The Sequential Algorithm for Combined State of Charge and State of Health Estimation of Lithium Ion Battery based on Active Current Injection

TL;DR

This paper introduces a sequential algorithm that improves the accuracy and efficiency of estimating the State of Charge and State of Health of lithium-ion batteries by using frequency-separated current injections and filtering techniques.

Contribution

The paper proposes a novel sequential estimation algorithm utilizing frequency scale separation and high-pass filtering to enhance accuracy and reduce computational cost in battery parameter estimation.

Findings

The sequential algorithm outperforms the concurrent estimation method in accuracy.

The method reduces computational costs compared to traditional simultaneous estimation.

Experimental results confirm effectiveness across different temperatures and battery capacities.

Abstract

When State of Charge, State of Health, and parameters of the Lithium-ion battery are estimated simultaneously, the estimation accuracy is hard to be ensured due to uncertainties in the estimation process. To improve the estimation performance a sequential algorithm, which uses frequency scale separation and estimates parameters/states sequentially by injecting currents with different frequencies, is proposed in this paper. Specifically, by incorporating a high-pass filter, the parameters can be independently characterized by injecting high-frequency and medium-frequency currents, respectively. Using the estimated parameters, battery capacity and State of Charge can then be estimated concurrently. Experimental results show that the estimation accuracy of the proposed sequential algorithm is much better than the concurrent algorithm where all parameters/states are estimated simultaneously, and the computational cost can also be reduced. Finally, experiments are conducted under different temperatures to verify the effectiveness of the proposed algorithm for various battery capacities.