近年來，隨著環保議題受到關注，電動車成為了全球各國積極發展的方向。其中，鋰離子電池是電動車重要的核心零件之一，具有高能量密度、長壽命、充電時間短等優勢，被廣泛應用於電動車領域。因此，電池管理系統的發展就變得至關重要，如何精準地估測出電池殘餘容量與電池健康狀況將直接影響鋰離子電池在電動車載系統上的性能。另一方面，因應近日物聯網為人類社會帶來的便利性以及即時監測的重要性，本文將提出一款具有物聯網功能之電池狀態監測系統，利用ESP32 微控器藉由其I2C傳輸協議與電池管理系統進行資料傳輸，再由Wi-Fi模組與由C# 撰寫之Windows Forms程式透過MQTT傳輸協議同步資料，實現具物聯網功能之電池實時監測系統。
在電池殘餘容量與電池健康狀況的估測中，鋰離子電池模型的準確率是非常重要的，本文針對近期發表與實務中常用之鋰離子電池模型共六種，建立其阻抗頻率響應轉移函數並與實際鋰離子電池阻抗的電化學頻譜圖進行比較。本文以均方根誤差當作其擬合程度的標的，先以粒子群演算法找出與實測電池阻抗擬合度最高的電池模型，再透過其他十二種元啟發式演算法測試，找出最適合進行鋰離子電池模型參數辨識的演算法。本實驗在粒子群演算法粒子數1000顆、迭代次數500次的條件下，得到最佳的鋰離子電池模型為Randle模型。接著以100個代理解，迭代次數上限為100次之條件執行20次，測試其他12種不同的演算法，得到最穩定的三種演算法分別為人工蜂群演算法、布穀鳥演算法以及鬼蝠魟演算法，並使用弗里德曼檢排名中判定人工蜂群演算法為最佳的等效模型參數辨識方法。

In recent years, with the increasing focus on environmental issues, electric vehicles (EVs) have become a key direction for global development. Among them, lithium-ion batteries are crucial components of EVs, offering advantages such as high energy density, long lifespan, and short charging time. As a result, the development of battery management systems (BMS) has become crucial. The accurate estimation of state of charge (SOC) and state of health (SOH) directly impacts the performance of lithium-ion batteries in EV systems. On the other hand, in response to the convenience and importance of real-time monitoring brought by the Internet of Things (IoT), this thesis proposes a battery state monitoring system with IoT capabilities. The ESP32 microcontroller is used for data transmission via the I2C protocol with the BMS, while the Wi-Fi module and a Windows Forms program written in C# utilize the MQTT protocol for synchronized data transmission, achieving a real-time battery monitoring system with IoT functionality.
In the estimation of SOC and SOH, the accuracy of the lithium-ion battery model is crucial. This thesis focuses on six commonly used lithium-ion battery models published recently and in practical applications. Transfer functions of impedance frequency response are established for these models and compared with the electrochemical impedance spectroscopy of actual lithium-ion batteries. The root-mean-square error (RMSE) is used as the criterion for fitting accuracy. The particle swarm optimization (PSO) algorithm is employed to find the battery model with the highest fitting accuracy to the measured battery impedance. Subsequently, twelve other metaheuristic algorithms are tested to identify the most suitable algorithm for lithium-ion battery model parameter identification. In the experiment, under the conditions of 1000 particles and 500 iterations, the Randle model is found to be the best lithium-ion battery model using PSO. Furthermore, by executing 20 runs with 100 particles and a maximum of 100 iterations, the three most stable algorithms are identified as artificial bee colony (ABC), cuckoo search (CS), and manta ray foraging optimization (MRFO). The Friedman test ranks the ABC algorithm as the best equivalent model parameter identification method.

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