Novel Scheduling Methodology for Battery Wear Function Considering DoD-SoC Level

Abstract

Energy storage-based applications including Vehicle-to-grid (V2G) service are highly dependent on an accurate battery degradation model. An appropriate wear model contributes to reducing the capacity loss for energy storage scheduling. In this work, the proposed model fully adopts battery wear according to depth of discharge (DoD) for each state-of-charge (SoC) level as a multi-objective function. This model is formulated with mixed-integer linear programming to derive an optimal solution without sacrificing other objectives. In addition, this model maintains low complexity without being affected by the number of EVs by clustering technique-based EV scheduling. The proposed methodology was verified through a case study that battery degradation was considered as a multi-objective function. In addition, it was possible to reduce the battery capacity decrease by more than 30% in a simulation for one month.