Journal
IEEE ACCESS
Volume 7, Issue -, Pages 38224-38235Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2906488
Keywords
Shared battery station; dispatching strategy; electric vehicle; peak shaving and valley filling; divisional battery control
Categories
Funding
- National Key Research and Development Program of China [2018YFB1702300]
- National Natural Science Foundation of China [61573303]
- Natural Science Foundation of Hebei Province [E2017203284]
- China Postdoctoral Science Foundation [2016M601282]
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With increasing application of electric vehicle (EV), the battery swapping station (BSS) and battery charging station (BCS) have gradually gained recognition by electric vehicle users. With the stations, it is possible to refuel within several minutes, which promotes the EV popularization greatly. In this paper, a shared battery station (SBS) model is proposed, which is a multi-functional facility having the ability to charge, discharging, sleeping, and swapping abilities. Just like other shared economic, the customer has temporary access to the battery and pays corresponding fees according to swapping energy. Different from the traditional BCS and BSS, the SBS has a new business model. Besides, based on divisional battery control method, a battery dispatching strategy is proposed to control the charging, discharging, sleeping, and swapping processes. Through the divisional battery control method, the number of variables can be reduced greatly. Hence, the large-scale battery dispatching problem can be solved reasonably and quickly. From the view of the operator, an optimization objective function to maximize the revenue is established to optimize the number of batteries in each segment in each time slot. The dispatching strategy and the objective function cooperate with each other during the SBS operation to satisfy customers' battery demand, to ensure a sustainable and safe operation, and to participate in peak shaving and valley filling. Using the genetic algorithm, we perform extensive simulations to validate the optimization model and to demonstrate the efficiency of the dispatching strategy.
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