# Analysis and Visualization of New Energy Vehicle Battery Data
## 1 Introduction  
As the world is moving towards sustainable survival and development, the shortage of oil and increasingly prominent environmental pollution make research on new energy and renewable energy an inevitable trend for the development of all walks of life [1; 2; 3; 4; 5; 6]. Among them, new energy vehicles have gradually become the main development object in the transportation industries of various countries, and the battery components necessary for new energy vehicles have become increasingly perfect with the continuous development of science and technology [7; 8]. At present, lithium-ion batteries with low cost, small volume, and long service life have been put into production through continuous experiments and improvement, and their safety and reliability have been continuously improved [9; 10; 11]. Lithium-ion batteries have been widely used in new energy vehicles, electric bicycles, aerospace, the military, and other fields, especially in the field of electric vehicles [12; 13]. However, the current lithium-ion battery has poor abuse resistance and is vulnerable to the external environment, resulting in safety-related accidents. In order to improve the utilization rate of the battery, prevent overcharge and overdischarge of the battery, prolong the service life of the battery, and monitor the state of the battery, major manufacturers have conducted in-depth research on battery technology, thus the battery management system came into being. The battery management system (BMS) will monitor the battery, includingreal-time monitoring of battery physical parameters, battery state estimation and charging control. However, when relevant faults occur, the battery management system itself cannot analyze the original data generated by the battery. It can only artificially analyze the stored data and the messages in the CAN bus, and it can not find the root cause of the battery faults [14; 15]. In recent years, with the continuous improvement and maturity of battery technology, the battery energy storage system (present battery maximum capacity at a certain condition is called the SOC of the battery) has been used as an important indicator to evaluate the battery state [16]. Since Li-ion batteries are renewable energy sources and intermittent in nature, the interpretation and analysis of SOC is important in the development of effective charging and discharging schemes [17], so the analysis and evaluation of battery energy storage is the top priority in the development of new energy vehicles. A previous paper [18] has conducted a detailed study on some data of new energy batteries, and introduced the cyclic neural network (RNN) to visualize and warn on battery data management; Ref. [19] proposed a method to analyze battery fault diagnosis of electric vehicles based on short-term and long-term memory networks. In reference [20], the author proposed a two-way coupled electrochemical thermal model to study and analyze the effects of water cooling liquid inlet and flow rate on the effectiveness of battery thermal management system. The original battery data and factors impacting SOC have not been explored in the aforementioned literature, despite the fact that a variety of approaches have been suggested to detect battery failure. However, the SOC of the battery is affected by many factors (vehicle state, voltage, temperature, etc.). The existing methods focus on the direct prediction of SOC but ignore the importance of analyzing and visualizing the original data. There is no practical method to analyze the factors affecting SOC. In order to solve the shortage of existing parsing of original battery data, visual analysis, and analysis of factors affecting SOC, this paper is based on parsing the original battery data (hexadecimal) intuitively, visualizing and analyzing each index of the battery on the data, and finally using the indexes affecting SOC to realize the prediction of SOC by the KNN algorithm.