SOC estimation of a lithium battery under high pulse rate condition based on improved LSTM

被引：0
|
作者：
Ming T. [1 ]
Zhao J. [2 ]
Wang X. [3 ]
Wang K. [1 ]
机构：
[1] College of Electrical Engineering, Qingdao University, Qingdao
[2] Electric Power Research Institute of State Grid Shandong Electric Power Company, Jinan
[3] Hisense Visual Technology Co., Ltd., Qingdao
来源：
Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control | 2021年 / 49卷 / 08期
关键词：
High rate pulsed; Recurrent neural network; State of charge (SOC); Ternary lithium battery;
D O I：
10.19783/j.cnki.pspc.200776
中图分类号：
学科分类号：
摘要：
The lithium-ion battery is an indispensable energy storage component in a power system. To predict the State of Charge (SOC) of a lithium-ion battery under high pulse rate conditions, an improved Long Short-Term Memory (LSTM) neural network is used to build the SOC prediction model of a ternary lithium-ion battery. Two gating units are added to the original LSTM to improve the dynamic approximation ability of the model by enhancing the interaction between input and output. Compared with Back Propagation (BP) and LSTM neural networks, the prediction performance of the algorithm under high pulse rate conditions is proved superior. The results show that the improved method can accurately characterize the operating characteristics of ternary lithium batteries, and meet the actual needs of SOC estimation. © 2021 Power System Protection and Control Press.
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页码：144 / 150
页数：6
相关论文
共 27 条
  • [1] MING Tongtong, WANG Kai, TIAN Dongdong, Et al., State of charge estimation of lithium ion battery based on LSTM neural network, Guangdong Electric Power, 33, 3, pp. 26-33, (2020)
  • [2] CHENG Jun, QU Yan, LI Yuan, Et al., Equalization strategy of battery pack charge and discharge based on residual power estimation, Power System Protection and Control, 48, 3, pp. 122-129, (2020)
  • [3] CUI Hongfen, YANG Bo, JIANG Ye, Et al., Secondary frequency modulation control strategy based on fuzzy control and SOC self recovery energy storage, Power System Protection and Control, 47, 22, pp. 89-97, (2019)
  • [4] ZHENG Y, OUYANG M, HAN X, Et al., Investigating the error sources of the online state of charge estimation methods for lithium-ion batteries in electric vehicles, Journal of Power Sources, 377, pp. 161-188, (2018)
  • [5] LIU Junhua, ZHANG Qichao, LI Cheng, Et al., Study on the rapid assessment method of health of lithium iron phosphate battery module, Power System and Clean Energy, 36, 10, pp. 112-118, (2020)
  • [6] HERATH A, KODITUWAKKU S, DASANAYAKE D, Et al., Comparison of optimization-and rule-based EMS for domestic PV-battery installation with time-varying local SOC limits, Journal of Electrical and Computer Engineering, 2019, pp. 1-14, (2019)
  • [7] HE Ping, QI Pan, SHEN Runjie, Et al., Damping analysis of integrated energy system considering wind power and fuel cell, Journal of Electric Power Science and Technology, 35, 1, pp. 14-23, (2020)
  • [8] GAO Z C, CHIN C S, TOH W D, Et al., State-of-charge estimation and active cell pack balancing design of lithium battery power system for smart electric vehicle, Journal of Advanced Transportation, 2017, pp. 1-14, (2017)
  • [9] CHEN W, LIANG J, YANG Z, Et al., A review of lithium-ion battery for electric vehicle applications and beyond, Energy Procedia, 158, pp. 4363-4368, (2019)
  • [10] WANG Kai, XIA Guoting, LI Liwei, Et al., Application of edge detection and evidence theory in robot target recognition, Research and Exploration in Laboratory, 38, 3, pp. 29-33, (2019)
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