A Novel On-Board Electrochemical Impedance Spectroscopy System for Real-Time Battery Impedance Estimation

Koseoglou, Markos; Tsioumas, Evangelos; Papagiannis, Dimitrios; Jabbour, Nikolaos; Mademlis, Christos

doi:10.1109/tpel.2021.3063506

Cited by 53 publications

(13 citation statements)

References 55 publications

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“…By contrast, for application scenarios (e.g., EVs), where the available energy state plays more important role, the capacity is mostly regarded for SOH measure, which can be defined as

where

and

denote the maximum discharge capacity at the current cycle and the nominal capacity when the battery is fresh. Note that the internal resistance can be gauged by electrochemical impedance spectroscopy (EIS) ( Koseoglou et al., 2021 ), calculated by hybrid pulse power characterization (HPPC) test ( Shu et al., 2020a ), or estimated by advanced identification algorithms (such as genetic algorithm [GA] [ Chen et al., 2019c ] and recursive least square [RLS] [ Shu et al., 2020c ]). The internal resistance can be easily interfered by experimental temperature and state of charge (SOC).…”

Section: Overview Of Soh Prediction Methodsmentioning

confidence: 99%

State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives

Shu

Shen

et al. 2021

iScience

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Summary Accurate state of health (SOH) prediction is significant to guarantee operation safety and avoid latent failures of lithium-ion batteries. With the development of communication and artificial intelligence technologies, a body of researches have been performed toward precise and reliable SOH prediction method based on machine learning (ML) techniques. In this paper, the conception of SOH is defined, and the state-of-the-art prediction methods are classified based on their primary implementation procedure. As an essential step in ML-based SOH algorithms, the health feature extraction methods reported in the literature are comprehensively surveyed. Next, an exhausted comparison is conducted to elaborate the development of ML-based SOH prediction techniques. Not only their advantages and disadvantages of the application in SOH prediction are reviewed but also their accuracy and execution process are fully discussed. Finally, pivotal challenges and corresponding research directions are provided for more reliable and high-fidelity SOH prediction.

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“…By contrast, for application scenarios (e.g., EVs), where the available energy state plays more important role, the capacity is mostly regarded for SOH measure, which can be defined as

where

and

Section: Overview Of Soh Prediction Methodsmentioning

confidence: 99%

State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives

Shu

Shen

et al. 2021

iScience

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“…It can be concluded that EIS is a sensitive measurement, which is challenging to be implemented in BMS and applied online. Recently, some works [10], [12], [16]- [18] have managed to design a low cost prototype for a friendly EIS measurement in battery applications. An impedance-based BMS is designed to measure the impedance of a 50 Ah battery from 1 to 1000Hz [10].…”

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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“…Fig. 1 shows the existing topologies have been used for the EIS measurement [19][20][21]. In the Fig.…”

Section: Introductionmentioning

confidence: 99%

“…In Fig. 1(b), the paper [20] proposed the on-board EIS for estimation of real time battery impedance. However, the proposed topology only has a single function which is to measure the battery impedance for a wide range of frequency.…”

Section: Introductionmentioning

confidence: 99%

Integration of Battery Impedance Spectroscopy With Reduced Number of Components Into Battery Management Systems

Simatupang

Park

2022

IEEE Access

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This paper proposes the integration of battery impedance spectroscopy (BIS) into a battery management system with a reduced number of inductor and switch components compared to existing methods. Moreover, this paper presents an internal preheating mechanism, active state of charge (SoC) equalizer, and BIS without an external power source so that there is no additional component needed. During a BIS measurement, the battery management system controls the switches in order to circulate a periodical current signal and measure the battery voltage terminals. The SoC equalizer is needed to equalize the SoC in each battery to prevent over-charging and over-discharging. The internal preheating mechanism is used during low temperatures to preheat the battery since the battery has a higher resistance at low temperatures. The prototype can measure the battery impedance from 100 -5 kHz with the parameter errors for the internal series resistances of the battery (Rbatt) are 1.59%, 3.72%, and 3.72% and the charge transfer resistances (Rct) are 5.91%, 8.04%, and 6.89% for B1, B2, and B3. While the double layer capacitances (Cdl) parameter errors are 5.62%, 7.29%, and 6.48% for B1, B2, and B3. A 50 kHz switching frequency is used with the equalizing duration of approximately 400s with the 90.67% efficiency. The self-heating energy consumption is 0.21 %/° C from 0 to 13.5 °C.

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A Novel On-Board Electrochemical Impedance Spectroscopy System for Real-Time Battery Impedance Estimation

Cited by 53 publications

References 55 publications

State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives

State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives

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

Integration of Battery Impedance Spectroscopy With Reduced Number of Components Into Battery Management Systems

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