An Efficient Prediction of battery capacity based on temperature and state of charge using impedance

As the world is moving towards the demand for portable devices and electric vehicles, accurate and reliable prediction of battery health has become a crucial aspect of ensuring optimal performance and enriching the battery’s lifespan. A precise technique for predicting from impedance the battery capacity at different temperatures and states of charge is proposed. The obtained results prove that using machine learning techniques is effective for accurately predicting battery health (SOC), as we incorporate relevant input parameters that have a significant impact on battery SOC. This technique can be especially beneficial in situations where knowing the remaining capacity of a battery is crucial, like in electric vehicles or portable electronic devices. Thus, this research serves as a platform for developing robust battery health monitoring systems and optimising battery usage in a wide range of applications.