Estimation of Discharge Energy of Lithium-ion Battery for Different Temperatures by FOX-Bidirectional Recurrent Neural Network Method

Yıl 2024, Erken Görünüm, 1 - 1

Göksu Taş

https://doi.org/10.29109/gujsc.1525754

Öz

In this study, the discharge energy of the lithium-ion battery was estimated by using the FOX- Bidirectional Recurrent Neural Network (Bi-RNN) method for the Dynamic Stress Test (DST) driving cycle method and different temperatures. For lithium-based batteries, discharge energy estimation is critical for long-term use, while problems such as overheating are major problems. For this reason, in this study, the discharge energy of lithium-ion batteries under different temperature conditions was estimated using bidirectional-based deep learning methods. In addition, the hyperparameter values of the BiRNN method were determined with FOX optimization, and the FOX-BiRNN method was proposed. The discharge energy estimations of FOX-BiRNN, BiRNN, Bidirectional Gated Recurrent Unit (Bi-GRU), and Bidirectional Long-short term (Bi-LSTM) methods were compared. The obtained estimation results were compared using the most commonly used battery parameter estimation metrics in the literature for performance comparison. The estimation success of the proposed method was presented using many comparison metrics and graphics. The FOX-BiRNN method was the most successful method for discharge energy estimation by obtaining values of %99.4186 at 0 0C according to the R2 metric, %99.6080 at 25 0C according to the R2 metric, and %99.4148 at 45 0C according to the R2 metric.

Anahtar Kelimeler

Electric vehicles, Lithium-ion battery, FOX optimization, Bi-RNN, Energy Estimation

Kaynakça

• [1] J. A. Sanguesa, V. Torres-Sanz, P. Garrido, F. J. Martinez, and J. M. Marquez-Barja, “A Review on Electric Vehicles: Technologies and Challenges,” Smart Cities, vol. 4, no. 1, Art. no. 1, Mar. 2021, doi: 10.3390/smartcities4010022.
• [2] A. Boyar, Y. Kabalcı, and E. Kabalcı, “Grey Wolf Optimization Algorithm-Based Hybrid Energy Storage System Controller Design for Electric Vehicles,” Gazi Univ. J. Sci. Part C Des. Technol., pp. 1–1, doi: 10.29109/gujsc.1475819.
• [3] Y. Wang et al., “A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems,” Renew. Sustain. Energy Rev., vol. 131, p. 110015, Oct. 2020, doi: 10.1016/j.rser.2020.110015.
• [4] K. Kaysal, F. O. Hocaoğlu, and A. Kaysal, “Pasif Batarya Yönetim Sisteminin ARM Tabanlı Mikroişlemciler Kullanılarak Tasarımı ve Deneysel Uygulaması,” Gazi Univ. J. Sci. Part C Des. Technol., vol. 9, no. 1, Art. no. 1, Mar. 2021, doi: 10.29109/gujsc.811313.
• [5] G. Taş, C. Bal, and A. Uysal, “Performance comparison of lithium polymer battery SOC estimation using GWO-BiLSTM and cutting-edge deep learning methods,” Electr. Eng., vol. 105, no. 5, pp. 3383–3397, Oct. 2023, doi: 10.1007/s00202-023-01934-z.
• [6] G. Lee, D. Kwon, and C. Lee, “A convolutional neural network model for SOH estimation of Li-ion batteries with physical interpretability,” Mech. Syst. Signal Process., vol. 188, p. 110004, Apr. 2023, doi: 10.1016/j.ymssp.2022.110004.
• [7] C. Zhang et al., “Battery SOH estimation method based on gradual decreasing current, double correlation analysis and GRU,” Green Energy Intell. Transp., vol. 2, no. 5, p. 100108, Oct. 2023, doi: 10.1016/j.geits.2023.100108.
• [8] F. Wang, Z. Zhao, Z. Zhai, Z. Shang, R. Yan, and X. Chen, “Explainability-driven model improvement for SOH estimation of lithium-ion battery,” Reliab. Eng. Syst. Saf., vol. 232, p. 109046, Apr. 2023, doi: 10.1016/j.ress.2022.109046.
• [9] J. Obregon, Y.-R. Han, C. W. Ho, D. Mouraliraman, C. W. Lee, and J.-Y. Jung, “Convolutional autoencoder-based SOH estimation of lithium-ion batteries using electrochemical impedance spectroscopy,” J. Energy Storage, vol. 60, p. 106680, Apr. 2023, doi: 10.1016/j.est.2023.106680.
• [10] M. Berecibar, I. Gandiaga, I. Villarreal, N. Omar, J. Van Mierlo, and P. Van den Bossche, “Critical review of state of health estimation methods of Li-ion batteries for real applications,” Renew. Sustain. Energy Rev., vol. 56, pp. 572–587, Apr. 2016, doi: 10.1016/j.rser.2015.11.042.
• [11] X. Hu, L. Xu, X. Lin, and M. Pecht, “Battery Lifetime Prognostics,” Joule, vol. 4, no. 2, pp. 310–346, Feb. 2020, doi: 10.1016/j.joule.2019.11.018.
• [12] M. Cheng, X. Zhang, A. Ran, G. Wei, and H. Sun, “Optimal dispatch approach for second-life batteries considering degradation with online SoH estimation,” Renew. Sustain. Energy Rev., vol. 173, p. 113053, Mar. 2023, doi: 10.1016/j.rser.2022.113053.
• [13] Z. Deng, X. Hu, X. Lin, L. Xu, Y. Che, and L. Hu, “General Discharge Voltage Information Enabled Health Evaluation for Lithium-Ion Batteries,” IEEEASME Trans. Mechatron., vol. 26, no. 3, pp. 1295–1306, Jun. 2021, doi: 10.1109/TMECH.2020.3040010.
• [14] Y. Che et al., “State of health prognostics for series battery packs: A universal deep learning method,” Energy, vol. 238, p. 121857, Jan. 2022, doi: 10.1016/j.energy.2021.121857.
• [15] X. Hu, H. Yuan, C. Zou, Z. Li, and L. Zhang, “Co-Estimation of State of Charge and State of Health for Lithium-Ion Batteries Based on Fractional-Order Calculus,” IEEE Trans. Veh. Technol., vol. 67, no. 11, pp. 10319–10329, Nov. 2018, doi: 10.1109/TVT.2018.2865664.
• [16] X. Lai, Y. Huang, X. Han, H. Gu, and Y. Zheng, “A novel method for state of energy estimation of lithium-ion batteries using particle filter and extended Kalman filter,” J. Energy Storage, vol. 43, p. 103269, Nov. 2021, doi: 10.1016/j.est.2021.103269.
• [17] H. ALRahhal and R. Jamous, “RNN-AFOX: adaptive FOX-inspired-based technique for automated tuning of recurrent neural network hyper-parameters,” Artif. Intell. Rev., vol. 56, no. 2, pp. 1981–2011, Nov. 2023, doi: 10.1007/s10462-023-10568-3.
• [18] Q. Li, P. M. Ness, A. Ragni, and M. J. F. Gales, “Bi-directional Lattice Recurrent Neural Networks for Confidence Estimation,” in ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2019, pp. 6755–6759. doi: 10.1109/ICASSP.2019.8683488.
• [19] “Battery Data | Center for Advanced Life Cycle Engineering.” Accessed: Jul. 30, 2024. [Online]. Available: https://calce.umd.edu/battery-data
• [20] F. Zheng, Y. Xing, J. Jiang, B. Sun, J. Kim, and M. Pecht, “Influence of different open circuit voltage tests on state of charge online estimation for lithium-ion batteries,” Appl. Energy, vol. 183, pp. 513–525, Dec. 2016, doi: 10.1016/j.apenergy.2016.09.010.
• [21] M. El Marghichi, M. Bouzi, and N. Ettalabi, “Robust Parameter Estimation of an Electric Vehicle Lithium-Ion Battery Using Adaptive Forgetting Factor Recursive Least Squares,” Int. J. Intell. Eng. Syst., vol. 13, Aug. 2020, doi: 10.22266/ijies2020.1031.08.
• [22] J. Červený, S. Begall, P. Koubek, P. Nováková, and H. Burda, “Directional preference may enhance hunting accuracy in foraging foxes,” Biol. Lett., vol. 7, no. 3, pp. 355–357, Mar. 2011, doi: 10.1098/rsbl.2010.1145.
• [23] H. Mohammed and T. Rashid, “FOX: a FOX-inspired optimization algorithm,” Appl. Intell., vol. 53, no. 1, pp. 1030–1050, Jan. 2023, doi: 10.1007/s10489-022-03533-0.
• [24] G. S. Chadha, A. Panambilly, A. Schwung, and S. X. Ding, “Bidirectional deep recurrent neural networks for process fault classification,” ISA Trans., vol. 106, pp. 330–342, Nov. 2020, doi: 10.1016/j.isatra.2020.07.011.
• [25] E. Messner, M. Zöhrer, and F. Pernkopf, “Heart Sound Segmentation—An Event Detection Approach Using Deep Recurrent Neural Networks,” IEEE Trans. Biomed. Eng., vol. 65, no. 9, pp. 1964–1974, Sep. 2018, doi: 10.1109/TBME.2018.2843258.