A real-time strategy for vehicle-to-station recommendation in battery swapping mode

It is significant for electric vehicles (EVs) to swap their depleted batteries in the appropriate battery swapping stations because of the limited number of fully charged batteries. This study presents a real-time optimization strategy for recommending an optimal station for the EV upon its swapping request. The strategy aims to save the EV owners' time lost due to swapping, while improving the operational economy and stability of battery swapping stations and the distribution network. In this strategy, rolling optimization and Lyapunov optimization from two different time scales are combined. In particular, swapping demand is allocated by rolling optimization on a large time scale with predictive information. With the optimal allocation scheme, Lyapunov optimization theory is employed on a small time scale to transform the complex recommendation challenge into convex quadratic programming. Different recommendation strategies are compared in the Beijing traffic network and IEEE 33-bus system. Simulations suggest that our strategy achieves better results in terms of operational per-formance and solving speed.

Automated summary

It is important for electric vehicles (EVs) to swap their depleted batteries in the appropriate battery swapping stations due to the limited number of fully charged batteries. This study proposes a real-time optimization strategy that recommends an optimal station for EVs based on their swapping request, aiming to save time for EV owners and improve the operational efficiency and stability of battery swapping stations and the distribution network. The strategy combines rolling optimization and Lyapunov optimization from two different time scales, and achieves better results in terms of operational performance and solving speed through simulations.