As an energy storage system, Li-Ion batteries have many applications from mobile devices to vehicles. No matter what application they are used in, Li-Ion batteries lose performance over time, and this negatively affects the user experience in terms of both comfort and safety. For this reason, it is extremely important to estimate state of health (SOH) of Li-Ion batteries and to use the batteries accordingly. In this study, examinations on the SOH estimation of batteries with different machine learning (ML) methods are included using Constant Current (CC) and Constant Voltage (CV) charge-discharge characteristics of the li-Ion batteries. Moreover, how the estimation performance changes by both short-term and long-term features is observed by using mutual information metric. According to results, the highest accuracy on SOH estimation is achieved when long-term features are used with Bayesian Ridge Regression. When the short-term features are used, the accuracy of Bayesian Ridge Regression is dramatically reduced, and Random Forest Regression provides highest performance.
|Erken Görünüm Tarihi||8 Ağustos 2023|
|Yayımlanma Tarihi||1 Eylül 2023|
|Yayımlandığı Sayı||Yıl 2023Cilt: 22|