Online estimation of state-of-charge using auxiliary load

Year 2024, Volume: 8 Issue: 2, 101 - 115, 30.06.2024

Abdelaziz Zermout Hadjira Belaıdı , Ahmed Maache

https://doi.org/10.30521/jes.1339832

Abstract

Numerous approaches and methodologies have been established for online (while the load is supplied) estimation of the State-of-Charge of Lithium-ion cells and batteries. However, as battery load consumption fluctuates in real time because of delivered device operations, obtaining a precise online state of charge estimation remains a challenging task. This work proposes a new technique for online open circuit voltage measurement to estimate state of charge of batteries. This novel technique proposes the addition of an auxiliary regulated load that may be utilized to temporarily force specifically defined forms of the battery's current curve under particular conditions, which results in improving and simplifying online open circuit voltage computations. The effectiveness of the proposed technique was successfully validated through several experimental tests. The acquired findings demonstrated its efficiency with an acceptable online state of charge estimation accuracy. Typically, an estimation error of less than 2% was recorded in most tests, while the error was less than 1% when the battery’s state of charge was high.

Keywords

Auxiliary controlled load, Dynamic load, Lithium-ion battery (LiB), OCV, SoC

References

• [1] Espedal IB, Jinasena A, Burheim OS, Lamb JJ. Current Trends for State-of-Charge (SoC) Estimation in Lithium-Ion Battery Electric Vehicles. Energies 2021; 14(11):3284. doi:10.3390/en14113284.
• [2] Abdi H, Mohammadi-ivatloo B, Javadi S, Khodaei AR, Dehnavi E. Energy Storage Systems. In: Distributed Generation Systems. Elsevier; 2017:333-368. doi:10.1016/B978-0-12-804208-3.00007-8.
• [3] Azaroual M, Ouassaid M, Maaroufi M. Model predictive control-based energy management strategy for grid-connected residential photovoltaic–wind–battery system. In: Renewable Energy Systems. Elsevier; 2021:89-109. doi:10.1016/B978-0-12-820004-9.00014-0.
• [4] Uğurlu A, Gökçöl C. A case study of PV-wind-diesel-battery hybrid system. J Energy Syst. 2017; 1(4):138-147. doi:10.30521/jes.348335.
• [5] Wang K, Wang W, Wang L, Li L. An Improved SOC Control Strategy for Electric Vehicle Hybrid Energy Storage Systems. Energies 2020; 13(20):5297. doi:10.3390/en13205297.
• [6] Zhang R, Xia B, Li B, et al. State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles. Energies 2018; 11(7): 1820. doi:10.3390/en11071820.
• [7] Zhang L, Peng H, Ning Z, Mu Z, Sun C. Comparative Research on RC Equivalent Circuit Models for Lithium-Ion Batteries of Electric Vehicles. Appl Sci. 2017; 7(10): 1002. doi:10.3390/app7101002.
• [8] Belaidi H, Bentarzi H, Rabiai Z, Abdelmoumene A. Multi-agent System for Voltage Regulation in Smart Grid. In: Hatti M, ed. Artificial Intelligence and Renewables Towards an Energy Transition. Vol 174. Lecture Notes in Networks and Systems. Springer International Publishing; 2021:487-499. doi:10.1007/978-3-030-63846-7_46.
• [9] Belaidi H, Rabiai Z. Decentralized Energy Management System Enhancement for Smart Grid: In: Management Association IR, ed. Research Anthology on Smart Grid and Microgrid Development. IGI Global 2022, 77-90. doi:10.4018/978-1-6684-3666-0.ch004.
• [10] Kaddour D, Belaidi H. Impact of Integrating DERs and ESS on Smart-Grid Supply Continuity: A Review. In: 2022 3rd International Conference on Human-Centric Smart Environments for Health and Well-Being (IHSH); 26-28 October 2022: IEEE, 7-12. doi:10.1109/IHSH57076.2022.10092035.
• [11] Hu X, Cao D, Egardt B. Condition Monitoring in Advanced Battery Management Systems: Moving Horizon Estimation Using a Reduced Electrochemical Model. IEEEASME Trans Mechatron 2018; 23(1): 167-178. doi:10.1109/TMECH.2017.2675920.
• [12] Thakkar RR. Electrical Equivalent Circuit Models of Lithium-ion Battery. In: E. Okedu K, ed. Management and Applications of Energy Storage Devices. IntechOpen; 2022. doi:10.5772/intechopen.99851.
• [13] Çarkit T, Alçi M. Comparison of the performances of heuristic optimization algorithms PSO, ABC and GA for parameter estimation in the discharge processes of Li-NMC battery. J Energy Syst. 2022; 6(3): 387-400. doi:10.30521/jes.1094106.
• [14] Meng J, Luo G, Ricco M, Swierczynski M, Stroe DI, Teodorescu R. Overview of Lithium-Ion Battery Modeling Methods for State-of-Charge Estimation in Electrical Vehicles. Appl Sci. 2018; 8(5): 659. doi:10.3390/app8050659.
• [15] Zhang Q, Wang D, Yang B, Cui X, Li X. Electrochemical model of lithium-ion battery for wide frequency range applications. Electrochimica Acta. 2020; 343: 136094. doi:10.1016/j.electacta.2020.136094.
• [16] Ng KS, Moo CS, Chen YP, Hsieh YC. Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries. Appl Energy 2009; 86(9): 1506-1511. doi:10.1016/j.apenergy.2008.11.021.
• [17] Chang WY. The State of Charge Estimating Methods for Battery: A Review. ISRN Appl Math. 2013; 2013: 1-7. doi:10.1155/2013/953792.
• [18] Dong G, Wei J, Zhang C, Chen Z. Online state of charge estimation and open circuit voltage hysteresis modeling of LiFePO4 battery using invariant imbedding method. Appl Energy 2016; 162: 163-171. doi:10.1016/j.apenergy.2015.10.092.
• [19] Zheng F, Xing Y, Jiang J, Sun B, Kim J, Pecht M. Influence of different open circuit voltage tests on state of charge online estimation for lithium-ion batteries. Appl Energy 2016; 183: 513-525. doi:10.1016/j.apenergy.2016.09.010.
• [20] Meng J, Ricco M, Luo G, et al. An Overview and Comparison of Online Implementable SOC Estimation Methods for Lithium-Ion Battery. IEEE Trans Ind Appl. 2018; 54(2): 1583-1591. doi:10.1109/TIA.2017.2775179.
• [21] Wang X, Wei X, Dai H, Wu Q. State Estimation of Lithium Ion Battery Based on Electrochemical Impedance Spectroscopy with On-Board Impedance Measurement System. In: 2015 IEEE Vehicle Power and Propulsion Conference (VPPC); 19-22 October 2015: IEEE, 1-5. doi:10.1109/VPPC.2015.7353021.
• [22] Spagnol P, Rossi S, Savaresi SM. Kalman Filter SoC estimation for Li-Ion batteries. In: 2011 IEEE International Conference on Control Applications (CCA); 28-30 September 2011: IEEE, 587-592. doi:10.1109/CCA.2011.6044480.
• [23] Rzepka B, Bischof S, Blank T. Implementing an Extended Kalman Filter for SoC Estimation of a Li-Ion Battery with Hysteresis: A Step-by-Step Guide. Energies 2021; 14(13): 3733. doi:10.3390/en14133733.
• [24] Ali M, Kamran M, Kumar P, et al. An Online Data-Driven Model Identification and Adaptive State of Charge Estimation Approach for Lithium-ion-Batteries Using the Lagrange Multiplier Method. Energies 2018; 11(11):2940. doi:10.3390/en11112940.
• [25] Wang Y, Yang D, Zhang X, Chen Z. Probability based remaining capacity estimation using data-driven and neural network model. J Power Sources 2016; 315: 199-208. doi:10.1016/j.jpowsour.2016.03.054.
• [26] Xiong R, Yu Q, Wang LY. Open circuit voltage and state of charge online estimation for lithium ion batteries. Energy Procedia 2017;142:1902-1907. doi:10.1016/j.egypro.2017.12.388.
• [27] Song Y, Park M, Seo M, Kim SW. Online State-of-Charge Estimation for Lithium-Ion Batteries Considering Model Inaccuracies Under Time-Varying Current Conditions. IEEE Access 2020; 8: 192419-192434. doi:10.1109/ACCESS.2020.3032752.
• [28] Meng J, Boukhnifer M, Diallo D. Comparative study of lithium‐ion battery open‐circuit‐voltage online estimation methods. IET Electr Syst Transp. 2020; 10(2): 162-169. doi:10.1049/iet-est.2019.0026.
• [29] Zermout A, Belaidi H, Maache A. Implementation of Battery Characterization System. Eng. Proc. 2023; 29(1): 12. https://doi.org/10.3390/engproc2023029012
• [30] Girijaprasanna, T., & Dhanamjayulu, C. (2022). A review on different state of battery charge estimation techniques and management systems for EV applications. Electronics, 11(11), 1795.
• [31] LI, Jiabo, YE, Min, GAO, Kangping, et al. SOC estimation for lithium‐ion batteries based on a novel model. IET Power Electronics, 2021, vol. 14, no 13, p. 2249-2259.
• [32] Yang, F., Li, W., Li, C., & Miao, Q. (2019). State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network. Energy, 175, 66-75.