Determination of Battery Life Using State of Health Data and Linear Regression

The lifespan of a battery is a significant factor in the performance and reliability of electronic devices. The state of health (SoH) of a battery is a measure of its ability to store and deliver energy, and it can be used to predict how much longer the battery will last. This study proposes a machine learning method for determining the remaining life of a lead-acid battery based on its SoH using the Coulomb counting method. The charging and discharging parameters were recorded for several cycles, and this data was used to train a machine-learning model. The model was then tested using a different set of data, and the results showed that it was able to accurately predict the remaining life of the batteries based on their SoH. This approach has the potential to be used in a variety of applications, such as managing batteries in electric vehicles and renewable energy systems.