本文所建之雲端電池狀態診斷系統是基於中央管理單元(樹莓派)建立所提之雙層式可擴充架構，使其能夠擴充並連接各種可以收集電池數據之本地端系統，並依照該系統所上傳之數據，即可以對該電池或電池模組進行電量狀態(State of Charge, SOC)SOC、健康狀態(State of Health, SOH)SOH之估測方法，並將估測之結果顯示於雲端之人機介面當中。其次，本文所提之電池SOC/SOH估測方法，是利用神經網路模型機器學習的方法，以大量之電池測試數據先對神經網路模型進行訓練，因而建立電池參數與電池SOC、SOH之非線性關係，進而推估出電池之SOC、SOH狀態，且神經網路模型具備相當出色之泛化能力，並可以重新對模型進行訓練，對於未知數據之估測能力相當強大，因此，可以透過重新訓練或是增量學習輕易地擴展估測範圍，對於未知的數據也能解決其他因素的影響。再者，本文以雙向電能轉換器的架構提出一電池數據收集系統之架構，目前除了可以利用此數據收集系統進行電池數據之收集，也能透過此系統對待測電池以特定之電流型態進行抽載測試，以收集特定之電流抽載數據。此外，可以利用電池數據收集系統或擴充於本雲端下之數據BMS系統所收集之數據進行電池，或電池模組之電量狀態與健康狀態估測，亦可以由系統之人機介面顯示與進行相關控制。最後，以實際訓練之神經網路模型進行推論與實際值之比較，其實測之結果與預期相符。

This thesis's cloud battery diagnosis system is based on a central management unit (raspberry pi), which establishes a scalable bilayer architecture. The central management unit enables the system to expand and connect to various local systems that can collect battery data. According to the data uploaded by the data collection system, the SOC and SOH of the battery and battery module can be estimated, and show the result on UI in the cloud. Secondly, the proposed SOC/SOH estimation method uses a neural network model, which is trained with a large amount of battery data and establishes the nonlinear relationship between battery parameters and SOC/SOH. Moreover, the generalization capability of the neural networks is excellent, and the neural network model can also be retrained. Therefore, the estimation range can be expanded easily through incremental learning.
Moreover, this thesis proposes a battery collection system with a bidirectional converter. In addition to collecting data, the battery can also be tested with a specific current profile through the system. Finally, the battery data collected by the local system can be used to estimate the SOC/SOH. In this thesis, compare the inference result with the actual value, and the performance is in line with expectations.

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