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A practical solution for the state of charge estimation in lithium-ion batteries considering disturbance and errors in the measurement sensors

Year: 2025
Type: article
Abstract: In lithium-ion battery management systems (BMS), state of charge (SoC) is a key parameter that is used in most other functions. This paper introduces a different approach from the past presented methods and tackles the problems of model uncertainties, stochastic disturbances, measurement errors, and nonlinearity problems with an adaptive Hꝏ unknown input observer (AHUIO). In the proposed algorithm, model uncertainties are considered as unknown terms in the battery model, the effect of which is removed by a UIO. Furthermore, the effect of stochastic disturbances during the measurement and the battery operation is eliminated using the Hꝏ mechanism. Besides, an adaptive law guarantees asymptotic stability as well as sensor error estimation. In this way, the battery SoC is estimated with no sensor error effects. More specifically, during the stability proof, a special stability condition is considered, and using an adaptive law, the problem is reformed into a linear matrix inequality (LMI) problem. Finally, in solving the LMI problem utilizing the YALMIP toolbox, the estimator parameters are designed in such a way the asymptotic stability and Hꝏ criteria are guaranteed. Using experimental tests for 2.4 Ah fresh and aged lithium battery, the effectiveness of the AHUIO is validated, and the results confirm that the AHUIO algorithm has an estimation error of 0.2 % and 0.42 % for both cells. Besides, the effect of sensor errors on the SoC estimation has been investigated, and the results confirmed the superiority of the proposed method in sensor error elimination in contrast with the other existing methods.
DOI: 10.1016/j.rineng.2025.105085
Language: English
Open Access status: gold
Estimated APC paid: $1,940
OpenAlex ID: W4409799255
FWCI: 1.773
Cites: 41
Cited by: 7
Related to: 10