Real Time Determination of Rechargeable Batteries’ Type and the State of Charge via Cascade Correlation Neural Network

• Main Authors: Raif Bayir, Emel Soylu
• Format: Article
• Language: English
• Published: Kaunas University of Technology 2018-02-01
• Series: Elektronika ir Elektrotechnika
• Subjects: artificial neural network; battery monitoring software; rechargeable batteries; state of charge determination.
• Online Access: http://eejournal.ktu.lt/index.php/elt/article/view/20150

Batteries are used to store electrical energy as chemical energy. They have a wide using area from portable equipment to electric vehicles. It is important to know the state of charge of a battery to use it efficiently. In this study, a graphical user interface is developed using a visual programming language to monitor the electrical situations of batteries. Cascade neural network, which is one of the most chosen artificial neural networks, is used to determine the type and state of charge of batteries. The software is able to identify type and state of charge of batteries online. Lead acid, Lithium Ion, Lithium polymer, Nickel Cadmium, Nickel Metal Hydride rechargeable batteries are used in experiments. The experimental results indicate that accurate estimation results can be obtained by the proposed method. DOI: http://dx.doi.org/10.5755/j01.eie.24.1.20150