Open Access
Issue
E3S Web Conf.
Volume 235, 2021
2020 International Conference on New Energy Technology and Industrial Development (NETID 2020)
Article Number 01035
Number of page(s) 6
Section Research on New Energy Technology and Energy Consumption Development
DOI https://doi.org/10.1051/e3sconf/202123501035
Published online 03 February 2021
  1. “2018 China New Energy Vehicle Industry Consumer Survey Report.” [Google Scholar]
  2. “2019 China New Energy Vehicle Industry Consumer Survey Report.” [Google Scholar]
  3. J.L Yibing, L Binbin, H Ning, G zhichao, M Yuchen. “Estimation for SOC of batteries for EVs and range showing and alerting”. vol 54(02) Chinese Labat Man, 2017, pp. 65–69+93. [Google Scholar]
  4. D.C Qiujin. “Research on SOC Estimation and Range of Electric Vehicle Based on Simulink”. Chang’an University, 2018. [Google Scholar]
  5. J Jiuchun, R Haijun, S Bingxiang, W Leyi, G Wenzhong, Z Weige, “A low-temperature internal heating strategy without lifetime reduction for largesize automotive lithium-ion battery pack” Applied Energy, 2018. pp 257-266,. [Google Scholar]
  6. W Jian, L Tong, Z Hao, L Yanxiang, Z Guangquan, “Research on Modeling and SOC Estimation of Lithium Iron Phosphate Battery at Low Temperature, Energy” Procedia, vol 152, pp556-561, 2018. [Google Scholar]
  7. M Jinhao, R Mattia Anirudh, A Budna, L Guangzhao, Maciej Swierczynski, S Daniel-Ioan, Remus “Teodorescu, Low-complexity online estimation for LiFePO4 battery state of charge in electric vehicles” Journal of Power Sources, vol 395, 2018, pp280-288. [Google Scholar]
  8. “EV-TEST Electric Vehicle Evaluation Rules (2019)” [Google Scholar]
  9. J JunPing. Statistics, Beijing: Tsinghua University Press, 2004, 298-299. [Google Scholar]
  10. X Guodong, C Lei, B Guanglong, “A review on battery thermal management in electric vehicle application”, Journal of Power Sources, vol 367, 2017, pp 90-105. [Google Scholar]
  11. C Fleischer, W Waag, Z Bai, D Uwe Sauer, “On-line self-learning time forward voltage prognosis for lithium-ion batteries using adaptive neuro-fuzzy inference system”, Journal of Power Sources, vol 243, 2013, pp 728-749. [Google Scholar]
  12. C Sbarufatti, M Corbetta, M Giglio, F Cadini, “Adaptive prognosis of lithium-ion batteries based on the combination of particle filters and radial basis function neural networks”, Journal of Power Sources, vol 344, 2017, pp 128-140. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.