Analysis and Visualization of New Energy Vehicle Battery Data

Wenbo Ren, Xinran Bian, Jiayuan Gong, Anqing Chen, Ming Li, Zhuofei Xia and Jingnan Wang
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Wenbo Ren: Institute of Automotive Engineers, Hubei University of Automotive Technology, Shiyan 442002, China
Xinran Bian: Shiyan Industry Technique Academy of Chinese Academy of Engineering, Shiyan 442002, China
Jiayuan Gong: Institute of Automotive Engineers, Hubei University of Automotive Technology, Shiyan 442002, China
Anqing Chen: Institute of Automotive Engineers, Hubei University of Automotive Technology, Shiyan 442002, China
Ming Li: Institute of Automotive Engineers, Hubei University of Automotive Technology, Shiyan 442002, China
Zhuofei Xia: Institute of Automotive Engineers, Hubei University of Automotive Technology, Shiyan 442002, China
Jingnan Wang: Institute of Automotive Engineers, Hubei University of Automotive Technology, Shiyan 442002, China

Future Internet, 2022, vol. 14, issue 8, 1-16

Abstract: 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.

Keywords: data visualization; KNN; SOC; vehicle battery; data analysis (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
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