本文研製基於人工智慧之可攜式電池診斷平台。所提系統利用同步整流降壓型轉換器架構與Raspberry Pi單板電腦，對待測電池進行定電流與隨機電流的抽載，並藉此擷取待測電池之動態數據。所提電量狀態與健康狀態估測法則，利用機器學習的方法由大量的充放電數據取得電池內部動態特性，從而建立電池電量狀態與測量電壓變量之間的非線性關係，除了不需要特定的數學模型，也不需要花費時間等待電池的電化學反應，並可以輕易地擴展規格及解決其他影響因素。其次，除了可以在定電流抽載的情況下，估測電池初始電量與健康狀態；亦可於不同負載的情況下，即時估測電池之電量。再者，加入人機介面與電池診斷平台進行雙向溝通，除了可利用偵測電路蒐集主電路及待測電池之相關參數，並將資料傳送至人機介面顯示，亦可直接由人機介面對系統進行相關控制。最後，經訓練完成的模型實際進行電池電量狀態及健康狀態的推論，實測結果皆能與預期相符。

This thesis develops a portable battery diagnostic platform based on artificial intelligence. The dynamic characteristics of the tested battery are captured by the constant current and random current loading which is controlled by a synchronous buck converter and a raspberry pi computer. The proposed SOC and SOH estimation algorithm uses machine-learning technique to obtain the internal dynamics characteristics of battery from lots of charge-discharge data to establish the nonlinear relationship between the battery SOC and the differential voltage from measuring. This method requires neither a specific mathematical model and nor waiting time for electrochemical reaction of the battery, also can easily extend to higher specification and resolve other affecting factors. Secondly, in addition to estimating the SOH and initial SOC of tested battery under constant current loading conditions, the SOC of the tested battery also can be estimated in real time under random loading conditions. Furthemore, the human-machine interface(HMI) is added to the portable battery diagnostic platform for bidirectional communication. In addition to using the detection circuit to collect the relevant parameters and data of the main circuit and the tested battery and transmit it to the display of the HMI, the proposed approach can also be directly controlled by the HMI. Finally, the trained model is used to infer the SOC and SOH of the tested battery, the measured results are very close to expectations.

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