Li-ion Battery State-of-Charge estimation algorithm with CNN-LSTM and Transfer Learning using synthetic training data

  1. Azkue, Markel 12
  2. Oca, Laura 1
  3. Iraola, Unai 1
  4. Lucu, M. 2
  5. Martinez-Laserna, E. 2
  1. 1 Mondragon Unibertsitatea, Department of Electronics and Computer Science, 20120 Hernani, Spain
  2. 2 Ikerlan Technology Research Centre, Basque Research and Technology Alliance (BRTA). P° J.M. Arizmendiarrieta, 2, 20500 Arrasate-Mondragón, Spain
Actas:
35th International Electric Vehicle Symposium & Exhibition

Año de publicación: 2022

Tipo: Aportación congreso

GOOGLE SCHOLAR lock_openeBiltegia editor

Resumen

The development of State-of-Charge (SoC) algorithms for Li-ion batteries involves carrying out different laboratory tests with the money and time that this entails. Furthermore, such laboratory labours must typically be repeated for each new Li-ion cell reference. In order to minimise this issue, this work proposes a new approach for developing SoC algorithms, using a Recurrent Neural Network in combination with a Transfer Learning method. The latter technique will make possible to take advantage of the data generated for previously tested cell references and use it for the development of a SoC estimation algorithm for a new cell reference. This work provides a proof-of-concept for the proposed approach, using synthetic data generated from electrochemical models, which describes the behaviour of different Li-ion cell references.

Referencias bibliográficas

  • [1] I. B. Espedal, A. Jinasena, O. S. Burheim, and J. J. Lamb, “Current trends for state-of-charge (SoC) estimation in lithium-ion battery electric vehicles,” Energies, vol. 14, no. 11. MDPI AG, Jun. 01, 2021. doi: 10.3390/en14113284.
  • [2] J. P. Rivera-Barrera, N. Muñoz-Galeano, and H. O. Sarmiento-Maldonado, “Soc estimation for lithium-ion batteries: Review and future challenges,” Electronics (Switzerland), vol. 6, no. 4. MDPI AG, Dec. 01, 2017. doi: 10.3390/electronics6040102.
  • [3] F. Zhuang et al., “A Comprehensive Survey on Transfer Learning,” Proceedings of the IEEE, vol. 109, no. 1, Jan. 2021, doi: 10.1109/JPROC.2020.3004555.
  • [4] S. Shen, M. Sadoughi, M. Li, Z. Wang, and C. Hu, “Deep convolutional neural networks with ensemble learning and transfer learning for capacity estimation of lithium-ion batteries,” Applied Energy, vol. 260, no. November 2019, p. 114296, 2020, doi: 10.1016/j.apenergy.2019.114296.
  • [5] M. Savargaonkar and A. Chehade, “An Adaptive Deep Neural Network with Transfer Learning for State-of-Charge Estimations of Battery Cells,” in 2020 IEEE Transportation Electrification Conference & Expo (ITEC), Jun. 2020, pp. 598–602. doi: 10.1109/ITEC48692.2020.9161464.
  • [6] C. Vidal, P. Kollmeyer, E. Chemali, and A. Emadi, “Li-ion Battery State of Charge Estimation Using Long Short-Term Memory Recurrent Neural Network with Transfer Learning,” in 2019 IEEE Transportation Electrification Conference and Expo (ITEC), Jun. 2019, pp. 1–6. doi: 10.1109/ITEC.2019.8790543.
  • [7] K. Kaur, A. Garg, X. Cui, S. Singh, and B. K. Panigrahi, “Deep learning networks for capacity estimation for monitoring SOH of Li‐ion batteries for electric vehicles,” International Journal of Energy Research, vol. 45, no. 2, pp. 3113–3128, Feb. 2021, doi: 10.1002/er.6005.
  • [8] S. Kiranyaz, O. Avci, O. Abdeljaber, T. Ince, M. Gabbouj, and D. J. Inman, “1D convolutional neural networks and applications: A survey,” Mechanical Systems and Signal Processing, vol. 151, p. 107398, Apr. 2021, doi: 10.1016/J.YMSSP.2020.107398.
  • [9] M. Cañizo Zubizarreta, “Large-scale anomaly detection and diagnosis on industrial heterogeneous multi-sensor systems using deep learning,” Universidad de Deusto, 2020.
  • [10] C. Li, F. Xiao, and Y. Fan, “An Approach to State of Charge Estimation of Lithium-Ion Batteries Based on Recurrent Neural Networks with Gated Recurrent Unit,” Energies (Basel), vol. 12, no. 9, p. 1592, Apr. 2019, doi: 10.3390/en12091592.
  • [11] X. Song, F. Yang, D. Wang, and K.-L. Tsui, “Combined CNN-LSTM Network for State-of-Charge Estimation of Lithium-Ion Batteries,” IEEE Access, vol. 7, pp. 88894–88902, 2019, doi: 10.1109/ACCESS.2019.2926517.
  • [12] J. Tian, R. Xiong, W. Shen, and J. Lu, “State-of-charge estimation of LiFePO4 batteries in electric vehicles: A deep-learning enabled approach,” Applied Energy, vol. 291, Jun. 2021, doi: 10.1016/j.apenergy.2021.116812.
  • [13] H. I. Fawaz, G. Forestier, J. Weber, L. Idoumghar, and P. A. Muller, “Transfer learning for time series classification,” Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018, pp. 1367–1376, Jan. 2019, doi: 10.1109/BIGDATA.2018.8621990.
  • [14] “Doyle et al. - 1996 - Comparison of Modeling Predictions with Experimental Data from Plastic Lithium Ion Cells”.