鋰離子電池現今已被大量運用在可攜式電子產品、電動機車、電動汽車及儲能系統中，而不管運用在任何產品上，鋰離子電池的安全性則是首要目標，準確估測電池的健康狀態則是電池管理系統重要的技術之一。而鋰電池老化是一非線性的衰減過程，且鋰電池很容易受到環境溫度、充放電電流大小及電池溫升等因素所影響，若無法在完全的充放電狀態下，很難在即時系統上估測出電池的健康狀態。
因此本文提出一主動式電池健康狀態之估測方法，當電池殘餘容量為100%時，主動偵測鋰電池各頻率交流阻抗，並利用類神經網路演算法估測出電池健康狀態。本文使用Arbin Instruments LBT21084進行電池老化實驗，並搭配Bio-Logic VSP恆電位儀進行交流阻抗量測，以及使用MATLAB提供之類神經網路軟體介面進行訓練。
根據使用不同老化電池進行的測試結果，本文所提出的第一種類神經網路，輸入參數為23個頻率點之交流阻抗，隱藏層神經元20個，所估測的電池健康狀態之最大誤差為2.03%，最小誤差為0%，平均相對誤差為0.60%，平均絕對誤差為0.51%，均方誤差為0.46%。本文所提出的第二種類神經網路，輸入參數為10個頻率點之交流阻抗，採用10個隱藏層神經元，估測結果之之最大誤差為1.31%，最小誤差為0%，平均相對誤差為0.41%，平均絕對誤差為0.35%，均方誤差為0.21%。因此本文所提出之方法，可有效的被用來估算鋰離子電池的健康狀態。

Nowadays, Li-ion battery has been widely used in portable electronics, electric scooter (ES), electric vehicle (EV), and energy storage system (ESS). No matter which product is applied to, the primary goal is the safety of Li-ion battery; accurately estimating the state of health (SOH) is an essential technique in the battery management system (BMS). Furthermore, the Li-ion battery degradation is a non-linear decay process. Li-ion battery is subject to environmental temperature, current of charging and discharging, as well as the battery temperature rise. It is not easy to estimate SOH in real-time systems without a fully charged or discharged state.
Therefore, this study proposed an active SOH estimation method. When the State of Charge (SOC) is 100%, the AC Impedance of the Li-ion battery at each frequency will be detected actively; besides, the neural network algorithm will be utilized to estimate the SOH. This study adopted Arbin Instruments LBT21084 for battery degradation experiments and used Bio-Logic VSP potentiostat for AC impedance measurement. Also, the neural network software interfaces provided by MATLAB were employed for training.
According to the test results using different aging batteries, the first type of neural network proposed in this study, the input parameters are AC impedance at 23 frequency points and 20 hidden layer neurons. The maximum error of the estimated SOH is 2.03%; minimum error is 0%; mean relative error (MRE) is 0.60%; average absolute error (MAE) is 0.51%, and mean square error (MSE) is 0.46%. In this study’s second type of neural network, the input parameters are AC impedance at 10 frequency points and 10 hidden layer neurons. The maximum error of the estimation results is 1.31%; the minimum error is 0%; the mean relative error (MRE) is 0.41%; the average absolute error (MAE) is 0.35%, and the mean square error (MSE) is 0.21%. As a result, the method proposed in this study can be effectively used to estimate the SOH of Li-ion batteries.

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