A Systematic Framework for Battery Performance Estimation Considering Model and Parameter Uncertainties

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Published Nov 1, 2020
Rong Jing Zhimin Xi 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 diagnostics and prognostics. As a consequence, accuracy of
the battery performance estimation is dominated by the model fidelity and may vary from cell-to-cell. This paper proposes a systematic framework to quantify battery model and parameter uncertainties for more effective battery performance estimation. Such a framework is generally applicable for estimating various battery performances of interest (e.g. state of charge (SOC), capacity, and power capability). Case studies for battery SOC estimation are conducted to demonstrate the effectiveness of the proposed framework.

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Keywords

extended Kalman filter, Battery SoC, model uncertainty, parameter uncertainty, battery diagnostics

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.
Chen, Z., Mi, C.C., Fu, Y., Xu, J., Gong, X., (2013), Online battery state of health estimation based on Genetic Algorithm for electric and hybrid vehicle applications, Journal of Power Sources, v240, p184-192.
Han, J., Kim, D., Sunwoo, M., (2009), State-of-charge estimation of lead-acid batteries using an adaptive extended Kalman filter, Journal of Power Sources, v188 (2), p606-612.
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 W., Williard N., Chen C., and Pecht M., (2013), State of Charge Estimation for Electric Vehicle Batteries using Unscented Kalman Filtering, Microelectronics Reliability, v53, n6, p840–847.
He Y., Liu X.T., Zhang C.B., Chen Z.H., 2013, A new model for State-of-Charge (SOC) estimation for high-power Liion 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.
Hu C., Jain G., Tamirisa P., and Gorka T., (2014a), Method for Estimating Capacity and Predicting Remaining Useful Life of Lithium-Ion Battery, Applied Energy, v126, p182–189.
Hu C., Jain G., Zhang P., Schmidt C., Gomadam P., and Gorka T., (2014b), Data-Driven Approach Based on Particle Swarm Optimization and K-Nearest Neighbor Regression for Estimating Capacity of Lithium-Ion Battery, Applied Energy, v129, p49–55.
Jun, M., Smith, K., Wood, E., and Smart, M., (2012), Battery Capacity Estimation of Low-Earth Orbit Satellite Application, International Journal of Prognostics and Health Management, v3(2), pages: 9.
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.
Li, J., Klee Barillas, J., Guenther, C., Danzer, M.A., (2013), A comparative study of state of charge estimation algorithms for LiFePO4 batteries used in electric vehicles, Journal of Power Sources, v230, p244-250.
Li, J., Klee Barillas, J., Guenther, C., Danzer, M.A., (2014), Sequential Monte Carlo filter for state estimation of LiFePO4 batteries based on an online updated model, Journal of Power Sources, v247, p156-162.
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.
Orchard, M.E., and Vachtsevanos, G.J., (2009), A particlefiltering approach for on-line fault diagnosis and failure prognosis. Transctions of the Institute of Measurement and Control, v31, n3/4, p221-246.
Orchard, M.E., Cerda, M., Olivares, B., and Silva, J.F., (2012), Sequential Monte Carlo methods for Discharge Time Prognosis in Lithium-Ion Batteries, International Journal of Prognostics and Health Management, v3(2), pages: 12.
Plett, G.L., (2004a), 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., (2004b), 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.
Rasmussen, C.E. and Williams, C.K.I., (2006), Gaussian processes for machine learning, the MIT Press.
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.
Truchot, C., Dubarry, M., Liaw, B.Y., (2014), State-ofcharge estimation and uncertainty for lithium-ion battery strings, Applied Energy, v119, p218-227.
Waag W., and Sauer D.U., (2013), Adaptive estimation of the electromotive force of the lithium-ion battery after current interruption for an accurate state-of-charge and capacity determination, Applied Energy, v111, p416–427.
Wang D., Miao Q., and Pecht M., (2013), Prognostics of lithium-ion batteries based on relevance vectors and a conditional three-parameter capacity degradation model, Journal of Power Sources, v239, p253–264.
Wang, P., Youn, B.D., Xi, Z., and Kloess A., (2009), Bayesian reliability analysis with evolving, insufficient, and subjective data sets. Journal of Mechanical Design, v131(11), 111008 (11 pages).
Xi, Z., Fu, Y., and Yang, R.J., (2013a), Model bias characterization in the design space under uncertainty, International Journal of Performability Engineering, v9(4), p433-444.
Xi, Z., Fu, Y., and Yang, R.J, (2013b), An Ensemble Approach for Model Bias Prediction, SAE Int. J. Mater. Manf., v6 (3), p532-539.
Xing, Y., He, W., Pecht, M., Tsui, K.L., (2014), State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures, Applied Energy v113, p106-115.
Xiong R., Sun F., Chen Z., and He H., (2014), A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion polymer battery in electric vehicles, Applied Energy, v113, p463–476.
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.
Youn, B.D., Jung, B.C., Xi, Z., Kim, S.B., and Lee, W.R., (2011), A hierarchical framework for statistical model calibration in engineering product development, Computer Methods in Applied Mechanics Engineering, v200, p1421-1431.
Zhan, Z., Fu, Y., Yang, R., Xi, Z., and Shi, L., (2012), A Bayesian Inference based Model Interpolation and Extrapolation, SAE International Journal of Materials and Manufacturing, v5, p357-364.
Zhang, X., and Pisu, P., (2014), An Unscented Kalman Filter based on-line Diagnostic approach for PEM fuel cell Flooding, International Journal of Prognostics and Health Management, v5(1), pages: 18.
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Technical Papers