电动汽车（electric vehicle，EV）既是交通网的车流负荷，又是电网的用电负荷，它的出行以及充电行为会对交通网和电网的运行规律造成影响。针对电动汽车单体进行随机抽样的传统预测方法，未能从电动汽车群体的移动运行状态考虑，提出了一种基于交通均衡理论的电动汽车充电负荷预测方法。首先在时间和空间两个维度上，分别对电动汽车和路网进行建模，采用A*算法构建电动汽车出行原目的地（origin-destination，OD）对的有效的路径集。计及用户有限理性，引入分时电价制定了一种计及交通流量约束的充电站电价，并结合随机效用理论与分时电价，建立了半动态交通均衡模型来求解交通流的分配。随后基于组合荷电状态（calculation method of combined state of charge，CSOC）的概率数值计算方法对电动汽车充电负荷进行求解。最后，基于上海市新能源汽车数据，分别以改进的13节点路网和上海市某区域路网分析验证了所提充电负荷预测方法的有效性，结果表明所提方法可以降低充电成本，缓解充电站充电压力。

Electric vehicles (EVs) are both traffic loads of the transport network and electricity loads of power grid, and their travelling as well as charging behaviours will have an impact on the operating laws of the transport network and power grid. Aiming at the traditional EV prediction method of random sampling for a single EV unit, which fails to consider the operation state from the movement of EV groups, in this paper, a method for forecasting electric vehicle charging loads that leverages traffic equilibrium theory is proposed. Firstly, models for electric vehicles and road networks are constructed in both time and space dimensions, and an efficient path set for origin-destination (OD) pairs of electric vehicles is generated using the A* algorithm. Taking into account users' limited rationality, a charging station electricity pricing, which incorporates traffic flow constraints, is formulated by introducing time-of-use (TOU) pricing. Furthermore, a semi-dynamic traffic equilibrium model is developed by integrating stochastic utility theory with TOU pricing to address the traffic flow allocation. then based on the probabilistic numerical calculation method of combined state of charge (CSOC), the charging load of electric vehicles is solved. Finally, based on the data of new energy vehicles in Shanghai, the effectiveness of the proposed charging load prediction method is verified by analysing the improved 13 nodes road network and a regional road network in Shanghai, the results show that the proposed method can reduce the charging cost and alleviate the charging pressure of charging stations.