Deep Learning in the State of Charge Estimation for Li-Ion Batteries of Electric Vehicles: A Review
As one of the critical state parameters of the battery management system, the state of charge (SOC) of lithium batteries can provide an essential reference for battery safety management, charge/discharge control, and the energy management of electric vehicles (EVs). To analyze the application of dee...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/10/10/912 |
| _version_ | 1797471905223016448 |
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| author | Dawei Zhang Chen Zhong Peijuan Xu Yiyang Tian |
| author_facet | Dawei Zhang Chen Zhong Peijuan Xu Yiyang Tian |
| author_sort | Dawei Zhang |
| collection | DOAJ |
| description | As one of the critical state parameters of the battery management system, the state of charge (SOC) of lithium batteries can provide an essential reference for battery safety management, charge/discharge control, and the energy management of electric vehicles (EVs). To analyze the application of deep learning in electric vehicles’ power battery SOC estimation, this study reviewed the technical process, common public datasets, and the neural networks used, as well as the structural characteristics and advantages and disadvantages of lithium battery SOC estimation in deep learning methods. First, the specific technical processes of the deep learning method for SOC estimation were analyzed, including data collection, data preprocessing, feature engineering, model training, and model evaluation. Second, the current commonly and publicly used lithium battery dataset was summarized. Then, the input variables, data sets, errors, and advantages and disadvantages of three types of deep learning methods were obtained using the structure of the neural network used for training as the classification criterion; further, the selection of the deep learning structure for SOC estimation was discussed. Finally, the challenges and future development directions of lithium battery SOC estimation using the deep learning method were explained. Over all, this review provides insights into deep learning for EVs’ Li-ion battery SOC estimation in the future. |
| first_indexed | 2024-03-09T19:54:42Z |
| format | Article |
| id | doaj.art-0a49928ae8b44a65afe6a6db313b698c |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-09T19:54:42Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-0a49928ae8b44a65afe6a6db313b698c2023-11-24T00:59:36ZengMDPI AGMachines2075-17022022-10-01101091210.3390/machines10100912Deep Learning in the State of Charge Estimation for Li-Ion Batteries of Electric Vehicles: A ReviewDawei Zhang0Chen Zhong1Peijuan Xu2Yiyang Tian3School of Automobile, Chang’an University, Xi’an 710067, ChinaSchool of Automobile, Chang’an University, Xi’an 710067, ChinaSchool of Transport Engineering, Chang’an University, Xi’an 710067, ChinaSchool of Automobile, Chang’an University, Xi’an 710067, ChinaAs one of the critical state parameters of the battery management system, the state of charge (SOC) of lithium batteries can provide an essential reference for battery safety management, charge/discharge control, and the energy management of electric vehicles (EVs). To analyze the application of deep learning in electric vehicles’ power battery SOC estimation, this study reviewed the technical process, common public datasets, and the neural networks used, as well as the structural characteristics and advantages and disadvantages of lithium battery SOC estimation in deep learning methods. First, the specific technical processes of the deep learning method for SOC estimation were analyzed, including data collection, data preprocessing, feature engineering, model training, and model evaluation. Second, the current commonly and publicly used lithium battery dataset was summarized. Then, the input variables, data sets, errors, and advantages and disadvantages of three types of deep learning methods were obtained using the structure of the neural network used for training as the classification criterion; further, the selection of the deep learning structure for SOC estimation was discussed. Finally, the challenges and future development directions of lithium battery SOC estimation using the deep learning method were explained. Over all, this review provides insights into deep learning for EVs’ Li-ion battery SOC estimation in the future.https://www.mdpi.com/2075-1702/10/10/912electric vehiclesreviewSOC estimationdeep learninglithium-ion battery |
| spellingShingle | Dawei Zhang Chen Zhong Peijuan Xu Yiyang Tian Deep Learning in the State of Charge Estimation for Li-Ion Batteries of Electric Vehicles: A Review Machines electric vehicles review SOC estimation deep learning lithium-ion battery |
| title | Deep Learning in the State of Charge Estimation for Li-Ion Batteries of Electric Vehicles: A Review |
| title_full | Deep Learning in the State of Charge Estimation for Li-Ion Batteries of Electric Vehicles: A Review |
| title_fullStr | Deep Learning in the State of Charge Estimation for Li-Ion Batteries of Electric Vehicles: A Review |
| title_full_unstemmed | Deep Learning in the State of Charge Estimation for Li-Ion Batteries of Electric Vehicles: A Review |
| title_short | Deep Learning in the State of Charge Estimation for Li-Ion Batteries of Electric Vehicles: A Review |
| title_sort | deep learning in the state of charge estimation for li ion batteries of electric vehicles a review |
| topic | electric vehicles review SOC estimation deep learning lithium-ion battery |
| url | https://www.mdpi.com/2075-1702/10/10/912 |
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