Real-Time State of Charge Estimation of Lithium-Ion Batteries Using Optimized Random Forest Regression Algorithm

• Main Authors: Hossain Lipu M.S., Hannan M.A., Hussain A., Ansari S., Rahman S.A., Saad M.H.M., Muttaqi K.M.
• Other Authors: 58562396100
• Format: Article
• Published: Institute of Electrical and Electronics Engineers Inc. 2024
• Subjects: Differential search algorithm; electric vehicle; lithium-ion battery; random forest regression; state of charge; Battery management systems; Charging (batteries); Data handling; Decision trees; Digital storage; Electric discharges; Electric vehicles; Estimation; Ions; Learning algorithms; Machine learning; Regression analysis; Battery; Prediction algorithms; Random forest regression; Random forests; Regression algorithms; Search Algorithms; States of charges; Lithium-ion batteries

This paper presents an improved machine learning approach for the accurate and robust state of charge (SOC) in electric vehicle (EV) batteries using differential search optimized random forest regression (RFR) algorithm. The precise SOC estimation confirms the safety and reliability of EV. Neverthel...