Optimal Day-Ahead Scheduling of Fast EV Charging Station With Multi-Stage Battery Degradation Model

The paper proposes a day-ahead scheduling framework with a novel multi-stage battery degradation modeling method for an electric vehicle (EV) fast charging station (FCS) equipped with a battery energy storage system (BESS). Unlike previous studies, which employ a single battery degradation model to represent the aging process, this paper proposes a novel multi-stage battery degradation modeling method to accurately capture the degradation process across the whole lifespan. Subsequently, the multi-stage model is explicitly integrated into the proposed adaptive optimization framework in a computationally tractable way, thus having important practical implications in the field. The paper provides case studies to demonstrate the effectiveness of the proposed modeling method on a selected cycle aging model in reducing the operation cost of FCS with BESS operating in different stages. As a result, the overall operation cost with the multi-stage model is around 2.1
$\%$
on average lower than the single-stage model counterpart. In addition, results show that with the increasing number of divided stages, the model error decreases and becomes stable, while the reduced operation cost compared with the single-stage model increases and saturates. Finally, we apply the multi-stage framework considering other conventional degradation models to show the superiority of the proposed method.