State of Charge Prediction Algorithm of Lithium-Ion Battery Based on PSO-SVR Cross Validation
Lithium-ion battery refers to a complex nonlinear system. Real-time diagnosis and accurate prediction of battery state of charge(SOC) parameters are hotspots and critical issues in battery research. To reduce the dependence of state of charge prediction on battery model accuracy and speed, and achie...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8954689/ |
| _version_ | 1818665312049954816 |
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| author | Ran Li Shihui Xu Sibo Li Yongqin Zhou Kai Zhou Xianzhong Liu Jie Yao |
| author_facet | Ran Li Shihui Xu Sibo Li Yongqin Zhou Kai Zhou Xianzhong Liu Jie Yao |
| author_sort | Ran Li |
| collection | DOAJ |
| description | Lithium-ion battery refers to a complex nonlinear system. Real-time diagnosis and accurate prediction of battery state of charge(SOC) parameters are hotspots and critical issues in battery research. To reduce the dependence of state of charge prediction on battery model accuracy and speed, and achieve real-time online estimation, a SOC prediction model of lithium-ion battery system is developed based on the model of support vector machine (SVM). SVM parameter is optimized using an algorithm of particle swarm optimization, and the performance of prediction model is assessed using cross-validation. The obtained experimental data is simulated, involving the comparison with the support vector machine model, and the prediction simulation of the battery in the state of fault. The results reveal that this model with a better performance than that of the support vector machine exhibits high accuracy and generalization ability. |
| first_indexed | 2024-12-17T05:46:38Z |
| format | Article |
| id | doaj.art-9481977bc6d243cea4e7b432c0328b00 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T05:46:38Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-9481977bc6d243cea4e7b432c0328b002022-12-21T22:01:18ZengIEEEIEEE Access2169-35362020-01-018102341024210.1109/ACCESS.2020.29648528954689State of Charge Prediction Algorithm of Lithium-Ion Battery Based on PSO-SVR Cross ValidationRan Li0https://orcid.org/0000-0002-6504-6179Shihui Xu1https://orcid.org/0000-0001-8207-5601Sibo Li2https://orcid.org/0000-0002-8785-2614Yongqin Zhou3https://orcid.org/0000-0002-2808-1055Kai Zhou4https://orcid.org/0000-0003-1169-6325Xianzhong Liu5https://orcid.org/0000-0001-7520-5420Jie Yao6https://orcid.org/0000-0001-8298-1081Engineering Research Center of Automotive Electronics Drive Control and System Integration, Ministry of Education, Harbin University of Science and Technology, Harbin, ChinaSchool of Electrical and Electronic Engineering, Harbin University of Science and Technology, Harbin, ChinaSchool of Electrical and Electronic Engineering, Harbin University of Science and Technology, Harbin, ChinaEngineering Research Center of Automotive Electronics Drive Control and System Integration, Ministry of Education, Harbin University of Science and Technology, Harbin, ChinaEngineering Research Center of Automotive Electronics Drive Control and System Integration, Ministry of Education, Harbin University of Science and Technology, Harbin, ChinaHarbin Guangyu Battery Company Ltd., Harbin, ChinaSchool of Electrical and Electronic Engineering, Harbin University of Science and Technology, Harbin, ChinaLithium-ion battery refers to a complex nonlinear system. Real-time diagnosis and accurate prediction of battery state of charge(SOC) parameters are hotspots and critical issues in battery research. To reduce the dependence of state of charge prediction on battery model accuracy and speed, and achieve real-time online estimation, a SOC prediction model of lithium-ion battery system is developed based on the model of support vector machine (SVM). SVM parameter is optimized using an algorithm of particle swarm optimization, and the performance of prediction model is assessed using cross-validation. The obtained experimental data is simulated, involving the comparison with the support vector machine model, and the prediction simulation of the battery in the state of fault. The results reveal that this model with a better performance than that of the support vector machine exhibits high accuracy and generalization ability.https://ieeexplore.ieee.org/document/8954689/State predictionparticle swarm optimizationLithium-ion power batterynonlinear data diagnosissupport vector machine |
| spellingShingle | Ran Li Shihui Xu Sibo Li Yongqin Zhou Kai Zhou Xianzhong Liu Jie Yao State of Charge Prediction Algorithm of Lithium-Ion Battery Based on PSO-SVR Cross Validation IEEE Access State prediction particle swarm optimization Lithium-ion power battery nonlinear data diagnosis support vector machine |
| title | State of Charge Prediction Algorithm of Lithium-Ion Battery Based on PSO-SVR Cross Validation |
| title_full | State of Charge Prediction Algorithm of Lithium-Ion Battery Based on PSO-SVR Cross Validation |
| title_fullStr | State of Charge Prediction Algorithm of Lithium-Ion Battery Based on PSO-SVR Cross Validation |
| title_full_unstemmed | State of Charge Prediction Algorithm of Lithium-Ion Battery Based on PSO-SVR Cross Validation |
| title_short | State of Charge Prediction Algorithm of Lithium-Ion Battery Based on PSO-SVR Cross Validation |
| title_sort | state of charge prediction algorithm of lithium ion battery based on pso svr cross validation |
| topic | State prediction particle swarm optimization Lithium-ion power battery nonlinear data diagnosis support vector machine |
| url | https://ieeexplore.ieee.org/document/8954689/ |
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