由於石化能源枯竭及溫室效應議題日益嚴重，綠色能源勢必要取代傳
統石化燃料。在日常生活使用之大眾運輸車輛，受限於內燃機引擎能源轉換 效率，無法降低排放之溫室氣體，將規劃逐年減產。而以高能密鋰離子電池 為儲能設備核心之電動車，因符合環保節能需求，且大幅提升能源使用效 率，促成電動車產業技術快速發展，成為今日的新興綠能產業。電動車為準 確預測行車可用里程，必須依靠電池模型預測剩餘電量，更需搭配線上檢測 功能評估電池老化、環境溫度及充放電瞬時峰值等狀態的變化，再以優化控 制技術，克服電池系統意外故障之隱憂。
本論文採用半經驗推導之等效電路為電池模型，藉由商業軟體 MATLAB/SimulinkR進行參數建模分析，以開路電壓源、內部串聯電阻及 3 組電阻/電容並聯網絡之電路元件作為模型基本架構，配合動態脈衝放電實 驗數據進行模型參數擬合，過程以非線性最小平方法進行參數優化。在實務 應用端，使用參數找查表模型紀錄各電池芯之荷電狀態及參數變化，以預測 電池芯之完整放電行為，最後達成模型動及靜態預測均方根誤差分別為 3.48 mV(0.096%)及 41.78 mV(1.151%)，經評估為可實際應用之成果，期望 本論文研究成果能提供電動車產業電池系統建模之重要參考根據。

Due to the depletion of fossil energy and the concerns on global warming, green energy is bound to replace traditional fossil fuels. Production of public transportation vehicles used in daily life are constrained by fuel efficiency of ICE has been yearly scheduled in less volume due to the inability in reduction of greenhouse gas emissions. Electric Vehicles relying energy storage with high energy-density Li-ion batteries can fulfill the requirements of environmental protection and energy conservation together with highly improvement in energy efficiency; hence, lead to the rapid technology development in the electric vehicle industry and bloom into highlight of green energy industry. To accurately predict the available mileage of EVs, battery models are essential for the prediction of remaining capacity. Moreover, they are prominent in forecasting on-line the battery cell aging, ambient temperature and charging/discharging power peaks so that unexpected failures of battery system are prevented via optimal control techniques.
In this thesis, a semi-empirical equivalent circuit is employed for the battery model, with the commercial software MATLAB/SimulinkR being adopted for the parametric modeling. An open-circuit voltage source, internal series resistance, and three paralleled RC networks are chosen for the model structure. For practical application, a parametric look-up table model is generated for prediction of the overall discharging behavior of battery cells via matching the state of charge and parameter variations of each individual battery cell. Finally, both dynamic and static RMSE in predictions are 3.48 mV (0.096%) and 41.78 mV (1.151%), respectively. As a result, this investigation will contribute to source of references for researches in battery modeling in the electric vehicle industry.

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