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.

Maintaining stable operation of battery swapping stations under time-varying demand is a critical issue in Advanced Air Mobility (AAM). This study proposes a stepwise capacity expansion strategy in which pre-secured standby swapping channels are sequentially activated when either queue length or longest waiting time reaches a predefined threshold. The main comparison was conducted between a two-channel fixed-capacity configuration (Fixed-2) and the stepwise expansion configuration (Stepwise), while a three-channel fixed-capacity configuration (Fixed-3) was additionally used as a benchmark. Using common random numbers, 100 simulation replications were performed under identical demand and operating conditions. Compared with Fixed-2, Stepwise reduced average waiting time by 88.48%, waiting probability by 50.80%, and average queue length by 88.76%, while throughput remained nearly unchanged. In the paired comparison, Stepwise showed a lower average waiting time in 99 of 100 replications and the same value in one replication. Sensitivity analysis showed that lower threshold values activated channels earlier and improved waiting performance, but increased the number of active channels. These findings indicate that stepwise activation can effectively mitigate congestion under model-based time-varying demand, although the threshold values should be interpreted as operating reference conditions rather than universal optima.

Advanced Air Mobility (AAM) systems require efficient ground infrastructures to support high-frequency operations, where battery swapping stations play a critical role. In such environments, energy supply stability significantly affects system performance, particularly under time-varying demand. This study analyzes the impact of different energy supply configurations on battery swapping station performance using a simulation-based approach. Three configurations are considered: external power grid, distributed energy resources (DER), and integrated DER with energy storage systems (ESS). Key performance metrics include average waiting time, system staying time, task completion rate, and battery availability. The results show that grid-based systems experience increased delays under peak demand, while DER-only configurations improve some performance indicators, but their effectiveness remains limited under highly time varying demand due to supply variability. In contrast, the integration of DER and ESS achieves the most stable performance by mitigating energy fluctuations. These findings indicate that the temporal adaptability of energy supply is a critical factor in determining operational performance, highlighting the importance of integrating energy storage systems in AAM infrastructure design.

Advanced Air Mobility (AAM) systems rely on high-frequency operations of electric vertical take-off and landing (eVTOL) aircraft, making the performance of ground energy infrastructure a critical factor for overall system efficiency and reliability. In particular, battery swapping stations function as service systems in which multiple aircraft arrivals share limited resources, inevitably leading to processing delays under stochastic demand conditions. Previous studies have primarily focused on optimization-based scheduling and simulation-driven performance evaluation. However, the probabilistic mechanisms governing delay generation and operational stability in battery swapping systems have not been sufficiently explored. This study presents a probabilistic operational analysis of battery swapping stations in AAM using queueing theory. The system is modeled as a multi-server queueing system with stochastic arrival and service processes, and key performance metrics—including system utilization, average waiting time, and delay probability are analytically derived. Furthermore, delay behavior is examined not only in terms of average values but also through probabilistic distributions and service-level-based performance criteria. To validate the proposed analytical framework, MATLAB-based discrete-event simulations are conducted under various operational scenarios, including different arrival rates and service time variability conditions. The results indicate that stochastic characteristics significantly influence delay behavior and system stability, particularly under peak demand conditions. The proposed approach provides a theoretical foundation for understanding delay dynamics in AAM battery swapping operations and offers practical insights for designing stable and efficient ground infrastructure systems.

도핑 엔지니어링은 넓은 밴드갭, 낮은 전하 운반체 농도, 제한된 전하 수송 속도 등 리튬 이온 배터리용 산화물 기반 세라믹 분리막의 고유한 한계를 극복하는 효과적인 전략으로 부상했다. 이종 원자가 도핑은 전자 구조와 결함 화학을 변화시켜 산소 결함 및 결함 상태를 생성함으로써 리튬 이온 수송을 향상시키고 계면 저항을 감소시킨다. 또한, 도핑으로 인 한 격자 안정화는 기계적 강도를 개선하고 덴드라이트 침투를 억제하며 전기화학적 신뢰성을 향상시킨다. 산화물 기반 세라 믹 분리막은 고온에서 심각한 수축 및 용융 현상을 보이는 기존 폴리올레핀 분리막에 비해 우수한 열 안정성을 나타낸다. 기 계적으로 견고한 세라믹 골격은 열 응력 하에서도 구조적 안정성을 유지하고 내부 단락을 방지하여 배터리 안전성을 크게 향 상시킨다. 특히, 전하 운반체 활성화와 구조적 안정성이 균형을 이루는 최적의 도핑 농도 범위가 존재하여 전하 수송 성능과 안전성을 극대화할 수 있다. 종합적으로, 도핑 엔지니어링은 차세대 리튬 이온 배터리용 고성능 및 본질적으로 안전한 세라믹 분리막 개발을 위한 합리적인 전략을 제공한다.

The potential release of toxic metals such as Li, Ni, and Co into aquatic environments is increasing due to the growth of the lithium-ion battery (LIB) industry and the expansion of recycling processes. In this study, the 24-h acute toxicity of Li, Ni, and Co was evaluated in both single-metal exposures and binary mixture using Daphnia magna. Single-metal toxicity showed the highest toxicity for Co, followed by Li and Ni. Mixture toxicity results indicated antagonistic interactions in the Li-Ni and Li-Co combinations, whereas a strong toxicity enhancement was observed for the Ni-Co combination. Nonlinear interaction patterns dependent on fixed concentrations and concentration ratios were also identified. These findings highlight the limitations of simple additivity assumptions and provide fundamental data for mixture-based ecological risk assessment related to LIB recycling activities.

Recently, changes in the electric vehicle transition policy have necessitated improved user acceptance by securing the competitiveness of electric vehicles over internal combustion engine vehicles. In particular, the importance of reliable condition diagnosis technology to prevent safety accidents such as battery pack fires has been receiving significant attention. However, lithium-ion battery packs, primarily used in domestic electric vehicles, require the development of battery pack health diagnosis technology that considers real-world driving characteristics, such as high energy density and irregular and incomplete charge/discharge patterns. This study utilized OBD-II data from 100 real-world electric vehicles to extract health indicators for assessing battery pack aging over time using IC curves. Using IC curves during charging, the most stable environment during real-world driving, key factors associated with battery pack aging were identified. The IC curves confirmed that aging increased with mileage from 30,000 km to 260,000 km, demonstrating the potential for developing integrated aging maps for the same vehicle model. Furthermore, this study is considered a practical tool for immediate condition assessment of electric vehicles without the need for additional equipment.

This study presents an optimization model for battery scheduling in Advanced Air Mobility (AAM) operations considering congested (peak-hour) flight periods. Peak-hour demand concentration causes bottlenecks in vertiport charging/swapping facilities and accelerates battery degradation, reducing operational efficiency. A Mixed-Integer Linear Programming (MILP) model is developed, incorporating battery states (SoC, SoH), charger and swap-bay constraints, and power peak limits. Simulation results under peak and off-peak scenarios show that the proposed model reduces both delay time and total operating cost compared to average-demand scheduling. This study provides a quantitative decision-making basis for enhancing resource efficiency in AAM operations. The findings offer practical implications for improving AAM infrastructure efficiency and resource management policies.

This study presents a standalone diagnostic device for HEV high-voltage battery packs that communicates directly with the BMS outside the vehicle and enables quantitative verification of BMS SOC and SOH outputs. The prototype, developed for a Renault CMA HEV pack, activates the BMS via the low-voltage harness, reads key variables such as SOC, SOH, cell voltage and temperature, and pack voltage and current over CAN, and safely controls the pack’s high-voltage relay. Using a pack reported as 100% SOH by the BMS, constantcurrent discharge at about a 0.1 C-rate was performed in the SOC range from 30% to 45%, and for 5, 10 and 15-minute segments the usable energy estimated from the BMS SOC and the rated capacity showed mean values around 1.54kWh with a coefficient of variation of approximately 2-3%. The proposed BMS-linked evaluation equipment estimates the usable capacity within a tolerance consistent with the manufacturer’s nominal specification and can serve as a practical basis and tool for second-life evaluation of used high voltage battery packs.

Advanced Air Mobility (AAM) operations using electric vertical takeoff and landing (eVTOL) aircraft require reliable ground energy infrastructure to ensure stable service. During congested periods, temporally concentrated flight demand can cause delays and operational instability in battery swapping stations. This study examines the operational impacts of different energy supply configurations under demand variability. Three configurations are considered: an external power grid–only condition, a distributed energy resource (DER)–applied condition, and an integrated DER and energy storage system (ESS) condition. A discrete-time simulation model is used to evaluate operational performance in terms of average waiting time, task completion rate, and battery inventory stability. The results show that the integrated DER and ESS configuration provides the most stable performance during peak-demand periods, highlighting the importance of energy storage–based integrated supply structures for resilient AAM battery swapping infrastructure.

This study was conducted to examine the structural stability of a lightweight structure for a sliding-type battery rack system located under an electric bus. To address the shortcomings of the existing sliding battery rack systems, the battery rack system was designed by applying lightweight materials and utilizing a bolt-mounting connection type. Finite Element Method(FEM)-based structural analysis was performed, considering both the system’s self-weight and the weight of the installed batteries. The analysis identified the maximum stress value and its location within the entire system. Furthermore, considering the different materials used in various components, the maximum stress values for each component were individually derived. By comparing the maximum stress with the yield strength of each material, it was confirmed that the designed lightweight battery rack system had secured structural stability.

국제해사기구(IMO)의 탈탄소 정책과 함께 소형선박 분야에서는 배터리 기반 전기추진의 도입이 확대되고 있다. 그러나 리튬이 온 배터리는 열폭주와 화재, 폭발 등 안전상의 위험을 내포하고 있어 이에 대한 체계적인 대응 방안이 필요하다. 본 연구는 한국, 노르웨 이 및 유럽의 관련 규정을 비교·분석하고, 이를 소형어선의 실제 적용 가능성 측면에서 검토하였다. 규정 분석을 통해 공통적으로 배터리 실에 대한 화재 탐지와 고정식 소화설비의 필요성이 확인되었으며, 소형선박을 대상으로 한 사례 검토에서는 가스계 소화설비는 설치 가 능성이 높은 반면, 수계 소화설비는 공간적·운항적 제약으로 적용이 어려운 것으로 나타났다. 따라서 연안 소형선박에서의 안전한 배터리 운용을 위해서는 해수에 의한 소화를 연구하거나 가스계 소화설비를 통한 대피시간 확보 및 육상 연계 등을 통한 안전 확보가 필요할 것 으로 확인되었다. 본 연구는 소형선박 배터리 안전설비의 현황과 적용성에 대한 기초 자료를 제공하며, 향후 전기추진 소형선박의 보급을 위한 설계 기준 및 안전 가이드라인 마련에 기여할 수 있을 것으로 기대된다.

This study presents the results of compression, drop impact, and vibration durability analyses conducted to evaluate the mechanical reliability of Battery Pack Cases (BPCs) in electric vehicle (EV) systems. BPCs are essential structural components that must endure compressive loads, impact forces, and vibrational fatigue. Finite Element Analysis (FEA) was applied to a representative BPC model to assess deformation, impact resistance, and vibration endurance. The results indicate that the BPC maintained integrity within yield strength limits under compressive loading and effectively absorbed energy under drop impact. Furthermore, Power Spectral Density (PSD) analysis identified stress concentration regions, providing insights for structural optimization. Overall, the findings support the development of lightweight and reliable BPC designs for advanced EV applications.

모빌리티 기기에 주로 사용되는 리튬 이온 배터리는 고장 시 심각한 인명 피해로 이어질 수 있어, 실시간으로 배터리의 건전성 상태 (State of Health, SOH)를 정확하게 추정하고 모니터링하는 것이 매우 중요하다. 본 논문에서는 TCN-GRU 하이브리드 딥러닝 신경망 기반 리튬 이온 배터리의 SOH 추정 방법을 제안한다. 제안한 모델은 모빌리티 온보드 시스템에 적용 가능하도록 배터리 관리 시스템 (BMS)에서 직접 측정 가능한 전류, 전압, 시간의 원시 데이터를 리샘플링하여 입력으로 사용하였다. 해당 입력 데이터는 과거와 현재 의 데이터만을 활용하여 학습하는 TCN 모델을 통해 국소적 용량 회복을 포함한 배터리 열화 과정에서 나타나는 비선형적인 특징을 효과적으로 추출함으로써 모델의 신뢰성을 향상시켰다. 추출된 특징은 GRU 모델에 입력되어 시간적 정보 및 패턴을 학습하며, 정밀 한 SOH 추정 결과를 도출하였다. 제안한 방법은 CALCE 배터리 열화 데이터를 기반으로 검증하였으며, 평가 지표인 MAE와 RMSE 는 모든 배터리 셀에 대해 각각 최대 0.55 및 0.7의 일관되고 우수한 성능을 보였다.