Abstract
The State of Charge (SoC) of a lithium-ion battery cannot be measured directly, and its estimation is one of the main challenges in electrification of transport, among others. In this article, we introduce a systematic methodology for designing Luenberger observers for on-line SoC estimation, based on a given second-order Equivalent Circuit Model (ECM) for the battery. We show how the proposed methodology can be augmented with the Linear Quadratic Regulator (LQR) optimal control framework, resulting in a computationally lightweight state estimator that also displays time optimal convergence. An application to Lithium-Titanate Oxide (LTO) battery cells is presented to illustrate the proposed approach.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Yu, Z., Huai, R., Xiao, L.: State-of-charge estimation for lithium-ion batteries using a kalman filter based on local linearization. Energies 8(8), 7854–7873 (2015)
How, D.N., Hannan, M.H. Lipu, Ker, P.J.: State of charge estimation for lithium-ion batteries using model-based and data-driven methods: a review. IEEE Access 7, 136 116–136 136 (2019)
Gyenes, B., Stevens, D., Chevrier, V., Dahn, J.: Understanding anomalous behavior in coulombic efficiency measurements on li-ion batteries. J. Electrochem. Soc. 162(3), A278 (2014)
Rabah, M., Immonen, E., Shahsavari, S., Haghbayan, M.-H., Murashko, K., Immonen, P.: Capacity loss estimation for li-ion batteries based on a semi-empirical model. Anwendungen und Konzepte der Wirtschaftsinformatik, no. 14 (2021)
Immonen, E., Hurri, J.: Incremental thermo-electric CFD modeling of a high-energy lithium-titanate oxide battery cell in different temperatures: a comparative study. Appl. Thermal Eng. 197, 117260 (2021)
Zhou, W., Zheng, Y., Pan, Z., Lu, Q.: Review on the battery model and SOC estimation method. Processes 9(9), 1685 (2021)
Ali, M.U., Zafar, A., Nengroo, S.H., Hussain, S., Junaid Alvi, M., Kim, H.-J.: Towards a smarter battery management system for electric vehicle applications: a critical review of lithium-ion battery state of charge estimation. Energies 12(3), 446 (2019)
Shrivastava, P., Soon, T.K., Idris, M.Y.I.B., Mekhilef, S.: Overview of model-based online state-of-charge estimation using kalman filter family for lithium-ion batteries. Renew. Sustain. Energy Rev. 113, 109233 (2019)
Wang, Y., Tian, J., Sun, Z., Wang, L., Xu, R., Li, M., Chen, Z.: A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems. Renew. Sustain. Energy Rev. 131, 110015 (2020)
Vidal, C., Malysz, P., Kollmeyer, P., Emadi, A.: Machine learning applied to electrified vehicle battery state of charge and state of health estimation: State-of-the-art. IEEE Access 8, 52 796–52 814 (2020)
Lievre, A., Pelissier, S., Sari, A., Venet, P., Hijazi, A.: Luenberger observer for soc determination of lithium-ion cells in mild hybrid vehicles, compared to a kalman filter. In: 2015 Tenth International Conference on Ecological Vehicles and Renewable Energies (EVER), pp. 1–7. IEEE (2015)
Meng, J., Ricco, M., Luo, G., Swierczynski, M., Stroe, D.-I., Stroe, A.-I., Teodorescu, R.: An overview of online implementable SOC estimation methods for lithium-ion batteries. In: 2017 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM) & 2017 International Aegean Conference on Electrical Machines and Power Electronics (ACEMP), pp. 573–580. IEEE (2017)
Rahimi-Eichi, H., Chow, M.-Y.: Adaptive parameter identification and state-of-charge estimation of lithium-ion batteries. In: IECON 2012-38th Annual Conference on IEEE Industrial Electronics Society, pp. 4012–4017. IEEE (2012)
Tang, X., Liu, B., Gao, F., Lv, Z.: State-of-charge estimation for li-ion power batteries based on a tuning free observer. Energies 9(9) (2016). https://www.mdpi.com/1996-1073/9/9/675
Manthopoulos, A., Wang, X.: A review and comparison of lithium-ion battery SOC estimation methods for electric vehicles. In: IECON: The 46th Annual Conference of the IEEE Industrial Electronics Society, vol. 2020, pp. 2385–2392. IEEE (2020)
Turku university of applied sciences e-rallycross car project. https://erallycross.turkuamk.fi/en/main-page/. Accessed 10 Jan. 2022
Immonen, E., Hurri, J.: Equivalent circuit modeling of a high-energy lto battery cell for an electric rallycross car. In: IEEE 30th International Symposium on Industrial Electronics (ISIE), vol. 2021, pp. 1–5. IEEE (2021)
Ferrante, A., Lepschy, A., Viaro, U.: A simple proof of the routh test. IEEE Trans. Autom. Control 44(6), 1306–1309 (1999)
Rotella, F., Zambettakis, I.: On functional observers for linear time-varying systems. IEEE Trans. Autom. Control 58(5), 1354–1360 (2012)
Sontag, E.D.: Mathematical Control Theory: Deterministic Finite Dimensional Systems, vol. 6. Springer Science & Business Media (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Immonen, E. (2023). Luenberger Observer Design for Robust Estimation of Battery State of Charge with Application to Lithium-Titanate Oxide Cells. In: Theilliol, D., Korbicz, J., Kacprzyk, J. (eds) Recent Developments in Model-Based and Data-Driven Methods for Advanced Control and Diagnosis. ACD 2022. Studies in Systems, Decision and Control, vol 467. Springer, Cham. https://doi.org/10.1007/978-3-031-27540-1_3
Download citation
DOI: https://doi.org/10.1007/978-3-031-27540-1_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-27539-5
Online ISBN: 978-3-031-27540-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)