Comparison and Analysis of SOC Estimation Based on First-Order and Second-Order Thevenin Battery Models Based on EKF

Yue Xia

doi:10.54097/ajst.v6i3.10163

Authors

Yue Xia

DOI:

https://doi.org/10.54097/ajst.v6i3.10163

Keywords:

State of charge, Equivalent circuit model, Parameter identification, Extended Kalman filtering.

Abstract

The accurate modeling and estimation of state-of-charge (SOC) of lithium-ion batteries are of great significance for their utilization efficiency, service life extension and battery management system (BMS) design. This paper aims to provide understanding and guidance for understanding and guidance on different battery models and their parameter selection by comparing and analyzing the performance differences of Extended Kalman Filter (EKF) algorithm based on the first-order Thevenin battery model and the second-order Thevenin battery model. The Battery module in Matlab/Simulink is used to simulate the HPPC (HybridPulse Power Characterization) test of real batteries, and the model is parameterically identified. By establishing Thevenin battery models of different orders and applying the EKF algorithm for SOC estimation, the differences in accuracy, robustness and computational complexity of the two models are compared and analyzed, and experimental verification is carried out under different working conditions.

Downloads

Download data is not yet available.

References

Zhou, L., Cai, D., Yao, G., Yin, Z. (2019). Review of Key Technologies in Battery Management Systems. Battery, 49(04), 338-341. DOI: 10.19535/j.1001-1579.2019.04.018.

Lu, L., Li, J., Hua, J., Ouyang, M. (2016). Key Technologies of Lithium-ion Battery Management System for Electric Vehicles. Science and Technology Review, 34(06), 39-51.

Gao, M., Xu, H., Wu, M. (2021). Review of State-of-Charge Estimation Methods Based on Equivalent Circuit Models for Power Batteries. Journal of Electrical Engineering, 16(01), 90-102.

Ramsey, D., German, R., et al. (2020). Comparison of Equivalent Circuit Battery Models for Energetic Studies on Electric Vehicles. In 17th IEEE Vehicle Power and Propulsion Conference (VPPC).

Zhou, J., Hua, Y., Liu, K., Lan, H., Fan, C. (2019). Research on a High Precision Modeling Solution for Lithium-ion Batteries. Proceedings of the Chinese Society for Electrical Engineering, 39(21), 6394-6403. DOI: 10.13334/j.0258-8013.pcsee.182306.

Milishchuk, R., Bogodorova, T. (2022). Thevenin-based Battery Model with Ageing Effects in Modelica. In 21st IEEE Mediterranean Electrotechnical Conference, 243-248.

Ji, Y., Qiu, S., Li, G. (2020). Simulation of Lithium-ion Battery Second-order RC Equivalent Circuit Model Based on Variable Resistance and Capacitance. Journal of Central South University, 27(09), 2606-2613.

Nejad, S., Gladwin, D.T., et al. (2016). A Systematic Review of Lumped-parameter Equivalent Circuit Models for Real-time Estimation of Lithium-ion Battery States. Journal of Power Sources, 316, 183-196.

Yan, X., Guo, Y., Cui, Y., et al. (2018). Electric Vehicle Battery State of Charge Estimation based on GNL Model Adaptive Kalman Filter. Journal of Physics: Conference Series, 1087(5).

Wang, Z., Li, R., Li, X. (2021). Lithium-ion Battery State of Charge Estimation based on Hybrid AUKF and HIFF. Battery, 51(04), 380-384. DOI: 10.19535/j.1001-1579.2021.04.013.

Zhao, X., Li, M., Yu, Q., Ma, J., Wang, S. (2023). Overview of Lithium-ion Battery State Estimation for Electric Vehicle Power Systems. China Journal of Highway and Transport, 1-39. Retrieved from http://kns.cnki.net/kcms/detail/61.1313.U.20230420.1452.002.html

Hua, Z., Li, J. (2013). Overview of State of Charge Estimation Methods for Electric Vehicle Power Batteries. Power Supply Technology, 37(09), 1686-1689.

Guo, X., Hua, X., Fu, Z., et al. (2018). Kalman Filter State of Charge Estimation Based on Model Parameter Optimization. Journal of Electronic Measurement and Instrumentation, 32(8), 186-192.

Xu, J., Liu, B.H., Wang, X.Y., et al. (2016). Computational Model of 18650 Lithium-ion Battery with Coupled Strain Rate and State of Charge Dependencies. Applied Energy, 172, 180-189.

Wei, M., Li, J.B., Li, Z.Y., et al. (2020). State of Charge Estimation for Lithium-ion Batteries based on Gaussian Process Regression and Unscented Kalman Filter. Energy Storage Science and Technology, 9(4), 1206-1213.

Xia, L., Wang, S.L., Yu, C.M., et al. (2021). Research on SOC Estimation Method of Ternary Lithium-ion Batteries based on AEKF Algorithm. Control Engineering, 28(4), 730-735.

Liao, Y.P., Li, R., Lv, H., Zhang, F.J., Zhao, C.L. (2023). State of Charge Estimation of Lithium Titanate Battery based on Extended Kalman Filter. Power Supply Technology, 47(05), 639-643.