Development of a battery real-time state of health diagnosis based on fast impedance measurements

Locorotondo, Edoardo; Cultrera, Vincenzo; Pugi, Luca; Berzi, Lorenzo; Pierini, Marco; Lutzemberger, Giovanni

doi:10.1016/j.est.2021.102566

Cited by 80 publications

(18 citation statements)

References 46 publications

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“…With the continuous expansion of its application range, the research on battery management systems (BMS) has received increasing attention for the reliable operation of lithium-ion batteries. [9][10][11][12][13] In the BMS, the accurate estimation of the state-ofcharge (SOC) is very important, which is a key factor for safe charging-discharging and accurate life estimation of the battery pack. [14][15][16][17] Lithium-ion batteries mostly work in complex environments [18][19][20][21][22] ; these working environments and sensor measurement errors will increase the difficulty of accurate SOC estimation.…”

Section: Introductionmentioning

confidence: 99%

“…Due to the advantages of high energy density, long service life, and low environmental pollution, lithium‐ion batteries are widely used in new energy vehicles, special robots, and aerospace fields. With the continuous expansion of its application range, the research on battery management systems (BMS) has received increasing attention for the reliable operation of lithium‐ion batteries 9–13 . In the BMS, the accurate estimation of the state‐of‐charge (SOC) is very important, which is a key factor for safe charging‐discharging and accurate life estimation of the battery pack 14–17 …”

Section: Introductionmentioning

confidence: 99%

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Fuzzy adaptive singular value decomposition cubature Kalman filtering algorithm for lithium‐ion battery state‐of‐charge estimation

Yang

Wang

et al. 2021

Circuit Theory & Apps

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To solve the problem of the slow convergence speed for the battery stateof-charge estimation of cubature Kalman filter algorithm, the ternary lithium-ion battery is taken as the research object, and an algorithm combining the fuzzy self-adaptation and singular value decomposition cubature Kalman filtering is proposed. The algorithm takes the system innovation and its change rate as the fuzzy input and the output as the adjustment factor, which is used to adjust the process noise covariance matrix R. The Kalman gain is adjusted through the fuzzy control of R. To ensure the stability of the algorithm in the calculation process, the singular value decomposition is applied to cubature Kalman algorithm. Then, a second-order RC equivalent circuit model with double internal resistance is built and tested under different conditions to verify the rationality of the improved algorithm. The verification results show that under the simple condition, the convergence speed of the proposed algorithm in the different initial state-of-charge values increased by 40.00% and 25.00%, the maximum estimation error of the state-of-charge is 2.52% and 2.51%, the Mean Absolute Error is 0.816% and 0.880%, and the Root Mean Square Error is 1.276% and 1.380%. When the initial state-of-charge value is 0.8, the convergence speed in the complex condition is increased by about 30.00%; the maximum estimation result error, Mean Absolute Error, and Root Mean Square Error are 2.21%, 0.222%, and 1.327%, respectively. When the initial state-of-charge value is 0.6, the convergence speed in the complex condition is increased by about 10.00%; the maximum estimation result error, Mean Absolute Error, and Root Mean Square Error are 2.72%, 0.941%, and 1.327%, respectively. Without reducing the estimation accuracy, the improved algorithm can significantly increase the convergence speed of predictive value tracking, which provides a theoretical basis for the wide application of lithium-ion batteries.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fuzzy adaptive singular value decomposition cubature Kalman filtering algorithm for lithium‐ion battery state‐of‐charge estimation

Yang

Wang

et al. 2021

Circuit Theory & Apps

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“…Electrochemical Impedance Spectroscopy (EIS) can provide fruitful information for the electrode kinetics, which has a great potential to be used for the Li-ion battery SOX estimation [3]- [5], the degradation pattern identification [6], [7], internal temperature estimation [8], [9], and safety monitoring [10], [11]. For example, over 20,000 EIS measurements from 45mAh Li-ion cells can accurately reflect the battery degradation pattern by Gaussian process regression in [6].…”

Section: Introductionmentioning

confidence: 99%

“…The distribution of relaxation times is analyzed in [7] to diagnose the degradation mechanisms in the electrodes of the cell. Few specially selected frequencies are chosen to diagnose the battery SOH in [5], which indicates EIS could be possibly used in batteries' second-life applications. The authors in [11] use AC impedance to predict the overcharge and thermal runaway of the Li-ion battery.…”

Section: Introductionmentioning

confidence: 99%

A Fast Impedance Measurement Method for Lithium-Ion Battery Using Power Spectrum Property

Peng

Meng

et al. 2023

IEEE Trans. Ind. Inf.

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Electrochemical Impedance Spectroscopy (EIS) can provide fruitful information for Lithium-ion (Li-ion) battery modelling and diagnosis, yet EIS measurement is time-consuming with low-frequency signal injection. By stacking a group of broadband signals, Pseudo Random Sequence (PRS) makes it possible to obtain the battery EIS in a few seconds at the expense of measurement accuracy and Signal-to-Noise Ratio (SNR). Thus, this paper focuses on developing a highly effective signal processing procedure to extract useful information from the PRS for accurate EIS measurement. To enhance the ability of the data cleaning procedure, a three-dimensional cloud is firstly reconstructed for each impedance by integrating its Power Spectrum (PS). The impedance with lower PS can be easily removed through a statistical based multiple selection mechanism, which enables the extraction of the EIS without altering the original measurement. Experimental results on a 3000mAh Li-ion battery prove the effectiveness of the proposed method.

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“…In this case the stimulus is referred to as a multitone or multisine. Other complex waveforms can be used to find impedances at multiple frequencies, for example a pseudo-random binary sequence (PRBS) [14], [15], although there is a tradeoff between increasing numbers of frequencies and the signal-to-noise ratio (SNR) of the measurement.…”

Section: Introductionmentioning

confidence: 99%

Achieving Reliable and Repeatable Electrochemical Impedance Spectroscopy of Rechargeable Batteries at Extra-Low Frequencies

Dunn

Scott

2022

IEEE Trans. Instrum. Meas.

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There is a need for techniques for efficient and accurate measurement of the impedance of rechargeable batteries at extra-low frequencies (ELFs, of the order of microhertz), as these reflect real usage and cycling patterns, and their importance in fractional battery circuit modeling is becoming increasingly apparent. Major impediments include the time required to perform such measurements, and 'drift' in impedance values when measurements are taken from the same battery at different times. Moreover, commercial impedance analyzers are generally unable to measure at these frequencies. We describe here our use of programmable two-quadrant power supplies to deliver multiple small signal measurement tones in the presence of large signal 'working' currents, and our use of these data to generate impedance measurements with good precision and in reasonable time. The improvement in quality of electrochemical impedance spectroscopy data is verified through root mean square error when fitting equivalent-circuit models.

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Development of a battery real-time state of health diagnosis based on fast impedance measurements

Cited by 80 publications

References 46 publications

Fuzzy adaptive singular value decomposition cubature Kalman filtering algorithm for lithium‐ion battery state‐of‐charge estimation

Fuzzy adaptive singular value decomposition cubature Kalman filtering algorithm for lithium‐ion battery state‐of‐charge estimation

A Fast Impedance Measurement Method for Lithium-Ion Battery Using Power Spectrum Property

Achieving Reliable and Repeatable Electrochemical Impedance Spectroscopy of Rechargeable Batteries at Extra-Low Frequencies

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