IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v14y2022i8p225-d872219.html
   My bibliography  Save this article

Analysis and Visualization of New Energy Vehicle Battery Data

Author

Listed:
  • Wenbo Ren

    (Institute of Automotive Engineers, Hubei University of Automotive Technology, Shiyan 442002, China
    Shiyan Industry Technique Academy of Chinese Academy of Engineering, Shiyan 442002, China
    These authors contributed equally to this work.)

  • Xinran Bian

    (Shiyan Industry Technique Academy of Chinese Academy of Engineering, Shiyan 442002, China
    Information Systems and Decision Support, ISIMA, 63000 Clermont-Ferrand, France
    These authors contributed equally to this work.)

  • Jiayuan Gong

    (Institute of Automotive Engineers, Hubei University of Automotive Technology, Shiyan 442002, China
    Shiyan Industry Technique Academy of Chinese Academy of Engineering, Shiyan 442002, China)

  • Anqing Chen

    (Institute of Automotive Engineers, Hubei University of Automotive Technology, Shiyan 442002, China
    Shiyan Industry Technique Academy of Chinese Academy of Engineering, Shiyan 442002, China)

  • Ming Li

    (Institute of Automotive Engineers, Hubei University of Automotive Technology, Shiyan 442002, China
    Shiyan Industry Technique Academy of Chinese Academy of Engineering, Shiyan 442002, China)

  • Zhuofei Xia

    (Institute of Automotive Engineers, Hubei University of Automotive Technology, Shiyan 442002, China
    Shiyan Industry Technique Academy of Chinese Academy of Engineering, Shiyan 442002, China)

  • Jingnan Wang

    (Institute of Automotive Engineers, Hubei University of Automotive Technology, Shiyan 442002, China
    Shiyan Industry Technique Academy of Chinese Academy of Engineering, Shiyan 442002, China)

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.

Suggested Citation

  • Wenbo Ren & Xinran Bian & Jiayuan Gong & Anqing Chen & Ming Li & Zhuofei Xia & Jingnan Wang, 2022. "Analysis and Visualization of New Energy Vehicle Battery Data," Future Internet, MDPI, vol. 14(8), pages 1-16, July.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:8:p:225-:d:872219
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/14/8/225/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/14/8/225/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Uddin, Kotub & Jackson, Tim & Widanage, Widanalage D. & Chouchelamane, Gael & Jennings, Paul A. & Marco, James, 2017. "On the possibility of extending the lifetime of lithium-ion batteries through optimal V2G facilitated by an integrated vehicle and smart-grid system," Energy, Elsevier, vol. 133(C), pages 710-722.
    2. Richard M. Golden & Steven S. Henley & Halbert White & T. Michael Kashner, 2019. "Consequences of Model Misspecification for Maximum Likelihood Estimation with Missing Data," Econometrics, MDPI, vol. 7(3), pages 1-27, September.
    3. Yabe, Kuniaki & Shinoda, Yukio & Seki, Tomomichi & Tanaka, Hideo & Akisawa, Atsushi, 2012. "Market penetration speed and effects on CO2 reduction of electric vehicles and plug-in hybrid electric vehicles in Japan," Energy Policy, Elsevier, vol. 45(C), pages 529-540.
    4. Deng Ma & Kai Gao & Yutao Mu & Ziqi Wei & Ronghua Du, 2022. "An Adaptive Tracking-Extended Kalman Filter for SOC Estimation of Batteries with Model Uncertainty and Sensor Error," Energies, MDPI, vol. 15(10), pages 1-18, May.
    5. Ruifeng Zhang & Bizhong Xia & Baohua Li & Libo Cao & Yongzhi Lai & Weiwei Zheng & Huawen Wang & Wei Wang, 2018. "State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles," Energies, MDPI, vol. 11(7), pages 1-36, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gnann, Till & Stephens, Thomas S. & Lin, Zhenhong & Plötz, Patrick & Liu, Changzheng & Brokate, Jens, 2018. "What drives the market for plug-in electric vehicles? - A review of international PEV market diffusion models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 158-164.
    2. Zhang, Qi & Mclellan, Benjamin C. & Tezuka, Tetsuo & Ishihara, Keiichi N., 2013. "A methodology for economic and environmental analysis of electric vehicles with different operational conditions," Energy, Elsevier, vol. 61(C), pages 118-127.
    3. Ghorbanzadeh, Milad & Astaneh, Majid & Golzar, Farzin, 2019. "Long-term degradation based analysis for lithium-ion batteries in off-grid wind-battery renewable energy systems," Energy, Elsevier, vol. 166(C), pages 1194-1206.
    4. Hossam M. Hussein & Ahmed Aghmadi & Mahmoud S. Abdelrahman & S M Sajjad Hossain Rafin & Osama Mohammed, 2024. "A review of battery state of charge estimation and management systems: Models and future prospective," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 13(1), January.
    5. Bhandari, Vivek & Sun, Kaiyang & Homans, Frances, 2018. "The profitability of vehicle to grid for system participants - A case study from the Electricity Reliability Council of Texas," Energy, Elsevier, vol. 153(C), pages 278-286.
    6. Park, Sung-Won & Son, Sung-Yong, 2023. "Techno-economic analysis for the electric vehicle battery aging management of charge point operator," Energy, Elsevier, vol. 280(C).
    7. Raslavičius, Laurencas & Azzopardi, Brian & Keršys, Artūras & Starevičius, Martynas & Bazaras, Žilvinas & Makaras, Rolandas, 2015. "Electric vehicles challenges and opportunities: Lithuanian review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 786-800.
    8. Weixing Liu & Hongtao Yi, 2020. "What Affects the Diffusion of New Energy Vehicles Financial Subsidy Policy? Evidence from Chinese Cities," IJERPH, MDPI, vol. 17(3), pages 1-15, January.
    9. Alfredo Alvarez-Diazcomas & Adyr A. Estévez-Bén & Juvenal Rodríguez-Reséndiz & Miguel-Angel Martínez-Prado & Roberto V. Carrillo-Serrano & Suresh Thenozhi, 2020. "A Review of Battery Equalizer Circuits for Electric Vehicle Applications," Energies, MDPI, vol. 13(21), pages 1-29, October.
    10. Bian, Yuan & Yi, Grace Y. & He, Wenqing, 2024. "A unified framework of analyzing missing data and variable selection using regularized likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 194(C).
    11. Sujie Shao & Shaoyong Guo & Xuesong Qiu, 2017. "A Mobile Battery Swapping Service for Electric Vehicles Based on a Battery Swapping Van," Energies, MDPI, vol. 10(10), pages 1-21, October.
    12. Mousavizade, Mirsaeed & Bai, Feifei & Garmabdari, Rasoul & Sanjari, Mohammad & Taghizadeh, Foad & Mahmoudian, Ali & Lu, Junwei, 2023. "Adaptive control of V2Gs in islanded microgrids incorporating EV owner expectations," Applied Energy, Elsevier, vol. 341(C).
    13. Gnann, Till & Plötz, Patrick & Kühn, André & Wietschel, Martin, 2015. "Modelling market diffusion of electric vehicles with real world driving data – German market and policy options," Transportation Research Part A: Policy and Practice, Elsevier, vol. 77(C), pages 95-112.
    14. Theodoros Kalogiannis & Md Sazzad Hosen & Mohsen Akbarzadeh Sokkeh & Shovon Goutam & Joris Jaguemont & Lu Jin & Geng Qiao & Maitane Berecibar & Joeri Van Mierlo, 2019. "Comparative Study on Parameter Identification Methods for Dual-Polarization Lithium-Ion Equivalent Circuit Model," Energies, MDPI, vol. 12(21), pages 1-35, October.
    15. Andre Leippi & Markus Fleschutz & Michael D. Murphy, 2022. "A Review of EV Battery Utilization in Demand Response Considering Battery Degradation in Non-Residential Vehicle-to-Grid Scenarios," Energies, MDPI, vol. 15(9), pages 1-22, April.
    16. Muhammad Umair Ali & Amad Zafar & Sarvar Hussain Nengroo & Sadam Hussain & Muhammad Junaid Alvi & Hee-Je Kim, 2019. "Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation," Energies, MDPI, vol. 12(3), pages 1-33, January.
    17. Iliana Ilieva & Bernt Bremdal, 2021. "Utilizing Local Flexibility Resources to Mitigate Grid Challenges at Electric Vehicle Charging Stations," Energies, MDPI, vol. 14(12), pages 1-15, June.
    18. Maheshwari, A. & Nageswari, S., 2022. "Real-time state of charge estimation for electric vehicle power batteries using optimized filter," Energy, Elsevier, vol. 254(PB).
    19. Yi, Yahui & Xia, Chengyu & Shi, Lei & Meng, Leifeng & Chi, Qifu & Qian, Liqin & Ma, Tiancai & Chen, Siqi, 2024. "Lithium-ion battery expansion mechanism and Gaussian process regression based state of charge estimation with expansion characteristics," Energy, Elsevier, vol. 292(C).
    20. Mathieu, Romain & Baghdadi, Issam & Briat, Olivier & Gyan, Philippe & Vinassa, Jean-Michel, 2017. "D-optimal design of experiments applied to lithium battery for ageing model calibration," Energy, Elsevier, vol. 141(C), pages 2108-2119.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:14:y:2022:i:8:p:225-:d:872219. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.