隨著能源高效率應用之問題日益受到關注，電池儲能系統逐漸成為解決能源儲存和調節的重要技術手段之一。而在儲能系統中，電池是其中最為關鍵的組件之一，也是影響儲能系統性能和壽命的重要因素。因此，對電池狀態進行監測與分析，其對於保證儲能系統的正常運轉和延長電池壽命至關重要。
為了實現對電池狀態的監測和分析，本研究以離島儲能系統之電池充放電資料進行監測與蒐集，並分別以電路模型和以機器學習為基礎，各提出了一個可以用於電池電量估測的演算法，依據其優劣評估其是否適用實現於雲端電池管理系統。
在建置平台的方面，本文利用了關聯式資料庫管理系統SQL儲存了所要分析的電池的充放電資料，並藉由PHP和Python的數值方法與圖形程式庫實現了歷史資料輸出、取得電壓-電池電量曲線上的所有值和電池電量線上估測等功能。
本文提出儲能系統電池狀態估測與雲端電池電量分析平台，旨在實現對儲能系統的智慧監控和管理。未來，本平台可以進一步最佳化並完善，加入更多的資料和演算法，以提高其準確性和實用性。同時，將其應用於更多的實際應用場域中，如電動車、家庭儲能系統等，可以進一步擴展其應用範疇。

In recent years, with the increasing attention to high efficiency energy use issues, battery energy storage systems (BESSs) have gradually become one of the important technical means to solve energy storage and regulation problems. Among the components of energy storage systems (ESSs), batteries are one of the most critical and important factors affecting the performance and lifespan of ESSs. Therefore, monitoring and analyzing the battery status is crucial for ensuring the normal operation of ESSs and extending the battery life.
To achieve battery status monitoring and analysis, this study used the battery charging and discharging data collected from an ESS installed at a Taiwan’s outlying island. A circuit model and a machine learning based algorithm were proposed for battery state of charge (SoC) estimation, and their effectiveness for a cloud-based battery management system was evaluated based on their performance.
In terms of online analysis platform construction, this study used an open source relational database PostgreSQL to store all battery charging and discharging data, and implement historical data query, interpolate OCV-SoC curve, and online estimate SoC, and demonstrate analysis results through numerical methods libraries and graphics libraries of PHP and Python programing languages.
This study presented a battery status estimation and web-based battery capacity analysis platform for ESSs, aiming to achieve intelligent monitoring and management of ESSs. In the future, this platform can be further optimized and improved, with more data sources and algorithms added to enhance its accuracy and industrial applicability. Furthermore, it can be applied to more practical scenarios, such as electric vehicles and home ESSs, to further expand its application prospects.

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