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.

[1]  R. Fraser,et al.  Performance Study on the Effect of Coolant Inlet Conditions for a 20 Ah LiFePO4 Prismatic Battery with Commercial Mini Channel Cold Plates , 2022, Electrochem.

[2]  Ronghua Du,et al.  An Adaptive Tracking-Extended Kalman Filter for SOC Estimation of Batteries with Model Uncertainty and Sensor Error , 2022, Energies.

[3]  Baile Xu,et al.  V-SOINN: A topology preserving visualization method for multidimensional data , 2021, Neurocomputing.

[4]  Aggeliki Tsohou,et al.  AppAware: a policy visualization model for mobile applications , 2020, Inf. Comput. Secur..

[5]  Rumana Binte Faruque,et al.  Performance Comparison of Machine Learning Methods with Distinct Features to Estimate Battery SOC , 2019, 2019 IEEE Green Energy and Smart Systems Conference (IGESSC).

[6]  Steven S. Henley,et al.  Consequences of Model Misspecification for Maximum Likelihood Estimation with Missing Data , 2019, Econometrics.

[7]  Dimitris Mavridis,et al.  Dealing with missing outcome data in meta‐analysis , 2019, Research synthesis methods.

[8]  Qiang Wang,et al.  China's dependency on foreign oil will exceed 80% by 2030: Developing a novel NMGM-ARIMA to forecast China's foreign oil dependence from two dimensions , 2018, Energy.

[9]  Gregory L. Plett,et al.  An adaptive physics-based reduced-order model of an aged lithium-ion cell, selected using an interacting multiple-model Kalman filter , 2018, Journal of Energy Storage.

[10]  Baohua Li,et al.  State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles , 2018, Energies.

[11]  Ali Saadon Al-Ogaili,et al.  Design and development of three levels universal electric vehicle charger based on integration of VOC and SPWM techniques , 2017 .

[12]  James Marco,et al.  On the possibility of extending the lifetime of lithium-ion batteries through optimal V2G facilitated by an integrated vehicle and smart-grid system , 2017 .

[13]  Akshay Kumar Rathore,et al.  Industrial Electronics for Electric Transportation: Current State-of-the-Art and Future Challenges , 2015, IEEE Transactions on Industrial Electronics.

[14]  Daniel Sperling,et al.  Regulatory adaptation: Accommodating electric vehicles in a petroleum world , 2012 .

[15]  Atsushi Akisawa,et al.  Market penetration speed and effects on CO2 reduction of electric vehicles and plug-in hybrid electric vehicles in Japan , 2012 .

[16]  VARUN CHANDOLA,et al.  Anomaly detection: A survey , 2009, CSUR.

[17]  T. Johnson,et al.  Exploratory Data Mining and Data Cleaning , 2003 .

[18]  Mohammad Shahidehpour,et al.  Partial Decomposition for Distributed Electric Vehicle Charging Control Considering Electric Power Grid Congestion , 2017, IEEE Transactions on Smart Grid.

[19]  Luo,et al.  Impacts and Utilization of Electric Vehicles Integration Into Power Systems , 2012 .

[20]  Kai Ding,et al.  Battery-Management System (BMS) and SOC Development for Electrical Vehicles , 2011, IEEE Transactions on Vehicular Technology.