Preddictive Residual Energy In Batteries Using Machine Learning

Abstract

The Increasing Reliance On Battery-powered Systems In Renewable Energy And Electric Vehicle Applications Necessitates Accurate Estimation Of Battery Residual Energy For Efficient Power Management. This Project Presents A Smart Battery Monitoring And Prediction System Using Machine Learning, Particularly Linear Regression, To Forecast The Remaining Energy Of A Lithium Iron Battery Pack. The Battery System Consists Of Six Lithium Iron Batteries (3.7V, 2900mAh Each), Grouped Into Three Parallel-connected Pairs, Which Are Further Arranged In Series To Create A Higher Capacity Battery Bank. A Battery Management System (BMS) Is Integrated To Ensure Safety And Manage The Charging And Discharging Operations Effectively. The Core Of This System Lies In Precise Voltage Sensing And Prediction. Three Voltage Measurement Sensors Monitor The Battery Voltage In Real Time And Feed The Data To A PIC Microcontroller. These Voltage Levels Are Displayed On An LCD For User Reference. When The Sensed Voltage Of The Battery System Drops Below 5V, A Relay Is Triggered, And The System Activates A Boost Converter To Step Up The Voltage To A Suitable Level For The Load. This Voltage Regulation Mechanism Ensures Uninterrupted Power Supply To The Load While Maintaining Battery Safety And Prolonging Its Lifespan. To Enhance The Intelligence Of The System, A Machine Learning Algorithm—linear Regression—is Employed To Predict The Residual Energy Of The Batteries Based On Historical Voltage Data And Discharge Rates. By Analyzing The Pattern Of Voltage Decline Over Time, The System Can Estimate Future Battery Levels And Provide Advance Alerts, Aiding In Decision-making For Charging Cycles. This Predictive Functionality Helps In Avoiding Unexpected Power Losses And Supports Proactive Energy Management, Especially In Critical Applications.

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