电动汽车充电路径不确定优化调度

电动汽车充电路径优化调度中的很多参数变量都具有不确定性,传统的优化调度方法适用性较差。为解决该问题,不确定优化方法被应用到电动汽车充电路径优化调度中,由此建立了基于随机期望值的不确定优化调度模型。鉴于传统的粒子群优化算法容易陷于局部优化和收敛速度较慢,将模拟退火算法引入并组成混合智能算法进行模型求解。对比实验证明该混合智能算法能够有效减少电动汽车车主到充电站所用的行驶距离、在充电站的等待时间和充电时间。

Optimal scheduling of electric vehicles for charging route under uncertainty

The traditional optimal scheduling method has poor applicability in optimal scheduling of electric vehicles (EVs) for charging route because many parameter variables are uncertain. In view of the problem, uncertain optimization is applied to optimal scheduling of the EVs for charging route. Optimal scheduling model for charging route of the EVs based on random expected value is built. For the disadvantage that the particle swarm optimization algorithm is easy to fall into local optimization and the convergence speed is slow, the simulated annealing algorithm is introduced into the hybrid intelligent algorithm to solve the model. The comparison experiments show that the hybrid intelligent algorithm can effectively reduce the driving distance which the owners reach the charging station, waiting time and charging time at the charging station.