, the proposed method gives lower THD and attains energy saving.

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 fault-tolerant 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 twin-delayed 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.

In this paper a control method is proposed for a system composed of PV array, five-phase impedance source inverter, five-phase induction motor and centrifugal pump. This method is based on controlling the motor speed to control the pump power as the insolation level or temperature change to attain the maximum power extraction from the PV-array. The motor speed is controlled by using artificial neural network which is trained to provide the desired inverter frequency and modulation index at any insolation level and temperature to attain the maximum PV operating power. The data of the neural network are based on the operation of the induction motor at constant air gap flux and perturb and observe method for maximum power point tracking. Simulation results are obtained using MATLAB Simulink to verify the proposed control method.

Speed tracking control (STC) and energy saving of an electric vehicle (EV) play a crucial role in the stability and effectiveness of the operating performance of an EV drive system (EVDS). This paper proposes a novel STC and energy saving methodology for an EVDS that is tested by the European drive cycle (ECE-15) and customized inspection and maintenance drive cycle (custom IM240). The EVDS adopted the 5-ph interior permanent magnet synchronous motor (IPMSM) due to its benefits like high efficiency, compact size, low noise, reliability, reduced torque ripples, increased power density, and improved fault tolerance. A multilayer perceptron (MLP) - artificial neural network (ANN) is utilized to tune the PI controller online in the drive system (DS). The gating pulses of the 5-phase voltage source inverter (VSI) are accurately generated using an MPC based on a desired cost function to reduce current harmonics and thus torque ripples. A comparative study between the online tuning using the MLP-ANN and offline tuning using the transit search optimization (TSO) technique is presented. The process of comparison includes the percentage overshoot, mean square error (MSE), integral absolute error (IAE), and percentage energy saving. The result of the comparison proves the effectiveness of the proposed control methodology that gives superior speed tracking performance of the EVDS and attains energy saving. The attained energy saving across a long period results in cost reduction of charging the batteries and increasing their lifetime.