Full Article: PDF
Scientific Object Identifier: http://s-o-i.org/1.1/TAS-05-97-49
DOI: https://dx.doi.org/10.15863/TAS.2021.05.97.49
Language: English
Citation: Bobobee, E. D., et al. (2021). State of charge estimation of high-power lithium-ion batteries with improved equivalent circuit modeling and adaptive extended Kalman filtering algorithm. ISJ Theoretical & Applied Science, 05 (97), 248-268. Soi: http://s-o-i.org/1.1/TAS-05-97-49 Doi: https://dx.doi.org/10.15863/TAS.2021.05.97.49 |
Pages: 248-268
Published: 30.05.2021
Abstract: This paper focuses on the accurate estimation of the state of charge of lithium-ion batteries through the establishment of an equivalent model, experimentation, simulation, and the use of an adaptive extended Kalman filtering algorithm. Several models have been used in the creation of the high-power lithium-ion battery and as it is difficult to estimate the state of charge of the lithium-ion battery accurately numerous methods and techniques are employed. A Thevenin equivalent circuit model is designed to include two resistor-capacitors in series for easy parameterization and estimation of the state of charge of the battery. An experimental approach is adopted and data from the open-circuit voltage and the hybrid pulse power characteristic tests are used for parameterization. The battery is modeled and simulated in Simulink/MATLAB with inputs from the results and calculations from the experimental data. An improved adaptive extended Kalman filtering algorithm was used to accurately estimate the state of charge. The main idea of using the improved adaptive algorithm is to update the statistical noise covariance parameters and to improve the estimation performance and accuracy. This reduced the interference of system noise effectively and minimized estimation error to the smallest value. An extended Kalman filtering algorithm was employed alongside the adaptive extended Kalman filtering algorithm to verify the effectiveness of the adaptive algorithm. Results and computations from the experiment and simulation are compared and the results show that the improved adaptive extend Kalman filtering algorithm has good convergence speed, is more stable, and has a high precision of accuracy in the estimation of the state of charge. The maximum estimation error realized with the use of the extended Kalman filtering algorithm was 4.97%, and the maximum estimation error based on the use of the improved adaptive extended Kalman filtering algorithm was 1.85%. The results, therefore, show that the adaptive algorithm adopted in this paper can be used efficiently and effectively for the accurate state of charge estimation of the high-power lithium-ion battery.
Key words: high-power lithium-ion batteries; battery management system; Thevenin equivalent circuit model; state of charge; adaptive extended Kalman filtering algorithm.
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