Thermal analysis of brushless DC motor using multiobjective optimization

Abstract

In this article, the design of brushless DC (BLDC) motor is performed using multiobjective optimization algorithm (MOOA) by satisfying multiple objectives. Initially, sensitivity analysis is carried out to find the most influencing parameters that affect the performance of BLDC motor. MOOAs such as Pareto envelope-based selection algorithm (PESA), Pareto archived evolution strategy (PAES) and nondominated sorting genetic algorithm-II (NSGA-II) is employed in the optimal design of the BLDC motor. The proposed MOOA have three objectives namely: output torque maximization, volume minimization, and minimization of total losses. MOOAs are analyzed using performance metrics and qualitative comparison is provided to select the best algorithm. Later, finite element method (FEM) is used to investigate the transient and thermal characterization on the BLDC motor designed using NSGA-II. The thermal results thus obtained using NSGA-II for the above motor under different operating conditions is also compared with the existing single objective optimization algorithm. From the comparisons, it is observed that NSGA-II algorithm significantly outperforms the existing single objective optimization algorithm. Finally, the usefulness of the designed machine based on NSGA-II is compared with the results obtained from simulation and hardware analysis.

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“…In the meantime, Swarm Intelligence (SI) is involved with an attractive figure in the area of applied electromagnetics. Numerous bio-inspired SI algorithms, such as Multi-Objective Particle Swarm Optimization (MOPSO) [7], non-dominated sorting genetic algorithm Version-II [12], bat algorithm [13] and it's variant Multi-Objective Bat Algorithm (MOBA) [9], krill herd optimizer [14] and it"s variant Multi-objective Krill Herd Optimizer (MOKHO) [14], Multi-Objective Grey Wolf Optimizer (MOGWO) [15], Multi-Objective Whale Optimization Algorithm (MOWOA) [16], Multi-Objective Moth Flame Optimizer (MOMFO) [17], predator-prey biogeography-based optimization [18], imperialist competitive optimizer [19] and it"s variant Multi-Objective modified imperialist competitive optimizer [19], pigeon-inspired optimizer [20], and it"s variant multi-objective pigeon-inspired optimizer [20], and sequential quadratic programming [21] have been directly applied to the design problem of BLDC motor. The optimal solutions are a group of non-dominated Pareto with the best trade-off between two or more cost functions placed on the Pareto front in multiobjective optimization problems (MOOPs).…”