Comparative Study of Online Open Circuit Voltage Estimation Techniques for State of Charge Estimation of Lithium-Ion Batteries
Source abstract
Abstract
Online estimation techniques are extensively used to determine the parameters of various uncertain dynamic systems. In this paper, online estimation of the open-circuit voltage (OCV) of lithium-ion batteries is proposed by two different adaptive filtering methods (i.e., recursive least square, RLS, and least mean square, LMS), along with an adaptive observer. The proposed techniques use the battery’s terminal voltage and current to estimate the OCV, which is correlated to the state of charge (SOC). Experimental results highlight the effectiveness of the proposed methods in online estimation at different charge/discharge conditions and temperatures. The comparative study illustrates the advantages and limitations of each online estimation method.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Battery Technologies Research · Advancements in Battery Materials · Advanced DC-DC Converters
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Electronics, Carleton University, 1125 Colonel By Drive, Ottawa, ON K1S 5B6, Canada
Department of Electrical and Computer Engineering, Tennessee Technological University, 220 W. 10th Street, Cookeville, TN 38505, USA
Funding
Tennessee Technological University
References (32)
Show source-provided references
1. A chain structure of switched capacitor for improved cell balancing speed of lithium-ion batteries10.1109/TIE.2013.2288195 ↗
2. Battery charger for electric vehicle traction battery switch station10.1109/TIE.2012.2233695 ↗
3. Online estimation of state of charge in li-ion batteries using impulse response concept10.1109/TSG.2011.2169818 ↗
4. State-of-charge estimation of the lithium-ion battery using an adaptive extended Kalman filter based on an improved Thevenin model10.1109/TVT.2011.2132812 ↗
5. Lyapunov-based adaptive state of charge and state of health estimation for lithium-ion batteries10.1109/TIE.2014.2341576 ↗
6. Nonlinear state of charge estimator for hybrid electric vehicle Battery10.1109/TPEL.2008.924629 ↗
7. Chaoui, H., and Sicard, P. (2011, January 14–16). Accurate state of charge (SOC) estimation for batteries using a reduced-order observer. Proceedings of the 2011 IEEE International Conference on Industrial Technology, Taipei, Taiwan.10.1109/ICIT.2011.5754342 ↗
8. Adaptive state of charge estimation of lithium-ion batteries with parameter and thermal uncertainties10.1109/TCST.2016.2572362 ↗
9. An adaptive unscented Kalman filtering approach for online estimation of model parameters and state-of-charge of lithium-ion batteries for autonomous mobile robots10.1109/TCST.2014.2317781 ↗
10. State of charge estimation of lithium-ion batteries in electric drive vehicles using extended Kalman filtering10.1109/TVT.2012.2235474 ↗
11. Robust adaptive sliding-mode observer using RBF neural network for lithium-ion battery state of charge estimation in electric vehicles10.1109/TVT.2015.2427659 ↗
12. A novel mechanical analogy-based battery model for SoC estimation using a Multicell EKF10.1109/TSTE.2016.2574755 ↗
13. Luzi, M., Paschero, M., Rossini, A., Rizzi, A., and Mascioli, F.M.F. (2016, January 23–26). Comparison between two nonlinear Kalman Filters for reliable SoC estimation on a prototypal BMS. Proceedings of the 42nd Annual Conference of the IEEE Industrial Electronics Society, IECON, Florence, Italy.10.1109/IECON.2016.7794054 ↗
14. Zhang, Z.L., Cheng, X., Lu, Z.Y., and Gu, D.J. (2016). SOC estimation of lithium-ion batteries with AEKF and wavelet transform matrix. IEEE Trans. Power Electron., PP.10.1109/TPEL.2016.2636180 ↗
15. Fractional modeling and SOC estimation of lithium-ion battery10.1109/JAS.2016.7508803 ↗
16. Estimation of battery state of charge with H∞ observer: Applied to a robot for inspecting power transmission lines10.1109/TIE.2011.2159691 ↗
17. Soft computing for battery state-of-charge (BSOC) estimation in battery string systems10.1109/TIE.2007.896496 ↗
18. State-of-charge estimation for lithium-ion batteries using neural networks and EKF10.1109/TIE.2010.2043035 ↗
19. Estimation of battery state of health using probabilistic neural network10.1109/TII.2012.2222650 ↗
20. A merged fuzzy neural network and its applications in battery state-of-charge estimation10.1109/TEC.2007.895457 ↗
21. DSP-based probabilistic fuzzy neural network control for Li-ion battery charger10.1109/TPEL.2012.2187073 ↗
22. Zhang, Y., Xiong, R., and He, H. (2016, January 27–29). Evaluation of the model-based state-of-charge estimation methods for lithium-ion batteries. Proceedings of the 2016 IEEE Transportation Electrification Conference and Expo (ITEC), Dearborn, MI, USA.10.1109/ITEC.2016.7520184 ↗
23. Koirala, N., He, F., and Shen, W. (2015, January 15–17). Comparison of two battery equivalent circuit models for state of charge estimation in electric vehicles. Proceedings of the 2015 IEEE 10th Conference on Industrial Electronics and Applications (ICIEA), Auckland, New Zealand.10.1109/ICIEA.2015.7334077 ↗
24. Liu, G., Lu, L., Fu, H., Hua, J., Li, J., Ouyang, M., Wang, Y., Xue, S., and Chen, P. (September, January 31). A comparative study of equivalent circuit models and enhanced equivalent circuit models of lithium-ion batteries with different model structures. Proceedings of the 2014 IEEE Conference and Expo Transportation Electrification Asia-Pacific (ITEC Asia-Pacific), Beijing, China.
25. Berrueta, A., Martín, I.S., Sanchis, P., and Ursúa, A. (2016, January 23–26). Comparison of State-of-Charge estimation methods for stationary Lithium-ion batteries. Proceedings of the 42nd Annual Conference of the IEEE Industrial Electronics Society, IECON, Florence, Italy.10.1109/IECON.2016.7794094 ↗
26. Farag, M.S., Ahmed, R., Gadsden, S.A., Habibi, S.R., and Tjong, J. (2012, January 18–20). A comparative study of Li-ion battery models and nonlinear dual estimation strategies. Proceedings of the 2012 IEEE Transportation Electrification Conference and Expo (ITEC), Dearborn, MI, USA.10.1109/ITEC.2012.6243485 ↗
27. Xu, J., Cao, B., Cao, J., Zou, Z., Mi, C.C., and Chen, Z. (2013, January 15–18). A comparison study of the model based SOC estimation methods for lithium-ion batteries. Proceedings of the 2013 IEEE Vehicle Power and Propulsion Conference (VPPC), Beijing, China.10.1109/VPPC.2013.6671653 ↗
28. Meng, J., Luo, G., and Gao, F. (September, January 31). State-of-charge estimation for lithium-ion battery using AUKF and LSSVM. Proceedings of the 2014 IEEE Conference and Expo Transportation Electrification Asia-Pacific (ITEC Asia-Pacific), Beijing, China.
29. Lagraoui, M., Doubabi, S., and Rachid, A. (2014, January 17–19). SOC estimation of Lithium-ion battery using Kalman filter and Luenberger observer: A comparative study. Proceedings of the 2014 International Renewable and Sustainable Energy Conference (IRSEC), Ouarzazate, Morocco.10.1109/IRSEC.2014.7059849 ↗
30. Chen, X., Shen, W., Cao, Z., and Kapoor, A. (2012, January 18–20). A comparative study of observer design techniques for state of charge estimation in electric vehicles. Proceedings of the 2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA), Singapore.10.1109/ICIEA.2012.6360705 ↗
31. Jian, W., Jiang, X., Zhang, J., Xiang, Z., and Jian, Y. (2012, January 28–30). Comparison of SOC estimation performance with different training functions using neural network. Proceedings of the 2012 UKSim 14th International Conference on Computer Modelling and Simulation, Cambridge, UK.10.1109/UKSim.2012.69 ↗
32. Accurate electrical battery model capable of predicting runtime and I-V performance10.1109/TEC.2006.874229 ↗