Multi-tone Excitation Signal Optimization Based on Particle Swarm Optimization for Lithium-ion Battery EIS Measurement

Chao Li; Shunli Han; Luo Zhao; Zunheng Yang; Fei Li

doi:10.54097/g62qvf39

Authors

Chao Li National Key Laboratory of Science and Technology on Electronic Test and Measurement, The 41st Institute of China Electronic Technology Group Corporation, Qingdao, China
Shunli Han National Key Laboratory of Science and Technology on Electronic Test and Measurement, The 41st Institute of China Electronic Technology Group Corporation, Qingdao, China
Luo Zhao National Key Laboratory of Science and Technology on Electronic Test and Measurement, The 41st Institute of China Electronic Technology Group Corporation, Qingdao, China
Zunheng Yang National Key Laboratory of Science and Technology on Electronic Test and Measurement, The 41st Institute of China Electronic Technology Group Corporation, Qingdao, China
Fei Li National Key Laboratory of Science and Technology on Electronic Test and Measurement, The 41st Institute of China Electronic Technology Group Corporation, Qingdao, China

DOI:

https://doi.org/10.54097/g62qvf39

Keywords:

Lithium-ion battery, Electrochemical Impedance Spectroscopy, Multisine superposition excitation, Particle swarm optimization

Abstract

Electrochemical impedance spectroscopy (EIS) is a vital non-destructive method for lithium-ion battery state detection. Traditional single-tone sweep EIS suffers from long test duration, while fixed multisine excitation causes severe spectrum leakage and low-frequency impedance measurement error, failing to support dynamic online battery monitoring. To address these issues, this paper proposes a particle swarm optimization (PSO) strategy to optimize the amplitude and phase of multisine excitation harmonics. Taking full-band impedance relative error as the optimization objective, safe amplitude constraints are adopted to avoid additional battery polarization. Optimized parameters are embedded into a self-developed differential synchronous sampling EIS hardware system for fast and high-precision measurement. Comparative experiments of three excitation schemes are conducted under static and dynamic working conditions. The results show that the proposed method reduces EIS test time to less than 8 seconds, with static measurement error below 1.0% and dynamic error controlled within 1.6%. This optimized multisine excitation scheme realizes efficient and accurate EIS detection, providing a reliable technical support for online condition monitoring of vehicle and energy storage lithium-ion batteries.

Downloads

Download data is not yet available.

References

[1] Barré, A., Deguilhem, B., Grolleau, S., Gérard, M., Suard, F., & Riu, D. (2013). A review on lithium-ion battery ageing mechanisms and estimations for automotive applications. Journal of Power Sources, 241, 680-689. https://doi.org/10.1016/j.jpowsour.2013.05.040

[2] Birkl, C. R., Roberts, M. R., McTurk, E., Bruce, P. G., & Howey, D. A. (2017). Degradation diagnostics for lithium ion cells. Journal of Power Sources, 341, 373-386. https://doi.org/10.1016/j.jpowsour.2016.12.011

[3] Lourenco, J., Silva, J., & Santos, P. (2024). Development of an impedance monitoring system for rechargeable lithium batteries. In 2024 IEEE International Conference on Industrial Technology (ICIT) (pp. 1128-1133). IEEE. https://doi.org/10.1109/ICIT58233.2024.10540968

[4] Zhang, Z., Yang, S., & Jiang, J. (2025). Multiband multisine excitation signal for online impedance spectroscopy of battery cells. Energies, 18(5), Article 987. https://doi.org/10.3390/en18050987

[5] Chu, C., & Onori, S. (2025). Frequency domain parameterization of ECMs for Li-ion SOH estimation. Journal of The Electrochemical Society, 172(5), Article 050513. https://doi.org/10.1149/1945-7111/adc211

[6] Fan, C. X., Zhang, K., Peng, Q., Tang, L., & Wang, Y. (2025). Fast characterization of lithium-ion battery impedance and nonlinearity using optimized multisine perturbation signal. Journal of Power Sources, 586, Article 233689. https://doi.org/10.1016/j.jpowsour.2023.233689

[7] Guo, Z. G., Wang, Q., Yang, W., Liu, J., & Chen, X. (2022). A multi-sine excitation signal optimization strategy for rapid measurement of battery impedance spectrum in time domain. In 2022 IEEE 17th Conference on Industrial Electronics and Applications (ICIEA) (pp. 1624-1629). IEEE. https://doi.org/10.1109/ICIEA54703.2022.10006195

[8] Marčič, T., Mirošević, M., & Kovačević, B. (2023). PSO-based identification of the Li-ion battery cell parameters. Energies, 16(10), Article 3995. https://doi.org/10.3390/en16103995

[9] Vargas, C., Orts-Grau, S., Abadía, J., Gimeno-Sales, F. J., & Escribá, S. (2024). Optimizing lithium-ion battery modeling: A comparative analysis of PSO and GWO algorithms. Energies, 17(8), Article 822. https://doi.org/10.3390/en17080822

[10] Zhang, L., Li, Y., & Chen, H. (2025). Converter-based online battery impedance spectrum measurement using a random low-frequency-modulated multisine excitation. IEEE Transactions on Transportation Electrification, 11(2), 6763-6774. https://doi.org/10.1109/TTE.2025.3529480