以電池作為攜帶式電源的電動車和攜帶式設備。此外，在提供穩定的電源系統下，特別是對於含有再生能源的系統，電池扮演儲能設備的主要角色。因此設計一個良好的電池管理系統（Battery Management System,BMS）以保持最佳的電池性能是一項重要的課題。
充放電策略、電量估測(State of Charge, SOC)以及電池電壓平衡器將用來管理八個串聯的鉛酸蓄電池。小型電池儲能系統將用來測試本論文提出的BMS。對於充電策略，將說明二階和多階充電的方法，關於SOC估測，將對於直接開路電壓法（Direct Open Circuit Voltage,Direct OCV）、庫侖法(Coulometric)、開路電壓預測以及類神經網絡（Neural Network,NN）法進行說明與比較，另外，為了改善類神經網絡的性能，提出基於量子神經網絡（Quantum Neural Network, QNN）的SOC估測法。最後，電池電壓平衡器使用定電阻架構來減少各電池之間的電壓差。
實驗結果證明，多階充電相較於二階充電要來的迅速，可以省下58秒的充電時間，類神經網絡預測提供了良好的SOC估測，最大平均誤差在1.03％以下，提出的量子神經網絡法進一步提高了類神經網絡的性能，產生更精確的結果。

Electric vehicles and portable devices use batteries as a portable power source. Moreover, a battery as a storage device plays an important role in providing stable power, especially for operating with renewable energy sources. Therefore, it is very important to design an appropriate battery management system (BMS) for maintaining optimum battery performance.
Charging-discharging strategy, State-of-Charge (SoC) estimation, and battery Voltage Balancer are implemented to manage 8 units of lead-acid batteries in series. A scale-down experimental battery system is tested by the proposed BMS. For charging strategies, both two-step and multi-stage charging methods are described. Regarding the state-of-charge estimation, direct open circuit voltage (OCV), coulometric, OCV prediction, and neural network (NN) estimation methods are delineated and compared. To improve the performance of NN, a new estimation method based on Quantum Neural Network (QNN) is proposed for battery SoC estimation. Finally, battery voltage balancer using fixed-resistor method is employed to reduce the voltage difference between each cell of the battery.
Experimental results show that, multistage charging has faster charging process, which is 58 seconds less charging time compared with two-step charging. The NN estimation provides good SoC estimation with maximum average error no more than 1.03%. The proposed QNN method has further improved the NN performance, and yields more accurate results.

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