本文提出一種多級神經網路模型，專為鋰鐵電池設計，目標為解決此電池電壓平坦區的問題，達到準確估測電池電量狀態的功效。該模型能擷取電池非線性特性，並適用於其他鋰系電池，且能運用遷移學習策略，能迅速預測其他鋰系電池狀態，有效節省時間並提升效能。此外，透過結合微分容量(dQ/dV)曲線與所提出的模型，對鋰三元電池健康狀態進行估測，並比較不同模型的電池電量與健康狀態的估測結果。
其次，本文將數位雙生系統結合雲端-邊緣協作的方式，實現鋰系電池的網路化管理和服務，所提的四層網路化架構可突破傳統電池管理系統在計算和儲存方面的限制，以實現更高效的電池管理；同時，完成一個以雙向電能轉換器為基礎的電池數據收集系統之設計，該系統除了能夠收集電池數據外，還能進行特定或動態電流型態的抽載測試，以收集特定或動態的電流抽載數據。這些收集的數據可用於更新和優化所提的數位雙生系統，進一步提高了電池管理的效率和準確性。

This paper proposes a multi-stage neural network model specifically designed for lithium iron batteries with the aim of resolving the nominal voltage zone issue, thus accurately estimating the battery’s state of charge. This model can capture the nonlinear characteristics of batteries and also applicable to other lithium-based batteries. The adoption of transfer learning strategies allows for rapid prediction of the status of other lithium-based batteries, reduce training time and improving efficiency. In addition, by combining the dQ/dV curve with the proposed model, the health status of lithium ternary batteries is estimated, with comparative analyses performed between different models on battery capacity and health status estimation.
Additionally, this paper integrates digital twin system and cloud-edge collaboration to realize networked management and service of lithium-ion batteries. The four-layer network architecture proposed breaks the computational and storage limitations of traditional Battery Management Systems (BMS), achieving more efficient battery management. Simultaneously, a battery data collection system based on a bidirectional power converter is designed. This system not only collects battery data but also carries out specific or dynamic current draw tests to gather particular or dynamic current draw data. The collected data are utilized to update and optimize our digital twin system, further enhancing the efficiency and accuracy of battery management.

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