Publication: Differential Search Optimized Random Forest Regression Algorithm for State of Charge Estimation in Electric Vehicle Batteries
No Thumbnail Available
Date
2021
Authors
Hossain Lipu M.S.
Hannan M.A.
Hussain A.
Ansari S.
Ayob A.
Saad M.H.M.
Muttaqi K.M.
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers Inc.
Abstract
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. Nevertheless, SOC is influenced by numerous factors which cannot be measured directly. RFR is suitable for SOC estimation due to its robustness to noise, overfitting issues and capacity to work with huge datasets. However, proper selection of RFR architecture and hyper-parameters combination remains a key issue to be explored. Hence, a differential search algorithm (DSA) is employed to search for the optimal values of trees and leaves in RFR algorithm. DSA optimized RFR eliminates the utilization of the filter in data pre-processing steps and does not require a detailed understanding and knowledge about battery chemistry, rather only needs sensors to monitor battery voltage and current. The developed approach is validated at room temperature using two types of lithium-ion batteries under a pulse discharge test. In addition, the proposed model is verified under varying temperature settings under EV drive cycles. The experimental results demonstrate that the DSA optimized RFR algorithm is superior to other optimized machine learning approaches in achieving a lower error rate which illustrates the suitability of the proposed model in the online battery management system. � 2021 IEEE.
Description
Battery management systems; Charging (batteries); Data handling; Decision trees; Digital storage; Electric vehicles; Learning algorithms; Lithium-ion batteries; Machine learning; Differential search algorithm; Electric vehicle batteries; Lithium ions; Lithiumion battery; Random forest regression; Random forests; Regression algorithms; Search Algorithms; State-of-charge estimation; States of charges; Regression analysis