鋰離子 (Li-ion) 電池是生物醫學、汽車和電子應用的理想能源，因為與市場上的其他電池相比，它具有更小的尺寸、更低的自放電率、更高的能量和功率密度。 許多工業中的應用都使用鋰離子電池，但缺乏維護、惡劣的使用環境和不良的充電操作加速了它們的退化。 因此，即時線上剩餘使用壽命（RUL）預測是一個熱門的研究課題。 本論文整合了兩項使用標準化後的電池容量作為離子電池健康狀態 (SOH) 剩餘壽命預測研究。 使用滑動窗口方法的多步超前預測用於獲得SOH估計。 第一項研究使用六個圓柱形和棱柱形鋰離子 (Li-ion) 電池來評估所提出模型的性能。 使用所提出的即時在線 RUL 預測模型，六種鋰離子電池的相對誤差分別為 0.57%、0.54%、0.56%、0%、1.27% 和 1.41%。 為了評估所提出模型的可靠性，還使用 Monte Carlo dropout 方法提供了 RUL 預測的預測區間。 對於遷移學習方法，總共使用了 6 個電池（3 個作為來源數據集，3 個作為目標數據集）。 通過考慮來源數據集與目標數據集之間差異，使用可轉移性測量來識別來源域和目標域。 四個目標數據集的 RMSE 值分別為 0.0108, 0.0172, 和0.0201。

A lithium-ion (Li-ion) battery is an ideal energy source for biomedical, automotive, and electronics applications because of its smaller size, lower self-discharge rate, higher energy, and power density than other available batteries in the market. Many industrial applications use lithium-ion batteries, but lack of maintenance, harsh use environments, and poor charging operations accelerate their degradation. Therefore, online remaining useful lifetime (RUL) prediction is a hot research topic. This dissertation integrates two studies that use normalized capacity as a state of health (SOH) for RUL prediction of Lithium-ion batteries. Multi-step ahead prediction using a sliding window method is used to obtain the SOH estimates. The first study uses six cylindrical and prismatic lithium-ion (Li-ion) batteries to evaluate the proposed model's performance. Using the proposed online RUL prediction model, the relative errors for the six Li-ion batteries are 0.57%, 0.54%, 0.56%, 0%, 1.27 %, and 1.41 %, respectively. To evaluate the reliability of the proposed model, the prediction interval for the RUL prediction is also provided using the Monte Carlo dropout approach. For the transfer learning approach, a total of six batteries ( three as a source dataset and three as a target dataset) are used. The source domain and target domain are identified using transferability measurement by considering the cell-to-cell difference of the dataset. The RMSE values for the SOH prediction of three target datasets are 0.0108, 0.0172, and 0.0201.

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