Analysis and Visualization of New Energy Vehicle Battery Data
In order to safely and efficiently use their power as well as to extend the life of Li-ion batteries, it is important to accurately analyze original battery data and quickly predict SOC. However, today, most of them are analyzed directly for SOC, and the analysis of the original battery data and how...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/14/8/225 |
| _version_ | 1797432138473144320 |
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| author | Wenbo Ren Xinran Bian Jiayuan Gong Anqing Chen Ming Li Zhuofei Xia Jingnan Wang |
| author_facet | Wenbo Ren Xinran Bian Jiayuan Gong Anqing Chen Ming Li Zhuofei Xia Jingnan Wang |
| author_sort | Wenbo Ren |
| collection | DOAJ |
| description | In order to safely and efficiently use their power as well as to extend the life of Li-ion batteries, it is important to accurately analyze original battery data and quickly predict SOC. However, today, most of them are analyzed directly for SOC, and the analysis of the original battery data and how to obtain the factors affecting SOC are still lacking. Based on this, this paper uses the visualization method to preprocess, clean, and parse collected original battery data (hexadecimal), followed by visualization and analysis of the parsed data, and finally the K-Nearest Neighbor (KNN) algorithm is used to predict the SOC. Through experiments, the method can completely analyze the hexadecimal battery data based on the GB/T32960 standard, including three different types of messages: vehicle login, real-time information reporting, and vehicle logout. At the same time, the visualization method is used to intuitively and concisely analyze the factors affecting SOC. Additionally, the KNN algorithm is utilized to identify the K value and P value using dynamic parameters, and the resulting mean square error (MSE) and test score are 0.625 and 0.998, respectively. Through the overall experimental process, this method can well analyze the battery data from the source, visually analyze various factors and predict SOC. |
| first_indexed | 2024-03-09T09:56:56Z |
| format | Article |
| id | doaj.art-07ddc4d03cc842ab95290c8e302a451d |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-03-09T09:56:56Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-07ddc4d03cc842ab95290c8e302a451d2023-12-01T23:42:56ZengMDPI AGFuture Internet1999-59032022-07-0114822510.3390/fi14080225Analysis and Visualization of New Energy Vehicle Battery DataWenbo Ren0Xinran Bian1Jiayuan Gong2Anqing Chen3Ming Li4Zhuofei Xia5Jingnan Wang6Institute of Automotive Engineers, Hubei University of Automotive Technology, Shiyan 442002, ChinaShiyan Industry Technique Academy of Chinese Academy of Engineering, Shiyan 442002, ChinaInstitute of Automotive Engineers, Hubei University of Automotive Technology, Shiyan 442002, ChinaInstitute of Automotive Engineers, Hubei University of Automotive Technology, Shiyan 442002, ChinaInstitute of Automotive Engineers, Hubei University of Automotive Technology, Shiyan 442002, ChinaInstitute of Automotive Engineers, Hubei University of Automotive Technology, Shiyan 442002, ChinaInstitute of Automotive Engineers, Hubei University of Automotive Technology, Shiyan 442002, ChinaIn order to safely and efficiently use their power as well as to extend the life of Li-ion batteries, it is important to accurately analyze original battery data and quickly predict SOC. However, today, most of them are analyzed directly for SOC, and the analysis of the original battery data and how to obtain the factors affecting SOC are still lacking. Based on this, this paper uses the visualization method to preprocess, clean, and parse collected original battery data (hexadecimal), followed by visualization and analysis of the parsed data, and finally the K-Nearest Neighbor (KNN) algorithm is used to predict the SOC. Through experiments, the method can completely analyze the hexadecimal battery data based on the GB/T32960 standard, including three different types of messages: vehicle login, real-time information reporting, and vehicle logout. At the same time, the visualization method is used to intuitively and concisely analyze the factors affecting SOC. Additionally, the KNN algorithm is utilized to identify the K value and P value using dynamic parameters, and the resulting mean square error (MSE) and test score are 0.625 and 0.998, respectively. Through the overall experimental process, this method can well analyze the battery data from the source, visually analyze various factors and predict SOC.https://www.mdpi.com/1999-5903/14/8/225data visualizationKNNSOCvehicle batterydata analysis |
| spellingShingle | Wenbo Ren Xinran Bian Jiayuan Gong Anqing Chen Ming Li Zhuofei Xia Jingnan Wang Analysis and Visualization of New Energy Vehicle Battery Data Future Internet data visualization KNN SOC vehicle battery data analysis |
| title | Analysis and Visualization of New Energy Vehicle Battery Data |
| title_full | Analysis and Visualization of New Energy Vehicle Battery Data |
| title_fullStr | Analysis and Visualization of New Energy Vehicle Battery Data |
| title_full_unstemmed | Analysis and Visualization of New Energy Vehicle Battery Data |
| title_short | Analysis and Visualization of New Energy Vehicle Battery Data |
| title_sort | analysis and visualization of new energy vehicle battery data |
| topic | data visualization KNN SOC vehicle battery data analysis |
| url | https://www.mdpi.com/1999-5903/14/8/225 |
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