Online Battery Impedance Identification Based on Small AC Signal Injection of Inverter

Battery impedance online identification is a nondestructive measurement technique for battery condition estimation and health diagnosis. However, conventional methods are costly, time-consuming, and can only be performed on offline small cells. Spurred by this, we concentrate on the development of a transparent and novel measurement method that, for the first time, uses a controllable sine-sweep perturbation generated by an existing battery-connected inverter. In this article, through inverter control, a small ac excitation signal with controllable amplitude and target frequency can be transferred to the battery due to power flow. This method eliminates the need to disconnect the battery from the operating system or add an additional excitation circuit, which provides a low-cost, high-availability, and real-time means for online battery impedance identification. Once it is developed as an embedded function in a widely used battery-inverter system and performed online during operation continuously or periodically, it could provide a monitoring system for the whole pack as well as a single cell. Simulations and experiments prove its effectiveness. The online electrochemical impedance spectrum (EIS) tests, conducted on a 24-Ah battery module test bench, were completed within 300 s for a frequency range from 0.1 Hz to 10 kHz and within 50 s for a frequency range from 1 Hz to 1 kHz, respectively, much faster than an electrochemical workstation. The accuracy and dynamic performance of the proposed method are validated by contrasting online EIS results with offline results with cell voltage being 3.4 V, 3.5 V, 3.6 V. Moreover, a series of dynamic impedance spectra across varying discharge rates and state of charge highlights its promising potential for battery state estimation.

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Publication Details

Journal
IEEE Transactions on Power Electronics
Published
2024-09-19
DOI
https://doi.org/10.1109/tpel.2024.3463795
Citations
7
Primary Topic
Advanced Battery Technologies Research
Type
article
Field-Weighted Citation Impact
1.42
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article

Online Battery Impedance Identification Based on Small AC Signal Injection of Inverter

Ding Luo, Min Zhou, Hongyan Qu, Wei Sun et al.
7 citations
IEEE Transactions on Power Electronics
Advanced Battery Technologies Research
1.42
article

Online Battery Impedance Identification Based on Small AC Signal Injection of Inverter

Ding Luo, Min Zhou, Hongyan Qu, Wei Sun, Dong Jiang, Xuan Zhao
article en
7 citations

Abstract

Battery impedance online identification is a nondestructive measurement technique for battery condition estimation and health diagnosis. However, conventional methods are costly, time-consuming, and can only be performed on offline small cells. Spurred by this, we concentrate on the development of a transparent and novel measurement method that, for the first time, uses a controllable sine-sweep perturbation generated by an existing battery-connected inverter. In this article, through inverter control, a small ac excitation signal with controllable amplitude and target frequency can be transferred to the battery due to power flow. This method eliminates the need to disconnect the battery from the operating system or add an additional excitation circuit, which provides a low-cost, high-availability, and real-time means for online battery impedance identification. Once it is developed as an embedded function in a widely used battery-inverter system and performed online during operation continuously or periodically, it could provide a monitoring system for the whole pack as well as a single cell. Simulations and experiments prove its effectiveness. The online electrochemical impedance spectrum (EIS) tests, conducted on a 24-Ah battery module test bench, were completed within 300 s for a frequency range from 0.1 Hz to 10 kHz and within 50 s for a frequency range from 1 Hz to 1 kHz, respectively, much faster than an electrochemical workstation. The accuracy and dynamic performance of the proposed method are validated by contrasting online EIS results with offline results with cell voltage being 3.4 V, 3.5 V, 3.6 V. Moreover, a series of dynamic impedance spectra across varying discharge rates and state of charge highlights its promising potential for battery state estimation.

IEEE Transactions on Power ElectronicsVol. 40(2)
Huazhong University of Science and Technology (CN)
Affordable and clean energy
Openalex Percentile: Top 19%
Advanced Battery Technologies Research
1.42
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