Journal of IKEEE (전기전자학회논문지)

Institute of Korean Electrical and Electronics Engineers (한국전기전자학회)
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Comparison of Learning Techniques of LSTM Network for State of Charge Estimation in Lithium-Ion Batteries

리튬 이온 배터리의 충전 상태 추정을 위한 LSTM 네트워크 학습 방법 비교

  • Hong, Seon-Ri (Dept. of Electrical Engineering, Chungnam National University) ;
  • Kang, Moses (Dept. of Electrical Engineering, Korea Institute of Energy Research) ;
  • Kim, Gun-Woo (Dept. of Electrical Engineering, Chungnam National University) ;
  • Jeong, Hak-Geun (Dept. of Electrical Engineering, Korea Institute of Energy Research) ;
  • Beak, Jong-Bok (Dept. of Electrical Engineering, Korea Institute of Energy Research) ;
  • Kim, Jong-Hoon (Dept. of Electrical Engineering, Chungnam National University)
  • Received : 2019.12.10
  • Accepted : 2019.12.27
  • Published : 2019.12.31
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Abstract

To maintain the safe and optimal performance of batteries, accurate estimation of state of charge (SOC) is critical. In this paper, Long short-term memory network (LSTM) based on the artificial intelligence algorithm is applied to address the problem of the conventional coulomb-counting method. Different discharge cycles are concatenated to form the dataset for training and verification. In oder to improve the quality of input data for learning, preprocessing was performed. In addition, we compared learning ability and SOC estimation performance according to the structure of LSTM model and hyperparameter setup. The trained model was verified with a UDDS profile and achieved estimated accuracy of RMSE 0.82% and MAX 2.54%.

안전하고 최적의 배터리 성능을 유지하기 위해 정확한 충전상태(SOC) 추정 기술이 필수적이다. 본 논문에서는 기존의 전류적산 방법이 가지고 있는 문제를 해결하기 위해 시간 종속성을 가지는 인공지능 기반의 LSTM을 이용한 SOC 추정 방법을 적용하였다. 훈련과 검증에 필요한 데이터는 전기적 실험을 통해 일정 크기로 방전된 전류, 전압, 온도를 수집하였고 학습을 위한 입력데이터의 질을 향상시키기 위해 데이터 전처리를 수행하였다. 또한, LSTM 모델의 구조 및 하이퍼파라미터 설정에 따른 학습 능력과 SOC 추정 성능을 비교하였다. 학습한 모델은 UDDS 프로파일을 통해 검증하였으며, RMSE 0.82%, MAX 2.54%의 추정 정확도를 달성하였다.

Keywords

References

  1. Alicia K. Birky, "Modeling for Light and Heavy Vehicle Market Analysis," Energetics, 2015 Department of Energy, 2015.
  2. M. A. Hannan, M. S. H. Liqu, A. Hussain, A. Mohamed, "A review of lithium-ion battery state of charge estimation and management system in electric vehicle application: Challenges and recommendations," Renewable and Sustainable Energy Reviews, vol.78, pp.834-854, 2017. https://doi.org/10.1016/j.rser.2017.05.001
  3. L. Lu, X. Han, J. Li, J. Hua, and M. Ouyang, "A review on the key issues for lithium-ion battery management in electric vehicles," Journal of Power Sources, vol.226, pp.272-288, 2013. DOI: 10.1016/j.jpowsour.2012.10.060
  4. C. Huang, Z. Wang, Z. Zhao, L. Wang, C. S. Lai, and D. Wang, "Robustness evaluation of extended and unscented Kalman filter for battery state of charge estimation," IEEE, vol.6, pp.27617- 27628, 2018. DOI: 10.1109/ACCESS.2018.2833858
  5. Kong Soon Ng, Chin-Sien Moo, Yi-Ping Chen, Yao-Ching Hsieh, "Enhanced coulomb counting method for estimating state-of-charge and stateof- health of lithium-ion batteries," Applied Energy, vol.86, No,9, pp.1506-1511, 2009. DOI: 10.1016/j.apenergy.2008.11.021
  6. Saeed Sepasi, Reza Ghorbani, Bor Yann Liaw, "A novel on-board state-of-charge estimation method for aged Li-ion batteries based on model adaptive extended kalman filter," Journal of Power Sources, vol.245, pp.337-344, 2014. DOI: /10.1016/j.jpowsour.2013.06.108
  7. G. L. Plett, "Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 2. Modeling and identification," Journal of Power Sources, vol.134, pp.262-276, 2004. DOI: 10.1016/j.jpowsour.2004.02.032
  8. G. L. Plett, "Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. Modeling and identification," Journal of power Sources, vol.134, no.2, pp.277- 292, 2004. DOI: 10.1016/j.jpowsour.2004.02.032
  9. Bo Dai, Yuqi Zhang, Dahua Lin. "Detecting Visual Relationships with Deep Relational Networks," The IEEE Conference on Computer Vision and Pattern Recognition, pp.3076-3086, 2017.
  10. Xiaosong Hu, Shengbo Eben Li, Yalian Yang. "Advanced Machine Learning Approach for Lithium-Ion Battery State Estimation in Electric Vehicles," IEEE Transaction on Transportation Electrification, Vol.2, No.2, pp.140-149, 2016. DOI: 10.1109/TTE.2015.2512237
  11. YongZhi Zhang, Rui Ziong, HongWen He, Zhiru Liu. "A LSTM-RNN method for the lithium-ion battery remaining useful life prediction," Prognostics and System Health Management Conference(PHM-Harbin), 2017. DOI: 10.3390/en12040660
  12. Juan Pablo Rivera-Barrera, Nicolas Munooz- Galeano and Henry Omar Samiento-Maldonade "SOC Estimation for Lithium-ion Batteries: Review and Future Challenges," Journals of Electronics, Vol.6, No.4, pp.102, 2017. DOI: 10.3390/electronics6040102
  13. Juan Carlos Alvarez Anton, Paulino Jose Garcia Nieto, Cecilio Blanco Viejo, Jose Antonio Vilan Vilan, "Support Vector Machines Used to Estimate the Battery State of Charge," IEEE Transactions on Power Electronics, vol.28, pp. 5919-5926, 2013. DOI: 10.1109/TPEL.2013.2243918
  14. Xiangbao Song, Fangfang Yang, Dong Wang, and Kwok-Leung Tsui, "Combined CNNLSTM Network for State-of-Charge Estimation of Lithium-Ion Batteries," IEEE Access, vol.7, pp.88894-88902, 2019. DOI: 10.1109/ACCESS.2019.2926517
  15. Sutskever I. Training recurrent neural networks. Doctoral; 2013.
  16. Hochreiter S, Schmidhuber J. "Long shortterm memory," Neural Computation, vol.9(8), pp. 1735-80, 1997. https://doi.org/10.1162/neco.1997.9.8.1735
  17. Kingma DP, Ba J. "Adam: a method for stochastic optimization," CoRR, vol.abs/1412.6980, 2014.
  18. Zhe Li, Jun Huang, Bor Yann Liaw, Jianbo Zhang, "On state-of-charge determination for lithium-ion batteries," Journal of Power Sources, vol.348, pp.281-301, 2017. DOI: 10.1016/j.jpowsour.2017.03.001