AN ADAPTIVE AND HYBRID STATE OF CHARGE ESTIMATION METHOD INTEGRATING SEQUENCE-TO-POINT LEARNING AND COULOMB COUNTING FOR LI-ION BASED ENERGY STORAGE SYSTEMS

Year 2025, Volume: 13 Issue: 1, 98 - 109, 01.03.2025

Halil Çimen

https://doi.org/10.36306/konjes.1554945

Abstract

For safe and long-lasting operation of Li-ion batteries used in electric vehicles and electric grid applications, the State of Charge (SOC) of the battery cell must be estimated with high accuracy. However, due to the uncertainty in environmental conditions and the complex nature of battery chemistry, SOC estimation still presents a significant challenge. In this study, an adaptive and hybrid method for SOC estimation of a Li-ion battery cell is proposed. Convolutional Neural Network (CNN) based Sequence-to-point learning architecture is used to estimate the initial SOC values at specific time intervals. In order to increase the estimation accuracy, a multi-scale CNN architecture is designed, and useful features are captured. The obtained estimation values are integrated with the partial coulomb counting method to increase the accuracy. In addition, the proposed model adaptively updates the estimation weights with the help of the estimation error data obtained during the full charging of the batteries. The proposed model is tested on the LG 18650HG2 dataset. The results prove that the proposed model is 23% more accurate than benchmark models at 25°C and 55.5% more accurate at 0°C.

Keywords

Convolutional Neural Networks, Coulomb Counting, Deep Learning, Li-Ion Batteries, Sequence-to-Point Learning, State of Charge

References

• International Renewable Energy Agency, Innovation landscape brief: Utility-scale batteries, IRENA, Abu Dhabi, 2019.
• Y. Ko, K. Cho, M. Kim, and W. Choi, "A Novel Capacity Estimation Method for the Lithium Batteries Using the Enhanced Coulomb Counting Method With Kalman Filtering," Ieee Access, vol. 10, pp. 38793-38801, 2022, doi: 10.1109/ACCESS.2022.3165639.
• X. Lin, "Theoretical Analysis of Battery SOC Estimation Errors Under Sensor Bias and Variance," IEEE Transactions on Industrial Electronics, vol. 65, no. 9, pp. 7138-7148, 2018, doi: 10.1109/TIE.2018.2795521.
• W. Hou, Q. Shi, Y. Liu, L. Guo, X. Zhang, and J. Wu, "State of Charge Estimation for Lithium-Ion Batteries at Various Temperatures by Extreme Gradient Boosting and Adaptive Cubature Kalman Filter," IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 1-11, 2024, doi: 10.1109/TIM.2023.3346509.
• G. Sethia, S. K. Nayak, and S. Majhi, "An Approach to Estimate Lithium-Ion Battery State of Charge Based on Adaptive Lyapunov Super Twisting Observer," IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 68, no. 3, pp. 1319-1329, 2021, doi: 10.1109/TCSI.2020.3044560.
• R. Guo, Y. Xu, C. Hu, and W. Shen, "Self-Adaptive Neural Network-Based Fractional-Order Nonlinear Observer Design for State of Charge Estimation of Lithium-Ion Batteries," IEEE/ASME Transactions on Mechatronics, vol. 29, no. 3, pp. 1761-1772, 2024, doi: 10.1109/TMECH.2023.3321719.
• V. Chandran, C. K. Patil, A. Karthick, D. Ganeshaperumal, R. Rahim, and A. Ghosh, "State of Charge Estimation of Lithium-Ion Battery for Electric Vehicles Using Machine Learning Algorithms," World Electric Vehicle Journal, vol. 12, no. 1, 2021, doi: 10.3390/wevj12010038.
• J. Tian, C. Chen, W. Shen, F. Sun, and R. Xiong, "Deep Learning Framework for Lithium-ion Battery State of Charge Estimation: Recent Advances and Future Perspectives," Energy Storage Materials, vol. 61, p. 102883, 2023, doi: https://doi.org/10.1016/j.ensm.2023.102883.
• V. Q. Dao et al., "Design of an Effective State of Charge Estimation Method for a Lithium-Ion Battery Pack Using Extended Kalman Filter and Artificial Neural Network," Energies, vol. 14, no. 9, 2021, doi: 10.3390/en14092634.
• C. Vidal, P. Malysz, M. Naguib, A. Emadi, and P. J. Kollmeyer, "Estimating battery state of charge using recurrent and non-recurrent neural networks," J Energy Storage, vol. 47, p. 103660, 2022, doi: https://doi.org/10.1016/j.est.2021.103660.
• Y. Liu, J. Li, G. Zhang, B. Hua, and N. Xiong, "State of Charge Estimation of Lithium-Ion Batteries Based on Temporal Convolutional Network and Transfer Learning,", Ieee Access, vol. 9, pp. 34177-34187, 2021, doi: 10.1109/Access.2021.3057371.
• G. Chen, W. Peng, and F. Yang, "An LSTM-SA model for SOC estimation of lithium-ion batteries under various temperatures and aging levels,", J Energy Storage, vol. 84, 2024, doi: 10.1016/j.est.2024.110906.
• Y. Che et al., "Battery States Monitoring for Electric Vehicles Based on Transferred Multi-Task Learning," Ieee T Veh Technol, vol. 72, no. 8, pp. 10037-10047, 2023, doi: 10.1109/TVT.2023.3260466.
• K. H. Kim, K. H. Oh, H. S. Ahn, and H. D. Choi, "Time–Frequency Domain Deep Convolutional Neural Network for Li-Ion Battery SoC Estimation," Ieee T Power Electr, vol. 39, no. 1, pp. 125-134, 2024, doi: 10.1109/TPEL.2023.3309934.
• K. Jia, Z. Gao, R. Ma, H. Chai, and S. Sun, "An Adaptive Optimization Algorithm in LSTM for SOC Estimation Based on Improved Borges Derivative," IEEE Transactions on Industrial Informatics, vol. 20, no. 2, pp. 1907-1919, 2024, doi: 10.1109/TII.2023.3280340.
• F. Wu, S. Wang, D. Liu, W. Cao, C. Fernandez, and Q. Huang, "An improved convolutional neural network-bidirectional gated recurrent unit algorithm for robust state of charge and state of energy estimation of new energy vehicles of lithium-ion batteries,", J Energy Storage, vol. 82, 2024, doi: 10.1016/j.est.2024.110574.
• C. Zhang, M. Zhong, Z.-H. Wang, N. H. Goddard, and C. Sutton, “Sequence-to-point learning with neural networks for nonintrusive load monitoring,” in Proc. 32nd AAAI Conf. Artif. Intell. (AAAI-18), 2018, pp. 2604–2611.
• P. Kollmeyer, C. Vidal, M. Naguib, and M. Skells, “LG 18650HG2 Li-ion battery data and example deep neural network xEV SOC estimator script,” Mendeley Data, vol. 2, 2020, doi: 10.17632/cp3473x7xv.3.
• S. Minaee, Y. Boykov, F. Porikli, A. Plaza, N. Kehtarnavaz, and D. Terzopoulos, "Image Segmentation Using Deep Learning: A Survey," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 7, pp. 3523-3542, 2022, doi: 10.1109/TPAMI.2021.3059968.
• O. Ronneberger, P. Fischer, and T. Brox, "U-Net: Convolutional Networks for Biomedical Image Segmentation," in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, 2015, pp. 234-241.
• Y. Li, Y. Chen, N. Wang, and Z. Zhang, "Scale-Aware Trident Networks for Object Detection," 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 6053-6062.
• A. Bhattacharjee, A. Verma, S. Mishra, and T. K. Saha, "Estimating State of Charge for xEV Batteries Using 1D Convolutional Neural Networks and Transfer Learning,", Ieee T Veh Technol, vol. 70, no. 4, pp. 3123-3135, Apr 2021, doi: 10.1109/Tvt.2021.3064287.
• C. Bian, H. He, and S. Yang, "Stacked bidirectional long short-term memory networks for state-of-charge estimation of lithium-ion batteries,", Energy, vol. 191, 2020, doi: 10.1016/j.energy.2019.116538.
• M. A. Hannan et al., "SOC Estimation of Li-ion Batteries With Learning Rate-Optimized Deep Fully Convolutional Network,", Ieee T Power Electr, vol. 36, no. 7, pp. 7349-7353, 2021, doi: 10.1109/Tpel.2020.3041876.
• Z. Du, L. Zuo, J. Li, Y. Liu, and H. T. Shen, "Data-Driven Estimation of Remaining Useful Lifetime and State of Charge for Lithium-Ion Battery,", Ieee T Transp Electr, vol. 8, no. 1, pp. 356-367, Mar 2022, doi: 10.1109/Tte.2021.3109636.