Genetic Algorithm-Based Multiobjective Optimization of Three-Phase Coupled Inductor With Ladder Magnetic Core

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Abstract
Nowadays, interleaved boost converter (IBC) is widely used in electric vehicle on-board power supplies, which puts forward high requirements for the efficiency and power density of the converter due to high power and limited interior space. Consequently, a three-phase coupled inductor with ladder magnetic core is adopted in this paper and a multi-objective optimization approach based on genetic algorithm (GA) is proposed for converter efficiency and coupled inductor volume. Firstly, the comprehensive objective function and corresponding constraint condition are formulated for the coupled inductor structure to facilitate global optimization. During the modeling process, a source controlled by the inductor voltage is introduced into the magnetic reluctance model to analyze AC magnetic flux. On this foundation, GA is employed to optimize the objective function, which improves the optimization efficiency significantly and the Pareto front of converter efficiency and coupled inductor volume can be obtained according to the optimization results. Finally, an experimental prototype of 30kW is established with a power density of 22kW/L and a maximum efficiency of 98.6%. Compared with non-coupled inductor, the volume is reduced by 43.8% and the power density is increased by 12.8%.
Funding Information
  • National Natural Science Foundation of China (51977146)

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