電池的健康與電量狀態估測是電池管理系統中很重要的技術之一。本論文主要在研究鋰鈷電池的健康與電量狀態估測，此技術可使用於手機、電動工具、電動車或混合電動車，可提供電池狀態的資訊，避免因為電池電量不足或發生故障，所造成的不方便或致命的意外發生。
為了解鋰鈷電池的健康與電量狀態特性，本研究總共使用60顆鋰鈷電池，進行電池壽命循環實驗。由實驗所得數據，分析鋰鈷電池的健康與電量狀態特性。實驗結果顯示，以每一次測試週期中的充電時間、放電瞬間的電壓降、及放電完休息一段時間的開路電壓為參數，再利用機率神經網路及模糊辨識方法，診斷電池的健康狀態。另外，採用電池目前電壓與前兩次的電壓值、此相關電壓之取樣時間、及當時之放電電流。再利用模糊推論結合模糊辨識與模糊神經網路方法，診斷電池的電量狀態。但有關兩取樣電壓的時間非定值，在高電量狀態及低電量狀態之取樣時間較短，在中電量時的取樣時間為定值。
機率神經網路屬於監督式學習網路適合於分類型應用。模糊辨識是模糊數學的一個重要理論，可應用於辨識。模糊推論系統可依據專家經驗或實際數值資料，推論輸入與輸出的關係，可用於非線性或時變的控制問題。模糊推論之模糊神經網路具有可訓練與學習之能力。本研究利用上敘四種方式，分別估測鋰鈷電池的健康與電量狀態。比較機率神經網路與模糊辨識估測結果，兩個方法皆很好，其最大誤差皆為1.64%。此方法只需使用20顆鋰鈷電池的訓練數據，每一次計算電池的健康狀態的時間不超過15.6 ms。另外電池的電量狀態估測結果，以模糊神經網路的方法比較好，可得到的最大誤差為17.7%。此方法只需使用36顆鋰鈷電池的訓練數據，而一次計算電池的健康狀態的時間不超過148 ms。本研究驗證了鋰鈷電池的健康與電量狀態估測，可使用較少的訓練數據結合本研究所提方法，實現線上即時估測。

The prediction of the state of health (SOH) and state of charge (SOC) of a battery is an important aspect of a battery management system. This research proposed a method to predict the SOH and SOC of Li-Co batteries. This proposed technology can be used in the battery management system of mobile phones, power tools, electric vehicles, or hybrid electric vehicles. It provides useful battery information to avoid the inconvenience or the fatal accidents due to the battery failure or power outage.
For life cycle testing, 60 Li-Co batteries were used to study the characteristics of the SOH and SOC. The charging time, the voltage drop in the very initial discharging, and the open circuit voltage after the discharged battery takes a rest can be used as the SOH patterns for probabilistic neural network (PNN) and fuzzy identification (FI). The voltage at the current sampling time and the previous two sampled voltages, the sampling time, and the present discharging current are used as the SOC patterns. Then, the fuzzy inference system (FIS) combined with FI and the fuzzy neural network (FNN) are used to estimate the SOC of the battery. The sampling time mentioned above will be affected by the current SOC. The sampling time during the normal SOC is constant but the sampling time near the very high SOC and the very low SOC is shorter due to the faster voltage variation.
PNN belongs to the supervising type of learning network and is applicable in classification. FI is an important theory of fuzzy math that can be used for word and fingerprint identification. The FIS can utilize the experiences of experts or the experimental data to infer the corresponding relation between the input and output. It can be used to deal with non-linear and time-varied control problems. The FIS based FNN with the ability of training and learning. The four methods described above were used in this study to predict the SOH and SOC of the battery. The experimental results show that both the PNN and FI estimate SOH very well with using only 20 Li-Co batteries’ testing data. The maximum error of the predicted SOH is 1.64% and the computation time to predict the SOH was less than 15.6 ms. The prediction of SOC using FNN is performed better FIS with the training data taken from 36 Li-Co battery testing. The maximum error is 17.7% and the computation time to predict the SOC is less than 148 ms. The experimental results depict that the SOH and SOC of Li-Co battery can be predicted quite accurate and than can be used for the online prediction

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