The new thermal management structure of the motor is designed to ensure effective heat dissipation while maintaining low pressure loss, reduce the required power of the cooling pump. The dimensions of the new thermal management structure of the motor are optimized through the establishment of a multi-objective optimization platform. Computational Fluid Dynamics (CFD) simulations were conducted through single-factor analysis to compare and preliminarily determine the number of turns in the spiral channel. The key structural dimensions of the flow channel were adjusted using orthogonal design method for CFD simulations. The obtained complete orthogonal table was used as a dataset for comparing and analyzing the optimization of the Backpropagation (BP) neural network using the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO) algorithm. Results showed a higher fitting accuracy after optimization with the Particle Swarm Optimization algorithm. The Non-dominated Sorting Genetic Algorithm III (NAGA III) was employed for multi-objective optimization of the key flow channel dimensions. The optimal solution identified from the Pareto optimal solution set graph, which is the lowest point combining the highest stator temperature and maximum pressure in the flow channel, was verified through simulation to have a prediction accuracy of 99% for the comprehensive optimization algorithm. Furthermore, the simulation results of the cooling system obtained from multi-objective optimization were compared with the original cooling system, the comparison results show that the maximum pressure has decreased by 43.13%. Orthogonal design methods and composite algorithms are employed to replace traditional optimization methods for multi-objective optimization of motor cooling system in this study, offers a new perspective for the structural design of motor cooling systems.
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