Research on multi-objective optimization of hybrid vehicles based on improved particle swarm algorithm
To improve the economy,power and smoothness of hybrid electric vehicles,we take a parallel hybrid electric vehicle as the research object and employ the control strategy parameters and power system parameters as optimization variables,and the power battery charge balance as constraints to build a Multi-objective optimization model.During the optimization process,chaos operators and cosine strategies are introduced to improve the speed formula,inertia weight and learning factor of the particle swarm optimization algorithm.We propose an improved particle swarm optimization algorithm with simulation and optimization.Our results show while meeting the constraints,our algorithm improves the economy,ride comfort and power performance by 15.88％,11.71％and 3.51％respectively after optimization.Meanwhile,the efficiency distribution of the engine and motor operating points improve markedly.
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