本論文目的為研究與建立具有溫度效應之鋰電池即時動態模型。研究中使用3.2V/11.5Ah之磷酸鋰鐵電池。本研究主要分為三階段；第一階段為熱動態響應量測；透過二步驟量測法，在不同電池操作溫度下，依照電池在恆溫室中絕緣與一般對流，可求得電池單元之比熱與熱對流係數。第二階段為電池等效電路特性量測。主要作法為透過交流阻抗(AC Impedance)技術，在頻率0.1-1000 Hz的範圍下，針對不同溫度( 10、20 、30 、40 )、不同放電電流(0.5C、1C、2C)、不同電池電量(state-of-charge，SOC)，進行等效電路之元件參數量測。第三步驟為建立具溫度動態之高非線性電池即時模型；透過一具有隱藏層(hidden layer)之類神經網路，將電池溫度、SOC作為網路之2變數輸入向量，等效電路參數值(三電阻、一電容)作為網路之4變數輸出向量。經過倒傳遞法(back propagation)之神經元權重(weighting)訓練後，便可求得代表目標鋰電池之即時模擬等效電路。本模型未來可供系統整合與載具車應用。

This thesis aims at studying the real-time dynamic model of a lithium battery with thermal effect. We employed a 3.2V/11.5Ah lithium-iron-phosphate battery for our research. This research is separated into three stages. The first stage is to measure the thermal dynamics. By using a two-step measurement method, the heat capacity and heat transfer coefficient under various temperatures can be derived by tested under adiabatic and normal conditions in an incubator. The second stage is for the characteristics of the equivalent circuit of the cell. By using the AC impedance technique, within the frequency of interest within 0.1-1000 Hz, parameters of the equivalent circuit were measured under various temperatures (10, 20, 30, 40), various discharge currents (0.5C, 1C, 2C) and various battery state-of-charge. The third step is to establish a highly-nonlinear real-time model with thermal dynamics. By adopting a neural network with hidden layers, the battery temperature and SOC were two variables for the input vector, while the values of parameters (2 resistance, 1 inductance and 2 capacitors) formed a 5-variable output vector. The neural-network weights were trained by the back-propagation method. Hence, an electric circuit for real-time simulation of the target lithium battery was derived. This research provides the further applications for battery system integration and the vehicle integrations.

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