A BATTERY MANAGEMENT SYSTEM WITH FAST CHARGING STRATEGIES AND STATE OF CHARGE ESTIMATION BASED ON QUANTUM NEURAL NETWORK FOR LEADACID BATTERIES

• Main Authors: , Kevin Gausultan H.M, , Dr. Eng. F. Danang Wijaya, ST., MT.
• Format: Theses and Dissertations NonPeerReviewed
• Published: [Yogyakarta] : Universitas Gadjah Mada 2014
• Subjects: ETD
• Online Access: https://repository.ugm.ac.id/133716/; http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=74503
• Institution: Universitas Gadjah Mada

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.