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 presents a multi-stage battery degradation modeling method that accurately captures the degradation process across the entire lifespan. This multi-stage model is explicitly integrated into an adaptive optimization framework in a computationally efficient manner, yielding important practical implications. The paper includes case studies demonstrating the effectiveness of the proposed modeling method on a selected cycle aging model, specifically reducing the operation cost of FCS with BESS at different stages. Results indicate that the overall operation cost with the multi-stage model is approximately 2.9% lower on average than the single-stage counterpart. Furthermore, it is shown that as the number of divided stages increases, model error diminishes and stabilizes while the reduced operation cost relative to the single-stage model increases and eventually saturates. The framework is further applied to other conventional degradation models, illustrating the superiority of the proposed method.