Application of particle swarm optimisation (PSO) in efficiency optimisation of vector controlled surface mounted permanent magnet synchronous motor (PMSM) drive is presented in this study. Analytical expressions for the controllable electrical loss in PMSM are derived and optimised through control of direct axis (d-axis) stator current, according to the desired speed of operation and load torque. The optimal value of d-axis current has been obtained through analytical method as well as through PSO. The motor efficiency is maximised by using the optimal d-axis current to minimise the air gap flux, resulting in reduction of the core losses, especially at light load conditions. The robustness of the efficiency optimisation algorithms are demonstrated through parametric sensitivity analysis. Implementations of efficiency optimisation algorithms are carried out in Matlab and the performance of PMSM drive is described under steady state as well as transient operating conditions. The effect of efficiency optimisation algorithms in different region of operation of PMSM such as constant speed-variable load torque and constant torque-variable speed modes are described.

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In this study, an Integrated Taguchi method-assisted polynomial Metamodelling & Genetic Algorithm (ITM&GA)-based optimisation technique is implemented for design optimisation of a surface inset permanent magnet synchronous motor (SIPMSM). The motor geometry is analysed by implementing the finite element method for application of the motor in electric compressors of the cooling system of an electric vehicle (EV). The polynomial surrogate model is computed with the help of Taguchi experiments to eliminate the redesigning process of models to reach the optimum values of design parameters and reduce the ambiguity to select the best optimum solution in Traditional Taguchi Method. The root-mean-square error test is performed to validate the accuracy of metamodels. The optimum solutions are then converged using the GA technique. The optimum results are compared and presented. Using the ITM&GA technique, the reduction in unwanted ripples in torque and cogging torque along with the improved torque performance of the motor is achieved successfully. The proposed mechanism is effective in obtaining quick and accurate solutions for preliminary designs of the SIPMSM for the electric compressor application in EVs.

The intelligent optimization techniques have been introduced by carefully observing the behavior of various hunters like a whale, grey wolf, Aquila, and lizards for estimating global optimum solutions in fair time by forming appropriate mathematical models. However, hunting-based algorithms suffer from slow and pre-requisite convergence and get caught up in local optima. Aquila Optimizer (AO) is one of the recently developed hunting-based methods that encounter a similar type of shortcoming in a few situations. This research introduces the concept of chaotic mapping to the standard AO in order to increase the convergence speed. Also to maintain the balance of exploration performed by AO with its exploitation capability, a single stage evolutionary algorithm is also integrated with it. The performance of standard AO and modified AO are tested for well-defined unimodal and multimodal Benchmark functions. The proposed framework produces one population by standard AO and a new population by single stage genetic algorithm based evolutionary concept in which binary tournament selection, roulette wheel selection, shuffle crossing over and displacement mutationoccur to generate a new population.The chaotic mapping criteria are then applied to obtain various variants of the standard AO technique. The general results obtained from the proposed novel chaotic mapping-based advanced AO with single stage evolutionary algorithm shows that it outperforms the standard AO. This advanced technique is thus applied to real-world design engineering problems to study its significance from an industrial point of view.

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