隨著AI資料中心對高可靠供電需求提升，電池備援系統中鋰離子電池之老化與安全議題日益關鍵。研究針對長期高荷電狀態儲存的日曆老化與削峰填谷(Peak Shaving)操作老化之耦合效應，建立多溫度25°C、35°C與45°C長期實驗平台，並結合電池電流中斷裝置能力驗證，系統性評估容量衰退、內部壓力風險與壽命預測準確性。結果顯示，電池老化仍以日曆老化機制主導，惟導入微循環操作可於中高溫條件下降低後期加速劣化速率，容量保持率提升約1–5%。電流中斷裝置測試進一步量化不同電芯之氣體生成與壓力累積差異，其開閥時間呈現顯著分佈在約50–260小時，可作為安全壽命指標。此外，研究建立之電池健康度運轉時間預測模型經實驗室老化數據與實際不斷電系統客戶端運行數據交叉驗證後，平均運轉時間預測誤差可控制於±6%以內，最佳情況下可達±2%，顯示模型具備良好之工程預測能力。其中，以35°C建立之模型與實際資料中心運行環境最具一致性，可有效反映實際系統老化行為。基於上述結果，本研究提出結合日曆老化、微循環動態負載老化與電流中斷裝置安全指標之整合性老化評估方法，並建立對應之電池健康度(State of Health, SOH)預測觀念。此方法可提升電池備援系統壽命預估準確性與安全設計依據，對高功率密度資料中心之能源管理與預測性維護具有重要應用價值。

With the increasing demand for highly reliable power supply in AI data centers, the aging behavior and safety of lithium-ion batteries employed in Battery Backup Units (BBUs) have become increasingly important. This study investigates the coupled effects of calendar aging under long-term high state-of-charge (SOC) storage and operational aging induced by Peak Shaving. A long-term experimental platform was established at three ambient temperatures (25°C, 35°C, and 45°C). In addition, Current Interrupt Device (CID) activation tests were conducted to systematically evaluate capacity degradation, internal pressure risk, and the accuracy of battery lifetime prediction.
The experimental results indicate that battery degradation is predominantly governed by the calendar aging mechanism. However, the introduction of Peak Shaving micro-cycling mitigates the accelerated degradation observed during the later stages of aging under medium- and high-temperature conditions, resulting in an improvement in capacity retention of approximately 1–5%. Furthermore, the CID activation tests quantitatively revealed differences in gas generation and internal pressure accumulation among different battery cells. The measured CID activation times exhibited a significant distribution ranging from approximately 50 to 260 hours, demonstrating that CID activation behavior can serve as an effective indicator for battery safety and lifetime assessment.
To improve lifetime estimation for practical applications, a State of Health (SOH)-based runtime prediction model was developed and validated through cross-comparison between laboratory aging data and field operational data collected from commercial Uninterruptible Power Supply (UPS) systems. The proposed model achieved an average runtime prediction error within ±6%, with the best-case prediction accuracy reaching ±2%, demonstrating excellent engineering applicability. Among the developed models, the model established at 35°C showed the highest consistency with actual data center operating conditions and most accurately reflected the aging behavior of batteries in practical applications.
Based on these findings, this study proposes an integrated battery aging assessment methodology that combines calendar aging, Peak Shaving micro-cycling degradation, and CID-based safety indicators, together with a corresponding State of Health (SOH)-oriented prediction framework. The proposed methodology improves the accuracy of battery lifetime estimation and provides a more comprehensive basis for battery safety design. Consequently, it offers significant practical value for energy management and predictive maintenance in high-power-density AI data centers.

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