Artificial Neural Network based Smart Charging Systems for Lead Acid Batteries Dr. Umasankar P.*, Saravanan K.** *Associate Professor, AVS Engineering College, Salem, India **Assistant Professor, AVS College of Technology, Salem, India Online published on 15 September, 2016. Abstract While there are various choices for a rechargeable battery, lead-acid batteries are still the workhorses. They represent about 60% of all batteries sold worldwide. Lead-acid batteries are exceptionally economical and have a moderate tolerance for abuse. The fact that all batteries used for starting, lighting, and ignition (SLI) service on automobiles and trucks are lead-acid batteries indicates their ability to withstand varied forms of maltreatment and abuses. Lead-acid batteries also provide motive power for everything from forklifts to submarines. Lead-acid batteries are also mainstays of the backup systems that provide power when the electrical mains supply grid fails. Now, development of the sealed lead-acid battery has allowed lead technology to be used in applications such as electronics that need a clean power source. Rechargeable lead-acid batteries have become the most widely used type of battery in the world i.e., more than 20 times the use rate of its nearesrivals. In fact, battery manufacturing is the single largest use for lead in the world. Top Keywords Back propagation Neural Network (BPN), Artificial Neural Network (ANN), battery energy management (BEM), Absorptive Glass material (AGM). Top |