Xinran Bian
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Research Subjects
Research Interests
Xinran Bian has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Jiayuan Gong
Hubei University of Automotive Technology
2 shared publications - Wenbo Ren
Institute of Automotive Engineers, Hubei University of Auomotive Technology, Shiyan 442002, China
2 shared publications - Anqing Chen
Institute of Automotive Engineers, Hubei University of Auomotive Technology, Shiyan 442002, China
1 shared publication - Jingnan Wang
Institute of Automotive Engineers, Hubei University of Automotive Technology, Shiyan 442002, China
1 shared publication - Ming Li
Institute of Automotive Engineers, Hubei University of Auomotive Technology, Shiyan 442002, China
1 shared publication - Zhuofei Xia
Institute of Automotive Engineers, Hubei University of Automotive Technology, Shiyan 442002, China
1 shared publication
Latest Publications
- 11 May 2023
- Published by Springer Nature
- p. 1296-1304
Time Series Prediction of New Energy Battery SOC Based on LSTM Network
- 11 May 2023
- Published by Springer Nature
- p. 1296-1304
Time Series Prediction of New Energy Battery SOC Based on LSTM Network
- Open Access
- 26 July 2022
- Published by MDPI AG in Future Internet
- Vol. 14 (8) , 225
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 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.
- 26 July 2022
- Published by MDPI AG in Future Internet
- Vol. 14 (8) , 225
Open AccessAnalysis 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 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.