E3S Web Conf.
Volume 235, 20212020 International Conference on New Energy Technology and Industrial Development (NETID 2020)
|Number of page(s)||6|
|Section||Research on New Energy Technology and Energy Consumption Development|
|Published online||03 February 2021|
- “2018 China New Energy Vehicle Industry Consumer Survey Report.” [Google Scholar]
- “2019 China New Energy Vehicle Industry Consumer Survey Report.” [Google Scholar]
- 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]
- D.C Qiujin. “Research on SOC Estimation and Range of Electric Vehicle Based on Simulink”. Chang’an University, 2018. [Google Scholar]
- 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]
- 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]
- 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]
- “EV-TEST Electric Vehicle Evaluation Rules (2019)” [Google Scholar]
- J JunPing. Statistics, Beijing: Tsinghua University Press, 2004, 298-299. [Google Scholar]
- 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]
- 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]
- 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]
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