Real-Time State of Charge Estimation of Lithium-Ion Batteries Using Optimized Random Forest Regression Algorithm
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...
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my.uniten.dspace-347322024-10-14T11:22:09Z Real-Time State of Charge Estimation of Lithium-Ion Batteries Using Optimized Random Forest Regression Algorithm Hossain Lipu M.S. Hannan M.A. Hussain A. Ansari S. Rahman S.A. Saad M.H.M. Muttaqi K.M. 58562396100 7103014445 57208481391 57218906707 36609854400 7202075525 55582332500 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 Differential search algorithm 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. Nevertheless, SOC is influenced by numerous factors which cannot be measured directly. RFR is suitable for real-time 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 the 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 achieves RMSE of 0.382% in the HPPC test using LiNMC battery. Besides, the proposed method obtains satisfactory outcomes in EV drive cycles, estimating MAE of 0.193% and 0.346% in DST and FUDS cycles, respectively, at 25�C. � 2016 IEEE. Final 2024-10-14T03:22:09Z 2024-10-14T03:22:09Z 2023 Article 10.1109/TIV.2022.3161301 2-s2.0-85127075648 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127075648&doi=10.1109%2fTIV.2022.3161301&partnerID=40&md5=42188789a0477d5ce9547e15602fcf79 https://irepository.uniten.edu.my/handle/123456789/34732 8 1 639 648 Institute of Electrical and Electronics Engineers Inc. Scopus |
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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 Differential search algorithm Prediction algorithms Random forest regression Random forests Regression algorithms Search Algorithms States of charges Lithium-ion batteries |
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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 Differential search algorithm Prediction algorithms Random forest regression Random forests Regression algorithms Search Algorithms States of charges Lithium-ion batteries Hossain Lipu M.S. Hannan M.A. Hussain A. Ansari S. Rahman S.A. Saad M.H.M. Muttaqi K.M. Real-Time State of Charge Estimation of Lithium-Ion Batteries Using Optimized Random Forest Regression Algorithm |
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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 real-time 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 the 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 achieves RMSE of 0.382% in the HPPC test using LiNMC battery. Besides, the proposed method obtains satisfactory outcomes in EV drive cycles, estimating MAE of 0.193% and 0.346% in DST and FUDS cycles, respectively, at 25�C. � 2016 IEEE. |
| author2 |
58562396100 |
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58562396100 Hossain Lipu M.S. Hannan M.A. Hussain A. Ansari S. Rahman S.A. Saad M.H.M. Muttaqi K.M. |
| format |
Article |
| author |
Hossain Lipu M.S. Hannan M.A. Hussain A. Ansari S. Rahman S.A. Saad M.H.M. Muttaqi K.M. |
| author_sort |
Hossain Lipu M.S. |
| title |
Real-Time State of Charge Estimation of Lithium-Ion Batteries Using Optimized Random Forest Regression Algorithm |
| title_short |
Real-Time State of Charge Estimation of Lithium-Ion Batteries Using Optimized Random Forest Regression Algorithm |
| title_full |
Real-Time State of Charge Estimation of Lithium-Ion Batteries Using Optimized Random Forest Regression Algorithm |
| title_fullStr |
Real-Time State of Charge Estimation of Lithium-Ion Batteries Using Optimized Random Forest Regression Algorithm |
| title_full_unstemmed |
Real-Time State of Charge Estimation of Lithium-Ion Batteries Using Optimized Random Forest Regression Algorithm |
| title_sort |
real-time state of charge estimation of lithium-ion batteries using optimized random forest regression algorithm |
| publisher |
Institute of Electrical and Electronics Engineers Inc. |
| publishDate |
2024 |
| _version_ |
1814061068790530048 |
| score |
13.222552 |