Parameters Optimization for Hybrid Energy Storage System of Electric Vehicles Based on Cross-Entropy Algorithm

In order to improve the dynamic performance of electric vehicles and reduce costs, a parameter optimization method for vehicle-mounted hybrid power supply based on cross-entropy (CE) algorithm is explored with the intent of minimizing the hybrid power supply cost and power consumption. Firstly, a hybrid electric vehicle is used as the object, and the capacity ranges of its lithium-ion batteries and super-capacitors are determined according to the energy and power performance indexes. Secondly, the multi-objective optimization function of minimizing power supply cost and power consumption and the vehicle simulation model are established in ADVISOR. Subsequently, with CE algorithm, the mean and variance of the Gaussian probability density function are updated by the continuous iterations of populations to find out the optimal Pareto solution set. Finally, the typical solutions are selected to analyze the cost, power consumption and vehicle performance. The results show that under the basic requirements, 100 optimal solutions are found, which constitute an optimal Pareto solution set. Compared with the results of (non-dominated sorting genetic algorithm-Ⅱ) NSGA-Ⅱ, the convergence and distribution of CE algorithm are better, the cost of hybrid power supply is reduced by 9.49% and the vehicle power consumption by 22.81% on average. Furthermore, the maximum error of vehicle speed is reduced by 16.15% under UDDS cycle condition, and the vehicle dynamic performance is improved significantly with the acceleration time of 100 km reduced by 7.81% and the maximum speed increased by 1.98%.