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JACIII Vol.24 No.7 pp. 855-863
doi: 10.20965/jaciii.2020.p0855
(2020)

Paper:

Estimation of SOC Based on LSTM-RNN and Design of Intelligent Equalization Charging System

Xi Chen*,**,†, Kaoru Hirota*, Yaping Dai*, and Zhiyang Jia*

*School of Automation, Beijing Institute of Technology
No.5 Zhongguancun South Street, Haidian District, Beijing 100081, China

**College of Physics and Energy, Fujian Normal University
No.8 Xuefu South Road, Shangjie, Minhou, Fuzhou, Fujian 350117, China

Corresponding author

Received:
October 17, 2020
Accepted:
October 27, 2020
Published:
December 20, 2020
Keywords:
intelligent equalization charging, auxiliary constant current source, LSTM-RNN
Abstract

Lithium battery packs are the main driving energy source for electric vehicles. A battery pack equalization charging solution using a constant current source for variable rate charging is presented in this paper. The charging system consists of a main constant current source and independent auxiliary constant current sources. Auxiliary constant current sources are controlled by the battery management system (BMS), which can change the current rate of the corresponding single battery, and achieve full charging of each single cell in the series battery pack. At the same time, the state of charge (SOC) is regarded as time series data to establish a long short-term memory recurrent neural network (LSTM-RNN) model, and it is possible to obtain the single battery with lower capacity, so that the charging efficiency and battery pack consistency can be improved. The experimental results show that the open circuit voltage difference between the single cells is less than 50 mV after the charging of 20 strings of lithium battery packs by using this method, which achieve the purpose of equalization charging.

Cite this article as:
Xi Chen, Kaoru Hirota, Yaping Dai, and Zhiyang Jia, “Estimation of SOC Based on LSTM-RNN and Design of Intelligent Equalization Charging System,” J. Adv. Comput. Intell. Intell. Inform., Vol.24, No.7, pp. 855-863, 2020.
Data files:
References
  1. [1] Y. Ma, P. Duan, Y. Sun et al., “Equalization of Lithium-ion Battery Pack based on Fuzzy Logic Control in Electric Vehicle,” IEEE Trans. on Industrial Electronics, Vol.65, No.8, pp. 6762-6771, 2018.
  2. [2] Q. Huang, H.-B. Yan, and R. Ling, “Design and Implementation of Non-dissipative Equalization Management Scheme for Series Connected Li-ion Battery Pack,” Computer Engineering, Vol.37, No.12, 2011 (in Chinese).
  3. [3] F. X. Cheng, X. Wang, and Y. Wang, “On the Balanced Charging Control Method for EV Lithium Battery,” Automotive Electronics, Vol.5, pp. 49-51, 2014 (in Chinese).
  4. [4] B. B. Qiu, Z. H. Wang, C. Li et al., “Fuzzy Control Strategy for Battery Equalization Charge Based on State of Charge,” J. of Power Supply, Vol.13, No.2, pp. 113-120, 2015 (in Chinese).
  5. [5] J. Q. Qin, F. Ran, Y. Ji et al., “A Fuzzy Control Based Equalization System with Clipping and Parallel Valley Filling,” Power Electronics, Vol.2, pp. 74-77, 2017 (in Chinese).
  6. [6] S. Yarlagadda, T. T. Hartley, and I. Husain, “A Battery Management System using an active charge equalization technique based on a DC/DC converter topology,” IEEE Trans. on Industry Applications, Vol.49, No.6, pp. 2720-2729, 2013.
  7. [7] D. Cadar, D. Petreus, T. Patarau et al., “Fuzzy controlled energy converter equalizer for lithium ion battery packs,” Int. Conf. on Power Engineering, Energy and Electrical Drives, pp. 1-6, 2011.
  8. [8] A. J. Ding, L. Zou, J. Y. Qi et al., “Equalization strategy for MH-Ni battery charging based on BP algorithm,” Chinese J. of Power Sources, Vol.40, No.1, pp. 100-102, 2016 (in Chinese).
  9. [9] S. J. Song, Z. H. Wang, and X. F. Lin, “SOC-Based Bi-Directional Active Equalization Control for Lithium-Ion Power Battery,” J. of System Simulation, Vol.3, 2017 (in Chinese).
  10. [10] X. Y. Liu and X. Du, “Improvement of active equilibrium charge control method for lithium ion batteries,” Power Technology, Vol.43, No.2, pp. 124-126, 158, 2019 (in Chinese).
  11. [11] M. A. Hannan, M. M. Hoque, S. E. Peng, and M. N. Uddin, “Lithium-ion battery charge equalization algorithm for electric vehicle applications,” 2016 IEEE Industry Applications Society Annual Meeting, pp. 1-8, 2016.
  12. [12] M. A. Hannan, M. S. Hossain Lipu, A. Hussain et al., “Neural Network Approach for Estimating State of Charge of Lithium-ion Battery Using Backtracking Search Algorithm,” IEEE Access, Vol.6, pp. 10069-10079, 2018.
  13. [13] F. Asghar et al., “Simulation Study on Battery State of Charge Estimation Using Kalman Filter,” J. Adv. Comput. Intell. Intell. Inform,, Vol.20, No.6, pp. 861-866, 2016.
  14. [14] M. Talha, F. Asghar, and S. H. Kim, “Experimental Evaluation of Cell Balancing Algorithms with Arduino Based Monitoring System,” J. Adv. Comput. Intell. Intell. Inform., Vol.20, No.6, pp. 968-973, 2016.
  15. [15] H. Zhu and D. X. Xia, “A Simulation Study of Power Battery Grouping Equalization System,” J. of System Simulation, Vol.30, No.9, pp. 259-267, 2018 (in Chinese).
  16. [16] H. R. Liu, C. F. Du, B. Li, S. L. Chen, and Y. X. Guo, “Research on High Speed Energy Equalizer for Battery Pack Based on Mixed Chopper Circuit,” Trans. of China Electrotechnical Society, Vol.33, No.S2, pp. 472-478, 2018 (in Chinese).
  17. [17] Z. Y. Liu, Y. H. Wu, P. F. Li, and P. C. Li, “Battery Pack Equalization Method Based on Cuk Chopper Circuit,” Chinese J. of Scientific Instrument, Vol.2, 2019 (in Chinese).
  18. [18] Z. S. Zhang and X. S. Cai, “Switching power supply principle and design,” Publishing House of Electronics Industry, 2004 (in Chinese).
  19. [19] P. Geng, M. Xu, and S. Xue, “Battery SOC estimation method based on LSTM recurrent neural network,” J. of Shanghai Maritime University, Vol.40, No.3, pp. 120-126, 2019 (in Chinese).
  20. [20] E. Chemali, P. Kollmeyer, M. Preindl, R. Ahmed, and A. Emadi, “Long Short-Term Memory Networks for Accurate State-of-Charge Estimation of Li-ion Batteries,” IEEE Trans. on Industrial Electronics, Vol.65, No.8, pp. 6730-6739, 2017.
  21. [21] D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” 3rd Int. Conf. for Learning Representations, arXiv:1412.6980, 2015.

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