Battery Health Monitoring System with State Of Charge Estimation Using Artificial Neural Network
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2016, Vol 4, Issue 5
Abstract
Today Battery is an essential component of all critical electrical systems. Battery stores chemical, not electricity. Two different lead in an acid mixture react to produce an electrical pressure. This electrochemical reaction changes chemical energy to electrical energy. Some batteries are sensitive to overcharge and deep discharge, which may lead to permanent damages. Hence all these stationary batteries require routine maintenance to identify and correct problems. A visual inspection can identify physical problems, electrical testing identifies overcharging and undercharging problem. And state of charge test checks the electrolyte strength. This paper presenting a design of a cost effective battery monitoring system which can be easily installed on various type of batteries, captures data regarding the status of battery and sends data to authorized person. Also to estimate state of charge (SOC) accurately a very accurate, robust, stable method such as Artificial Neural Networks (ANN) is used. It will help us to prevent overcharging and over discharging the battery which ultimately results in longer battery service time.
Authors and Affiliations
Ms. Pawar Ashwini Dilip, Mrs. Thorat Rupali. A.
Keywords
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- EP ID EP22103
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