Battery monitoring system using machine learning

Battery life prediction helps in smooth and uniform functioning of the battery-operated systems. Although, the capacity of the battery can be monitored by some devices, they cannot estimate how long the battery can work before a failure occurs. The technique that we have proposed here, estimates the life span of a battery using Long Short Term-Memory (LSTM), an artificial Recurrent Neural Network (RNN) architecture in Machine Learning (ML). The battery life is measured by considering each cell voltage, load voltage, temperature of the battery and charge-discharge cycle. The voltage and temperature of the battery cells are measured by a thermistor and microcontroller. The voltage values fetched by the micro controller are sent to a web server and these values are displayed on a web based mobile phone application. Balance charging of the battery cells and over charge protection is provided by constantly monitoring the battery status. This paper includes explanation on different circuit parts, algorithm used in training the model, Graphical User Interface (GUI) and the test results.

Automated summary

This paper introduces a method using LSTM technology to predict battery life, which can measure the battery life by monitoring battery voltage, load voltage, temperature, and charge-discharge cycles.