ANN-based State of Charge Estimation of Li-ion Batteries for Embedded Applications
Abstract
The conventional state of charge (SOC) estimation model has several concerns, such as accuracy and reliability. In order to realize robust SOC estimation for embedded applications, this study focuses on three concerns of the existing SOC estimation model: accuracy, robustness, and practicality. In improving the estimation accuracy and robustness, this study took into account the dynamic of the actual SOC caused by the dynamic charging and discharging process. In practice, the charging and discharging processes have characteristics that must be considered to realize robust SOC estimation. The model-based SOC estimation developed based on the virtual battery model causes difficulties for real-time applications. Additionally, model-based SOC estimation cannot be reliably extrapolated to different battery types. In defining the behavior of various types of batteries, the model-based SOC estimation must be updated. Hence, this study utilized data-driven SOC estimation based on an artificial neural network (ANN) and measurable battery data. The ANN model, which has excellent adaptability to nonlinear systems, is utilized to increase accuracy. Additionally, using measurable battery data such as voltage and current signals, the SOC estimation model is suitable for embedded applications. Results indicate that estimating SOC with the proposed model reduced errors with respect to actual datasets. In order to verify the feasibility of the proposed model, an online estimation was out on the embedded system with the use of C2000 real-time microcontrollers. Results show that the proposed model can be executed in an embedded system using measurable battery data.
References
M.D. Ashidqi, M. Anwar, C. Hermanu B.A., A. Ramelan, and F. Adriyanto, “Fuzzy Logic Implementation for Accurate Electric Car Battery SOC measurement,” J. Nas. Tek. Elekt., Teknol. Inf., Vol. 10, No. 3, pp. 257–264, Aug. 2021, doi:10.22146/jnteti.v10i3.1885.
M.A. Kamali, A.C. Caliwag, and W. Lim, “Novel SOH Estimation of Lithium-Ion Batteries for Real-Time Embedded Applications,” IEEE Embed. Syst. Lett., Vol. 13, No. 4, pp. 206–209, Dec. 2021, doi: 10.1109/LES.2021.3078443.
C. Pan et al., “Adaptive Neural Network-Based Prescribed-Time Observer for Battery State-of-Charge Estimation,” IEEE Trans. Power Electron., Vol. 38, No. 1, pp. 165–176, Jan. 2023, doi: 10.1109/TPEL.2022.3205437.
R. Guo and W. Shen, “Lithium-Ion Battery State of Charge and State of Power Estimation Based on a Partial-Adaptive Fractional-Order Model in Electric Vehicles,” IEEE Trans. Ind. Electron., Vol. 70, No. 10, pp. 10123–10133, Oct. 2023, doi: 10.1109/TIE.2022.3220881.
Y. Wei and L. Ling, “State-of-Charge Estimation for Lithium-Ion Batteries Based on Temperature-Based Fractional-Order Model and Dual Fractional-Order Kalman Filter,” IEEE Access, Vol. 10, pp. 37131–37148, Mar. 2022 , doi: 10.1109/ACCESS.2022.3163413.
R. Hasan and J. Scott, “Comments on ‘State of Charge-Dependent Polynomial Equivalent Circuit Modeling for Electrochemical Impedance Spectroscopy of Lithium-Ion Batteries’,” IEEE Trans. Power Electron., Vol. 35, No. 4, pp. 4448–4448, Apr. 2020, doi: 10.1109/TPEL.2019.2938508.
Y. Song, M. Park, M. Seo, and S. W. Kim, “Improved SOC Estimation of Lithium-Ion Batteries with Novel SOC-OCV Curve Estimation Method Using Equivalent Circuit Model,” 4th Int. Conf. Smart, Sustain. Technol. (SpliTech), 2019, pp. 1–6, doi: 10.23919/SpliTech.2019.8783149.
X. Hu, F. Sun, and Y. Zou, “Estimation of State of Charge of a Lithium-Ion Battery Pack for Electric Vehicles Using an Adaptive Luenberger Observer,” Energies, Vol. 3, No. 9, pp. 1586–1603, Sep. 2010, doi: 10.3390/en3091586.
X. Chen, W. Shen, Z. Cao, and A. Kapoor, “A Novel Approach for State of Charge Estimation Based on Adaptive Switching Gain Sliding Mode Observer in Electric Vehicles,” J. Power Sources, Vol. 246, pp. 667–678, Jan. 2014, doi: 10.1016/j.jpowsour.2013.08.039.
C. Pan et al., “Adaptive Neural Network-Based Prescribed-Time Observer for Battery State-of-Charge Estimation,” IEEE Trans. Power Electron., Vol. 38, No. 1, pp. 165–176, Jan. 2023, doi: 10.1109/TPEL.2022.3205437.
L. Zhang et al., “A Sparse Least Squares Support Vector Machine Used for SOC Estimation of Li-Ion Batteries,” IFAC-Papers OnLine, Vol. 52, No. 11, pp. 256–261, 2019, doi: 10.1016/j.ifacol.2019.09.150.
Q. Wang, P. Wu, and J. Lian, “SOC Estimation Algorithm of Power Lithium Battery Based on AFSA‐BP Neural Network,” J. Eng., Vol. 2020, No. 13, pp. 535–539, Jul. 2020, doi: 10.1049/joe.2019.1214.
C. Pan et al., “Adaptive Neural Network-Based Event-Triggered SOC Observer with Application to a Stochastic Battery Model,” IEEE Trans. Neural Netw., Learn. Syst., early access, 21-Sep-2022, doi: 10.1109/TNNLS.2022.3205040.
Q. Wang et al., “State of Charge Estimation for Lithium-Ion Battery Based on NARX Recurrent Neural Network and Moving Window Method,” IEEE Access, Vol. 9, pp. 83364–83375, Jun. 2021, doi: 10.1109/ACCESS.2021.3086507.
Y. Che, Y. Liu, Z. Cheng, and J. Zhang, “SOC and SOH identification method of li-ion battery based on SWPSO-DRNN,” IEEE J. Emerg. Sel. Top. Power Electron., Vol. 9, No. 4, pp. 4050–4061, Aug. 2021, doi: 10.1109/JESTPE.2020.3004972.
M. Wei et al., “State of Charge Estimation of Lithium-Ion Batteries Using LSTM and NARX Neural Networks,” IEEE Access, Vol. 8, pp. 189236–189245, Oct. 2020, doi: 10.1109/ACCESS.2020.3031340.
M.A. Kamali, A. Caliwag, and W. Lim, “Deep Learning Based SOC Estimation for Hybrid Energy Storage System,” 8th Commun., Inf. Joint Conf. (JCCI’98), 2022, pp. 452–454.
Y. Song, M. Park, M. Seo and S.W. Kim, “Online State-of-Charge Estimation for Lithium-Ion Batteries Considering Model Inaccuracies Under Time-Varying Current Conditions,” IEEE Access, Vol. 8, pp. 192419–192434, Oct. 2020, doi: 10.1109/ACCESS.2020.3032752.
“SM벡셀 배터리사업부문,” bexel.co.kr, https://www.bexel.co.kr/html/index/index.php, access date: 1-Jan-2023.
Y. Gao et al., “Classification and Review of the Charging Strategies for Commercial Lithium-Ion Batteries,” IEEE Access, Vol. 7, pp. 43511–43524, Mar. 2019, doi: 10.1109/ACCESS.2019.2906117.
Z. Ren and C. Du, “State of Charge Estimation for Lithium-Ion Batteries Using Extreme Learning Machine and Extended Kalman Filter,” IFAC-PapersOnLine, Vol. 55, No. 24, pp. 197–202, 2022, doi: 10.1016/j.ifacol.2022.10.284.
M. Kim et al., “State of Charge Estimation for Lithium-Ion Battery Based on Reinforcement Learning,” IFAC-PapersOnLine, Vol. 51, No. 28, pp. 404–408, 2018, doi: 10.1016/j.ifacol.2018.11.736.
J. Wen et al., “State of Charge Estimation for Lithium Battery Based on Levenberg-Marquardt Back-Propagation Neural Network with Momentum Term,” Proc. 2022 5th Int. Conf. Algorithms Comput., Artif. Intell., Dec. 2022, pp. 1–5, doi:10.1145/3579654.3579696.
A.A. A’ziziyyah, B.A.S. Aji, and M.A. Kamali “Rekomendasi Pemilihan Program Studi Menggunakan Support Vector Regression,” Indonesian J. Comput., Inf. Technol. (IJCIT), Vol. 7, No. 2, pp. 143–150, Nov. 2022, 10.31294/ijcit.v7i2.14120.
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