State of Charge Estimation of Lithium-ion Batteries Considering Model and Parameter Uncertainties

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Zhimin Xi Rong Jing Xiao Guang Yang Ed Decker

Abstract

Up to date, model and parameter uncertainties are generally overlooked by majority of researchers in the field of battery study. As a consequence, accuracy of the SOC estimation is dominated by the model fidelity and may vary from cell-to- cell. This paper proposes a systematic framework with associated methodologies to quantify the battery model and parameter uncertainties for more effective battery SOC estimation. Such a framework is also generally applicable for estimating other battery performances of interest (e.g. capacity and power capability). There are two major benefits using the proposed framework: i) consideration of the cell-to-cell variability, and ii) accuracy improvement of the low fidelity model comparable to the high fidelity without scarifying computational efficiency. One case study is used to demonstrate the effectiveness of the proposed framework.

How to Cite

Xi, . Z. ., Jing, R. ., Guang Yang, X. ., & Decker, E. . (2013). State of Charge Estimation of Lithium-ion Batteries Considering Model and Parameter Uncertainties. Annual Conference of the PHM Society, 5(1). https://doi.org/10.36001/phmconf.2013.v5i1.2310
Abstract 15 | PDF Downloads 30

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Keywords

State of Charge, Lithium-ion battery, model uncertainty, parameter uncertainty

References
Andre, D., Nuhic, A., Guth, T.S., and Sauer, D.U., (2012), Comparative study of a structured neural network and an extended Kalman filter for state of health deternimation of lithium-ion batteries in hybrid electric vehicles. Engineering Applications of Artificial Intelligence, v26, n 3, p951-961.

He, H., Xiong, R., Guo, H., and Li, S. (2012), Comparision study on the battery models used for the energy management of batteries in electric vehicles. Energy Conversion and Management, v64, p113-121.

He, W., Williard, N., Chen, C., and Pecht, M., (2012), State of charge estimation for electric vehicle batteries under an adaptive filtering framework. Prognostics & System Health Management Conference, Beijing, China, 2012.

He Y., Liu X.T., Zhang C.B., Chen Z.H., 2013, A new model for State-of-Charge (SOC) estimation for high- power Li-ion batteries. Applied Energy, v101, p808– 814.

Hu C., Youn B.D., and Chung J., (2012), A multiscale framework with extended Kalman filter for lithium-ion battery SOC and capacity estimation. Applied Energy, v92, p694–704.

Lee S., Kim J., Lee J., and Cho B.H., (2011), Discrimination of Li-ion batteries based on Hamming network using discharging–charging voltage pattern recognition for improved state-of-charge estimation. Journal of Power Sources, v196, n4, p2227–2240.

Orchard, M.E., and Vachtsevanos, G.J., (2009), A particle- filtering approach for on-line fault diagnosis and failure prognosis. Transctions of the Institute of Measurement and Control, v31, n3/4, p221-246.

Plett, G.L., (2004), Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs Part 2. Modeling and identification. Journal of Power
Sources, v134, n2, p262-276.

Plett, G.L., (2004), Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs Part 3. State and parameter estimation. Journal of Power Sources, v134, n 2, p277–292.

Ng, K.S., Moo, C.S., Chen, Y.P., and Hsieh, Y.C., (2008), Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries. Applied Energy, v86, p1506-1511.

Santhanagopalan, S., and White, R.E., (2008), State of charge estimation for electrical vehicle batteries. IEEE, 17th International Conference on Control Applications, Part of 2008 IEEE Multi-conference on System and Control, San Antonio, Texas, USA, September 3-5, 2008.

Santhanagopalan S., and White R.E., (2010), State of charge estimation using an unscented filter for high power lithium ion cells. International Journal of Energy Research, v34, n2, p152–163.

Youn, B.D., Xi, Z., and Wang, P., (2008), Eigenvector Dimension-Reduction (EDR) method for sensitivity- free probability analysis. Structural and Multidisciplinary Optimization, v37, p13-28.
Section
Technical Papers