Maintaining stable operation of battery swapping stations under time-varying demand is an important issue in Advanced Air Mobility (AAM) operations. This study proposes a predictive control decision model that uses short-term demand information to proactively adjust swapping channels, charging resources, and available battery levels. Three operational strategies were compared: Fixed rule (S1), State response (S2), and Predictive control (S3). The system was modeled as a multi-resource service system with time-dependent aircraft arrivals, and each strategy was evaluated through 100 simulation replications under identical demand and facility conditions. The results showed that S3 reduced the average waiting time to 1.04 min, the waiting probability to 27.31%, and the battery shortage rate to 12.38%, corresponding to reductions of 98.32%, 64.39%, and 73.37%, respectively, compared with S1. In addition, S3 maintained fewer average active swapping channels and chargers than S2 while performing more frequent resource adjustments. These results demonstrate that short-term demand information can support proactive resource allocation and improve service performance and battery supply stability without simply increasing resource deployment.

Abstract
1. 서 론
2. 시스템 구성 및 자율 운영 모델
    2.1 배터리 교환 스테이션의 시스템 구성
    2.2 배터리 상태 변화 및 운영 제약
    2.3 단기 수요 예측 정보 기반 자율 운영 구조
3. 시뮬레이션 설계 및 분석 방법
    3.1 수요 및 기본 운영 조건
    3.2 비교 운영 전략 및 자원 조정 규칙
    3.3 반복 시뮬레이션 및 분석 방법
4. 분석 결과 및 고찰
    4.1 운영 전략별 주요 성능 비교
    4.2 운영 안정성 분석
    4.3 운영 자원 조정 특성 분석
5. 결 론
    5.1. 고정 운영 방식의 성능 한계
    5.2. 자원 조정에 따른 성능 개선
    5.3. 예측 기반 자율 운영의 자원 조정 특성
    5.4. 모델의 적용 범위 및 확장성
후 기
References

• 이우람(Division of Unmanned Aerial Vehicles, Kyungwoon University, Professor) | Wooram Lee Corresponding author