Integrated model construction for state of charge estimation in electric vehicle lithium batteries
Abstract This research addresses the issue of State of Charge (SOC) prediction for electric vehicle batteries by employing a dynamic Kalman neural network model. The model is optimized using a Genetic algorithm to adjust the neural network weights. Additionally, a strategy involving support vector m...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2024-03-01
|
| Series: | Energy Informatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s42162-024-00322-6 |
| _version_ | 1797259048447377408 |
|---|---|
| author | Yuanyuan Liu Wenxin Dun |
| author_facet | Yuanyuan Liu Wenxin Dun |
| author_sort | Yuanyuan Liu |
| collection | DOAJ |
| description | Abstract This research addresses the issue of State of Charge (SOC) prediction for electric vehicle batteries by employing a dynamic Kalman neural network model. The model is optimized using a Genetic algorithm to adjust the neural network weights. Additionally, a strategy involving support vector machines for model optimization is proposed. This strategy involves preprocessing the data, selecting appropriate kernel functions for training, and merging prediction results to enhance the stability of the model. Results indicated that the Dynamic Genetic Kalman Neural Network (DGKNN) model achieved the minimum prediction error percentage of only 0.1529% when the correction coefficient was set to 0.7. The DGKNN model consistently exhibited the lowest error percentage, average absolute error, mean square error, and root mean square error when handling small, medium, and large datasets. For instance, in the small dataset, the error percentage was only 0.1518, and the root mean square error was only 0.0604. The research findings demonstrated that the proposed model exhibited high real-time accuracy in predicting battery SOC, enabling real-time monitoring of battery operating parameters. The method proposed in this study can accurately predict the state of battery charge, extend the life of battery packs, and improve the performance of electric vehicles. It has important significance for promoting the development of the electric vehicle industry. |
| first_indexed | 2024-04-24T23:03:14Z |
| format | Article |
| id | doaj.art-ffd0d584fb804c9988ff175b844ccc4d |
| institution | Directory Open Access Journal |
| issn | 2520-8942 |
| language | English |
| last_indexed | 2024-04-24T23:03:14Z |
| publishDate | 2024-03-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Energy Informatics |
| spelling | doaj.art-ffd0d584fb804c9988ff175b844ccc4d2024-03-17T12:40:01ZengSpringerOpenEnergy Informatics2520-89422024-03-017111810.1186/s42162-024-00322-6Integrated model construction for state of charge estimation in electric vehicle lithium batteriesYuanyuan Liu0Wenxin Dun1Department of Mechanical and Electrical Engineering and Intelligent Manufacturing, Zhengzhou Vocational College of Information and Technology, Henan Open UniversityXianghe Kunlun New Energy Materials Co., LtdAbstract This research addresses the issue of State of Charge (SOC) prediction for electric vehicle batteries by employing a dynamic Kalman neural network model. The model is optimized using a Genetic algorithm to adjust the neural network weights. Additionally, a strategy involving support vector machines for model optimization is proposed. This strategy involves preprocessing the data, selecting appropriate kernel functions for training, and merging prediction results to enhance the stability of the model. Results indicated that the Dynamic Genetic Kalman Neural Network (DGKNN) model achieved the minimum prediction error percentage of only 0.1529% when the correction coefficient was set to 0.7. The DGKNN model consistently exhibited the lowest error percentage, average absolute error, mean square error, and root mean square error when handling small, medium, and large datasets. For instance, in the small dataset, the error percentage was only 0.1518, and the root mean square error was only 0.0604. The research findings demonstrated that the proposed model exhibited high real-time accuracy in predicting battery SOC, enabling real-time monitoring of battery operating parameters. The method proposed in this study can accurately predict the state of battery charge, extend the life of battery packs, and improve the performance of electric vehicles. It has important significance for promoting the development of the electric vehicle industry.https://doi.org/10.1186/s42162-024-00322-6Electric vehiclesLithium batteriesSOCIntegrated model |
| spellingShingle | Yuanyuan Liu Wenxin Dun Integrated model construction for state of charge estimation in electric vehicle lithium batteries Energy Informatics Electric vehicles Lithium batteries SOC Integrated model |
| title | Integrated model construction for state of charge estimation in electric vehicle lithium batteries |
| title_full | Integrated model construction for state of charge estimation in electric vehicle lithium batteries |
| title_fullStr | Integrated model construction for state of charge estimation in electric vehicle lithium batteries |
| title_full_unstemmed | Integrated model construction for state of charge estimation in electric vehicle lithium batteries |
| title_short | Integrated model construction for state of charge estimation in electric vehicle lithium batteries |
| title_sort | integrated model construction for state of charge estimation in electric vehicle lithium batteries |
| topic | Electric vehicles Lithium batteries SOC Integrated model |
| url | https://doi.org/10.1186/s42162-024-00322-6 |
| work_keys_str_mv | AT yuanyuanliu integratedmodelconstructionforstateofchargeestimationinelectricvehiclelithiumbatteries AT wenxindun integratedmodelconstructionforstateofchargeestimationinelectricvehiclelithiumbatteries |