自动共享电动汽车的云-边协调优化模型与控制策略

针对自动共享电动汽车(shared autonomous electric vehicles,SAEV)运行出现的车辆分配不平衡以及充电优化问题，提出了一种基于云-边协调计算的SAEV优化控制策略。首先，给出SAEV再平衡优化模型以及再平衡任务分配算法;其次，考虑使用V2G和动态电价进行SAEV车队的充放电优化，给出SAEV车队能量交换模型以及出行订单分配算法，以减少整个SAEV车队系统的充电成本；再次，利用云-边协调通信将这些优化结果信息在不同平台间进行互动传输，实现电动汽车的最优充电与迁移策略；最后，通过MATLAB使用真实的深圳出租车数据对该优化控制方法进行验证。结果表明，该框架可降低充电成本,提高交通效率，有望扩展应用到更大规模的系统中。所提云-边协调控制策略将复杂的SAEV优化问题分解成3个子问题进行求解，为SAEV的最优运行提供了一种新的方法。

Cloud-edge coordinative optimization model and control strategy for shared autonomous electric vehicles

Aiming at the problems of vehicle imbalance and charging optimization of shared autonomous electric vehicle (SAEV) operation, an optimal control strategy based on cloud-edge coordination calculation for SAEV was proposed. Firstly, a SAEV rebalancing optimization model and a rebalancing task allocation algorithm were proposed. Secondly, considering V2G and dynamic electricity price to optimize the charging and discharging of SAEV fleet, a SAEV fleet energy exchange model and a trip order allocation algorithm were proposed to reduce the charging cost of the entire SAEV fleet system. Then, the cloud-edge coordination communication was used to transfer these optimization results among different platforms, so as to realize the optimal charging and moving strategy of electric vehicles. Finally, the optimization control method was verified in MATLAB by using the real Shenzhen taxi data. The results show the framework can reduce the charging costs and improve the transportation efficiency, and is expected to be applied to larger scale of systems. The proposed cloud-edge coordinative control strategy divides the complex SAEV optimization problem into three sub-problems, and provides a new method and idea for the optimal operation of SAEV.