A Novel Method for SoC Estimation of Lithium-Ion Batteries Based on Kalman Filter in Electric Vehicle
Abstract views: 256
/ 
PDF downloads: 113
DOI:
https://doi.org/10.59287/ijanser.889Keywords:
Electric Vehicle, Estimation, , Li-Ion Battery, Kalman Filter, SoCAbstract
In recent years, the energy crisis has become more and more serious. Li-ion batteries are used in grids because of their benefits such as contributing to the intermittent generation of renewable energy sources and stabilizing the grid. In addition, li-ion batteries are widely used in electric vehicles due to their long cycle life and high energy density. Li-ion battery state of charge (SoC) is an important indicator for safety. Therefore, the SoC estimation of li-ion batteries is important. Today, there are different methods to determine the state of the SoC in many applications. The traditional estimation method, the ampere-hour integration method and the coulomb counting method, has a cumulative error and cannot achieve good results in a working environment with Gaussian noise. For this purpose, in this study, firstly, the Thevenin equivalent model was created for battery SOC estimation, and then the Kalman filter algorithm was applied. Thus, the estimation error caused by Gaussian noise is eliminated. SoC estimation was simulated for the battery model created in the MATLAB/Simulink program using this method. Using these simulation results, the charge/discharge characteristics of the battery were obtained. However, the SoC estimation has been made for the charging and discharging processes of the battery. In the simulation, the charge value was recorded for 6 hours. The data recorded every 10 minutes gave results very close to the true value.
Downloads
References
X. L. Liu, Z. M. Cheng, F. Y. Yi and T. Y. Qiu, “SOC calculation method based on extended Kalman filter of power battery for electric vehicle,” 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), Nanjing, China, 2017, pp. 1-4.
J. Wang, H. Chang, H. Mei, Y. Cheng and L. Sun, “SOC Estimation of Lithium-ion Batteries Based on Extended Kalman Filter,” 2022 IEEE 4th Eurasia Conference on IOT, Communication and Engineering (ECICE), Yunlin, Taiwan, 2022, pp. 170-175.
M. Özcan, H. Günay, “Control of diesel engines mounted on vehicles in mobile cranes via CAN bus,” Turkish Journal of Electrical Engineering and Computer Sciences, vol. 21, no. 8, pp. 2181-2190, 2013.
H. Huang, Z. Zhang, C. Guo and L. Ge, “SOC Estimation of Lithium Battery Based on Extended Kalman Filter Optimized by Recurrent Neural Network,” 2022 China International Conference on Electricity Distribution (CICED), Changsha, China, 2022, pp. 41-46.
H. Aung, K. Soon Low and S. Ting Goh, “State-of-Charge Estimation of Lithium-Ion Battery Using Square Root Spherical Unscented Kalman Filter (Sqrt-UKFST) in Nanosatellite,” in IEEE Transactions on Power Electronics, vol. 30, no. 9, pp. 4774-4783, Sept. 2015.
W. Wang, X. Wang, C. Xiang, C. Wei and Y. Zhao, “Unscented Kalman Filter-Based Battery SOC Estimation and Peak Power Prediction Method for Power Distribution of Hybrid Electric Vehicles,” in IEEE Access, vol. 6, pp. 35957-35965, 2018.
M. Shehab El Din, A. A. Hussein and M. F. Abdel-Hafez, “Improved Battery SOC Estimation Accuracy Using a Modified UKF With an Adaptive Cell Model Under Real EV Operating Conditions,” in IEEE Transactions on Transportation Electrification, vol. 4, no. 2, pp. 408-417, June 2018.
A. Khalid et al., “Comparison of Kalman Filters for State Estimation Based on Computational Complexity of Li-Ion Cells,” Energies, vol. 16, no. 6, p. 2710, Mar. 2023.
H. Chen, F. Zhang, X. Zhao, G. Lei, and C. He, “ARWLS-AFEKE: SOC Estimation and Capacity Correction of Lithium Batteries Based on a Fusion Algorithm,” Processes, vol. 11, no. 3, p. 800, Mar. 2023.
R. Bustos, S. A. Gadsden, M. Al-Shabi, and S. Mahmud, “Lithium-Ion Battery Health Estimation Using an Adaptive Dual Interacting Model Algorithm for Electric Vehicles,” Applied Sciences, vol. 13, no. 2, p. 1132, Jan. 2023.
M. Şen, M. Özcan, “Elektrikli Araçlarda Elektriksel Frenlemenin Bulanık Mantık Tabanlı Karar Destek Sistemleri ile Tasarlanması,” 6th International Symposium on Innovative Approaches in Smart Technologies (ISAS WINTER), Turkey, 2022, pp. 12-15.
J. Wang and Z. Zhang, “Lithium-ion Battery SOC Estimation Based on Weighted Adaptive Recursive Extended Kalman Filter Joint Algorithm,” 2020 IEEE 8th International Conference on Computer Science and Network Technology (ICCSNT), Dalian, China, 2020, pp. 11-15.
W. Li, Y. Zhu, X. Guo, X. Zhang, Y. Zhang and Y. Zhou, “Jointly Estimation Method of the SOC and SOH of Lithium-ion Battery based on Fractional Order Multi-Innovation Dual Unscented Kalman Filter,” IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society, Brussels, Belgium, 2022, pp. 1-6.
J. Xu, D. Wang and M. Jiao, “SOC estimation of lithium battery with weighted multi-innovation adaptive Kalman filter algorithm,” 2022 IEEE 5th International Electrical and Energy Conference (CIEEC), Nangjing, China, 2022, pp. 624-629.
V. Sangwan, R. Kumar and A. K. Rathore, “State-of-charge estimation for li-ion battery using extended Kalman filter (EKF) and central difference Kalman filter (CDKF),” 2017 IEEE Industry Applications Society Annual Meeting, Cincinnati, OH, USA, 2017, pp. 1-6.
E. İ. Tezde, H. İ. Okumuş “Batarya Modelleri ve Şarj Durumu (SoC) Belirleme,” EMO Bilimsel Dergi, vol. 8, no. 15, pp. 33-39, Jun.2018.
M. Hossain, S. Saha, M. E. Haque, M. T. Arif and A. Oo, “A Parameter Extraction Method for the Thevenin Equivalent Circuit Model of Li-ion Batteries,” 2019 IEEE Industry Applications Society Annual Meeting, Baltimore, MD, USA, 2019, pp. 1-7.
Y. Fu, B. Zhai, Z. Shi, J. Liang, and Z. Peng, “State of Charge Estimation of Lithium-Ion Batteries Based on an Adaptive Iterative Extended Kalman Filter for AUVs,” Sensors, vol. 22, no. 23, p. 9277, Nov. 2022.
X. Zhang, Y. Huang, Z. Zhang, H. Lin, Y. Zeng, and M. Gao, “A Hybrid Method for State-of-Charge Estimation for Lithium-Ion Batteries Using a Long Short-Term Memory Network Combined with Attention and a Kalman Filter,” Energies, vol. 15, no. 18, p. 6745, Sep. 2022.
M. M. Serinbaş, M. O. Gülbahçe, “Batarya Şarj Durumu Kestirim Yöntemleri Üzerine İnceleme,” Güç Sistemleri Konferansı (CIGRE), Ankara, Turkey, 2022.
J. Zhang, L. Zhang, Y. Li and H. Liu, “The State-of-Charge Estimation of Supercapacitor With Kalman Filtering Algorithm,” 2021 3rd International Conference on Electrical Engineering and Control Technologies (CEECT), Macau, Macao, 2021, pp. 208-211.
H. Wang, Y. Chen, J. Luo, C. Liu, P. Gao and G. Chen, “State of Charge Estimation of Li-Ion Battery Based on Improved Extended Kalman Filter,” 2021 IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), Chongqing, China, 2021, pp. 950-953.
