Battery recharging prediction using extreme learning machine

• Main Author: Gao, Yan
• Other Authors: Huang Guangbin
• Format: Final Year Project
• Language: English
• Published: 2013
• Subjects: DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
• Online Access: http://hdl.handle.net/10356/54415
• Institution: Nanyang Technological University

ELM (Extreme Learning Machine) is a newly developed algorithm working for SLFNs (single-hidden layer feedforward neural networks). It has better performance especially faster learning speed than other traditional learning methods, such as SVM (support vector machine). ELM can be used in a lot of applications with classification or regression requirements. Li-ion battery is a type of rechargeable battery which is widely used in daily life. It concerns the user when the battery will be out of charge. So it is necessary to inform the user to recharge the battery in advance. This report discusses how ELM can be applied in obtaining the time when the battery voltage drops below some certain voltage, i.e. 3V. Due to limited time, future work may be needed to make the prediction more applicable.