鋰離子電池之健康狀態(SOH)在預測電動車或工業機台的安全性與可靠性時十分重要。然而實務上，要停機獲取充放電數據是不實際的，但在極端溫度與跨設備的情況下又會使得傳統的數據驅動模型之預測因領域偏移而造成預測準確率大幅下降。此外在實務場域中難以取得足夠的標籤數據，要如何運用少量的早期數據來預測電池壽命後期之SOH變化也無可避免地成了實際運用上的難題。為了克服上述困難，本研究使用一套基於CNN+LSTM與AdaBN之無監督遷移學習架構。本論文分為四部分：(1)實驗數據與特徵前處理:利用本實驗室自行收集之不同溫度(25°C、5°C與45°C)下電池充放電數據與NASA開源電池數據集，並使用降採樣與正規化技術來對齊原始的高頻特徵；(2)基準預訓練模型建立:建立CNN+LSTM之基準預訓練模型，以便模型能同時學習單次循環內的充放電特徵和不同循環之間的容量衰退趨勢；(3)AdaBN無監督遷移學習:在完全無須目標域標籤且凍結核心網路權重的情況下，導入AdaBN演算法僅更新批次常態化層(BN層)中的平均值與變異數，將極端溫度或跨數據集之特徵分佈重新對齊至基準預訓練模型熟悉的特徵範圍之中；(4)預測結果與量化驗證:使用遷移指標(TI)等量化評估標準，對比從頭訓練之模型、直接預測模型與經AdaBN遷移後之預訓練模型之間的表現差異。實驗結果證實，25°C基準模型直接預測5°C #8(Ch4)與NASA(B0006)數據時，因嚴重領域偏移導致均方根誤差(RMSE)高達15.64%與11.68%。作為本研究之主要貢獻，所導入之跨溫度無監督AdaBN架構在「完全無需極端溫度標籤」且「免反向傳播」的低運算條件下，將上述RMSE收斂至5.58%與5.01%，並敏銳追蹤極端容量變化與高頻震盪。在多數高變異測試條件下，遷移模型TI值皆顯著小於1（5°C與45°C平均TI為0.577與0.679，NASA B0006更低至0.205）。本研究充分驗證了所使用之AdaBN架構，在面臨跨溫度及跨數據集時具備從頭訓練之模型無可比擬的預測能力，為邊緣運算設備上之即時自適應預估提供了一個高效的解決方案。;Accurate estimation of the State of Health (SOH) of lithium-ion batteries is crucial for ensuring the safety and reliability of electric vehicles and industrial machinery. However， in practical applications， halting operations to acquire charge-discharge data is unfeasible， and extreme temperatures along with cross-device operations often induce severe domain shifts， leading to a significant degradation in the prediction accuracy of traditional data-driven models. Additionally， acquiring sufficient labeled data in real-world environments is notoriously difficult. Effectively utilizing a limited amount of early-stage data to predict SOH variations in the later stages of battery life remains an inevitable practical challenge. To overcome these challenges， this study adopts an unsupervised transfer learning framework based on a Convolutional Neural Network combined with a Long Short-Term Memory network (CNN-LSTM) and Adaptive Batch Normalization (AdaBN). This thesis is structured into four main components: (1) Experimental Data and Feature Preprocessing: Raw high-frequency features from battery charge-discharge data， collected in-house at varying temperatures (25°C， 5°C， and 45°C) and from the open-source NASA battery dataset， are aligned using downsampling and normalization techniques. (2) Baseline Pre-trained Model Establishment: A CNN-LSTM baseline model is pre-trained to simultaneously learn intra-cycle charge-discharge characteristics and inter-cycle capacity degradation trends. (3) AdaBN Unsupervised Transfer Learning: Without requiring target domain labels and while freezing the core network weights， the AdaBN algorithm is introduced to exclusively update the mean and variance within the Batch Normalization (BN) layers， realigning feature distributions skewed by extreme temperatures or cross-dataset variations back into the baseline model′s established feature space. (4) Prediction Results and Quantitative Validation: The performance disparities among models trained from scratch， models using direct prediction， and the pre-trained model enhanced by AdaBN transfer are compared using quantitative evaluation metrics such as the Transfer Index (TI). Experimental results verify that direct prediction of 5°C Ch4 (battery #8) and NASA (B0006) data using the 25°C baseline model yields high Root Mean Square Errors (RMSE) of 15.64 % and 11.68%， respectively， due to severe domain shifts. As the primary contribution of this study， the introduced cross-temperature unsupervised AdaBN framework operating under low-computational conditions requiring "absolutely no extreme-temperature labels" and "no backpropagation” successfully converges the aforementioned RMSEs to 5.58% and 5.01%， while acutely tracking extreme capacity variations and high-frequency fluctuations. Under most high-variance testing conditions， the TI values of the transferred model are significantly less than 1 (averaging 0.577 and 0.679 for 5°C and 45°C， respectively， and reaching as low as 0.205 for NASA B0006). This study comprehensively validates that the adopted AdaBN framework demonstrates predictive capabilities far superior to models trained from scratch when confronted with cross-temperature and cross-dataset scenarios， offering a highly efficient solution for real-time adaptive estimation on edge computing devices.
