Impedance Of Lithium-ion Batteries Research Articles

Systematically Composed Binary Sequence Design Approach for Broadband Lithium-Ion Battery Impedance Measurement

Systematically Composed Binary Sequence Design Approach for Broadband Lithium-Ion Battery Impedance Measurement

Rapid Lithium-Ion Battery Impedance Measurements Using Binary Sequence With Optimized Frequency Components

Rapid Lithium-Ion Battery Impedance Measurements Using Binary Sequence With Optimized Frequency Components

Research on online passive electrochemical impedance spectroscopy and its outlook in battery management

Current lithium-ion battery (LIB) management technique relying solely on the limited time-domain measurements appears to reach its limit, and incorporating new sensing information, particularly the impedance of LIB, offers a promising path for improvement. Using the non-stationary random driving profile of electric vehicle (EV), a method to extract online passive electrochemical impedance spectroscopy (OPEIS) is proposed and validated, with relevant factors influencing its efficacy also investigated. The particularity of actual driving profile is revealed both theoretically and experimentally, and beyond expectation, the highly differentiated driving profiles yield a similar spectral pattern, which facilitates the acquisition of OPEIS. Continuous OPEIS characterized by the distribution of relaxation time (DRT) ranging from 0.2 Hz to 3 kHz are analyzed in detail under different battery conditions. Compared with offline reference EIS, most of the measurement errors are <3%. Based on the acquired spectral information and OPEIS, a safety-relevant detector and an internal temperature estimator for LIB are presented, and they can both realize a near instant electrochemical sensing up to 1 Hz. As the underlying opportunities of OPEIS are outlined, challenges in its reliable acquisition and engineering implementation are evaluated as well. By comprehensively discussing the realization of OPEIS, research in this paper is expected to provide valuable references for a more effective battery management.

Online Monitoring of Lithium-Ion Battery Impedance Using DC–DC Converter Self-Excited Switching Oscillations

Online Monitoring of Lithium-Ion Battery Impedance Using DC–DC Converter Self-Excited Switching Oscillations

Constructing battery impedance spectroscopy using partial current in constant-voltage charging or partial relaxation voltage

Electrochemical impedance spectroscopy (EIS) is an important technique to measure the impedance of lithium-ion batteries. However, practical applications of this technique are hindered by various factors such as expensive equipment costs, prolonged battery relaxation time, and lengthy measurement time, posing significant challenges and limitations. Without the need of an impedance analyzer, this study presents a machine learning (ML) approach by utilizing the current signals in constant voltage (CV) charging or the relaxation voltage (RV) data after charging as the input to construct the complete impedance spectrum of a battery at its full capacity. To validate the robustness and reliability of this approach, various scenarios, including the changes in the data length, the sampling interval, and the ML model, are discussed. We demonstrate that with 600 s of input data, using the CV current yields a root mean square error (RMSE) of 0.84 mΩ, while the RV achieves an even lower RMSE of 0.69 mΩ. With an input data of as short as 30 s, the two respective RMSEs simply increase to 1.94 and 0.82 mΩ. Incorporating the voltage curve in constant current (CC) charging into estimation analysis shows that, with the same data length, both CC and RV inputs yield even more accurate predictions than CV data.

A Novel Lithium-Ion Battery Impedance Fast Measurement Method With Enhanced Excitation Signal

Online battery impedance acquisition is of increasing interest due to its valuable information for state estimation and fault diagnosis. The Pseudo Random Sequence (PRS) can significantly mitigate the required time cost, yet suffers from an insufficient Signal-to-Noise Ratio (SNR). Motivated by this, this paper proposes a novel series-connected Dual Pseudo Random Sequence (DPRS) for enhancing the signal power content in all frequency ranges, which notably improves the robustness of broadband impedance acquisition against interference. A Weighted Bilateral Impedance Filter (WBIF) is further carried out on a semi-logarithm scale to process the measurement data, which effectively compensates for the noise-induced biases and enables a nearly unbiased result. Experimental studies verify the proposed framework by comparing it with the conventional PRS method in terms of accuracy and robustness under various States of Charges (SOCs), ambient temperatures, and aging status of the batteries.

(Keynote) Battery Archeology: Using Physics-Based Models to Unearth (Quantitative) Battery Aging Artifacts

A key question any battery modeler must grapple with is how much physics to include in a physics-based model. An ideal approach was articulated by Albert Einstein as “everything should be made as simple as possible, but not simpler.” Yet, a quick review of the electrochemical literature suggests an alternative is to embrace the Grateful Dead lyric, “too much of everything is just enough.” Finding the right balance between model complexity and experimental complexity is critical for rigorous model-data convergence, and remains an on-going challenge for nearly every battery chemistry.[1]The publications of honoree Ralph White often resolve this dilemma by incorporating impressively comprehensive physics in a model, along with equally impressive mathematical analysis, so a careful reader can determine, for themselves, what physics matters for the situation they care about. An elegant example of this is the analytical solution for the electrochemical impedance spectroscopy (EIS) response of the pseudo-two-dimensional (P2D) model of a porous insertion electrode [2]. Numerical solutions of the P2D model for linear or nonlinear EIS of a battery, as we have shown,[3-5] allows one to capture the behavior of an experimental battery system, but parameter identification challenges do not always allow rigorous validation of the essential physics. Analytical models such as given in [2] can help sort out what parameters, or clustering of parameters, matter.In this talk we tackle the question of model-data convergence, where the goal is modeling that meets Einstein’s ideal so that quantitative physics-based insights can be extracted and interpreted from experimental data. Just as with modeling, experimental measurements should embrace Einstein’s notion of being simple, but not too simple, if we hope to extract meaningful parameters from real batteries. A key experimental concept introduced in this talk is measurement signal parity in full-cell datasets. We show that a single physics-based model simultaneously fit to two datasets (of opposite signal parity) can help discern half-cell processes from full-cell data. We illustrate this, first, using slow cycling data from commercial NMC|C 18650 cells to non-destructively estimate half-cell thermodynamic parameters from the commercial cells as they cycle and age. The cornerstone of our thermodynamic modeling is adaptation of half-cell methods developed at General Motors,[6] but implemented here for full-cell analysis.[7] We also look at second harmonic nonlinear EIS as a negative parity complement to linear EIS (positive parity) data, but we step back from our prior use of computed P2D models;[3, 5] for our current state of knowledge, P2D models may be closer to the Grateful Dead end of second harmonic EIS battery modeling than Einstein’s ideal. To better align our understanding and ability to extract meaningful parameters, we attempt to thoughtfully build up the physics of nonlinear EIS from a basic physical chemistry description of the electrified interface – a Helmholz double layer and Butler-Volmer charge transfer kinetics – while progressively adding solid state transport and electrode geometry to increasingly capture more of the variance of linear (positive parity) and second harmonic nonlinear (negative parity) EIS experimental data without over-fitting. In both the thermo and EIS examples, one appropriate physics-based model is simultaneously fit to two datasets of opposite parity. Working with battery aging data, we show how this approach for aligning modeling physics to complementary datasets of opposite parity can be used to quantify half-cell physicochemical processes that evolve with cycle aging of an engineered commercial full-cell.[1] M. Cornish and M. Marinescu, Toward rigorous validation of Li-S Battery Models, J.E.S. 169 060531 (2022)[2] G. Sikha and R. White, Analytical expression for the impedance response of an insertion electrode cell, J.E.S. 154(1) A43 (2007).[3] M.D. Murbach and D.T. Schwartz, Extending Newman’s P2D lithium-ion battery impedance approach to include the nonlinear harmonic response, J.E.S. 164(11) E3311 (2017).[4] M.D. Murbach and D.T. Schwartz, Analysis of Li-ion battery electrochemical impedance spectroscopy data: an easy-to-implement approach for physics-based parameter estimation using an open-source tool, J.E.S. 165(2) A297 (2018).[5] M.D. Murbach, V.W. Hu, and D.T. Schwartz, Nonlinear electrochemical impedance spectroscopy of lithium-ion batteries: experimental approach, analysis, and initial finding, J.E.S. 165(11) A2758 (2018).[6] M. Verbrugge, D. Baker, B. Koch, X-C Xiao, and W-T. Gu, Thermodynamic model for substitutional materials: Application to lithiated graphite, spinel MnO, LFP, NMC, J.E.S. 164(11) E3243 (2017).[7] V.W. Hu and D.T. Schwartz, Low error estimation of half-cell thermodynamic parameters from whole-cell li-ion battery experiments: Physics-based model formulation, experimental demonstration, and an open software tool, J.E.S. 169(3) 030539 (2022).

A Two-Stage Optimization Framework for Fast Lithium-Ion Battery Impedance Measurement

Online monitoring of battery impedance is of increasing interest as it brings rich information for battery state estimation and fault diagnosis, however, the existing techniques are limited by high implementation costs and poor accuracy. This letter addresses the challenges by proposing a novel two-stage optimization framework to form a binary sequence with the optimal power proportion in specified harmonics for fast battery impedance measurements. The Optimized Binary Sequence (OBS) with finite amplitude levels can greatly simplify the implementation, and also the optimized power spectrum has superb accuracy to obtain the frequency responses. Experimental studies confirm the superiority of the proposed method in comparison with three existing techniques under various circumstances.

Determination of electrochemical impedance of lithium-ion battery from charge curve by wavelet transformation

Lithium-ion batteries (LIBs) are being used as power sources for use in electric vehicles and aircraft, and so on. Early detection technique of battery deterioration is required with the spread adoption of electric vehicles and aircraft. Electrochemical impedance spectroscopy is a powerful method to evaluate the internal state of LIB. We recently have proposed a method to determine the impedance spectra of LIB by wavelet transformation (WT) of the external input and the output signals. In the present paper, the impedance spectra were determined by performing WT on the charge curves of the LIB without an external input signal. A capacitive semicircle and Warburg impedance appeared in the impedance spectrum determined by the WT, which is roughly consistent with the impedance spectrum measured by a frequency response analyzer (FRA). In addition, the new impedance determination method was applied to the evaluation of deteriorated batteries. The diameter of the semicircle increased with fading the battery capacity. Curve fitting was applied to the spectra determined by the WT. The sum of the resistance related to electrode structure, Rc, and the charge transfer resistance, Rct, increased as the capacity loss increased. Moreover, the parameter values were in good agreement with those calculated from the impedance measured using FRA, indicating that this new impedance determination method is useful for evaluation of deteriorated batteries. Furthermore, we emphasize that this method can diagnose the battery deterioration simply by charging the battery.

Regeneration of Lithium-ion battery impedance using a novel machine learning framework and minimal empirical data

The use of Electrochemical Impedance Spectroscopy on rechargeable Lithium-ion battery characterization is an extensively recognized non-destructive procedure for both in-situ and ex-situ analyses. In an impedance measurement for a rechargeable battery, the oscillating current with an accompanying phase angle is the response for a potential perturbation. The proper evaluation of phase angle as a crucial impedance parameter, provides critical understanding of the status of the battery. Although fast and simple, impedance data is difficult to interpret. Using a novel data-centric Machine Learning framework (co-modeling) we demonstrate how to impute experimental data quickly, precisely, and inexpensively that agrees with wholly experimentally generated data. In particular, we predict the phase angle with 99.9% accuracy by training the minimal empirical impedance data. This approach demonstrates a potentially burgeoning field of Machine Learning experimental data imputation and the consequence of faster diagnostic and study of batteries.

Determination of Electrochemical Impedance of Lithium-Ion Battery from Charge Curve by Wavelet Transformation

Determination of Electrochemical Impedance of Lithium-Ion Battery from Charge Curve by Wavelet Transformation

A review of modeling, acquisition, and application of lithium-ion battery impedance for onboard battery management

Impedance is closely related to the internal physical and chemical processes of lithium-ion batteries. And the properties of the processes can be characterized and thus more detailed information be provided based on it. In the past decades, the impedance is frequently reported as a powerful tool used in the field of lithium-ion battery state estimation and diagnosis. This paper reviews more than 170 papers and organizes the related research according to three themes of impedance modeling, acquisition, and application under the premise of electric vehicle implementation. The strength and weaknesses of the research in each theme are discussed. Based on the research, the possibility and the value of impedance in onboard battery management are revealed. However, challenges are still faced due to the cost limitations, the complex vehicular conditions, and the time-varying battery states. The unsolved issues are summarized in the conclusion. To realize a more smart battery management system with the impedance, more significant work is still needed. • Modeling, acquisition, and application of battery impedance are reviewed. • Strength and weaknesses for on-board implementation are discussed. • Unsolved issues are summarized for battery management based on impedance.

Guidelines for the Characterization of the Internal Impedance of Lithium-Ion Batteries in PHM Algorithms

This article aims to describe the most important aspects to consider when using the concept of internal impedance in algorithms that focus on characterizing the degradation of lithium-ion (Li-ion) batteries. The first part of the article provides a literature review that will help the reader understand the concept of electrochemical impedance spectroscopy (EIS) and how Li-ion batteries can be represented through electrochemical or empirical models, in order to interpret the outcome of typical discharge and/or degradation tests on Li-ion batteries. The second part of the manuscript shows the obtained results of an accelerated degradation experiment performed under controlled conditions on a Li-ion cell. Results show that changes observed on the EIS test can be linked to battery degradation. This knowledge may be of great value when implementing algorithms aimed to predict the End-of-Life (EoL) of the battery in terms of temperature, voltage, and discharge current measurements. The purpose of this article is to introduce the reader to several types of Li-ion battery models, and show how the internal impedance of a Li-ion battery is a dynamic parameter that depends on different factors; and then, illustrate how the EIS can be used to obtain an equivalent circuit model and how the different electronic components vary with the use given to the battery.

Novel method for the on-line estimation of low-frequency impedance of lithium-ion batteries

• Impedance estimation with separate algorithm for high- and low-frequency processes. • Theoretical background to observable frequencies of impedance in real cycling data. • Algorithm is based on identification windows, and operates non-recursively. • Voltage error for commercial 18650 NCA cell below 10mV during the validation cycle. Impedance is an important characteristic of lithium-ion batteries since it directly influences their power capability. However, battery impedance is highly dependent on the operating condition and increases over the lifetime of the battery, due to degradation of the latter. Continuous tracking of the impedance can hence provide meaningful insights into the aging status of a battery. However, the on-line determination of battery impedance parameters, especially for its low-frequency part, is a challenging task, which has not yet been solved unambiguously in literature. This work provides an algorithm for the on-line determination of battery impedance, which features a novel approach to quantifying the impedance caused by diffusion processes at low frequencies. The algorithm works by parameterizing an equivalent circuit model comprised of RC elements, which reproduces the Li-ion kinetics. The on-line functionality is enabled by parameterizing the model during parameter identification windows of battery operating data, which allow for the separation of high-frequency and low-frequency dynamics. The developed algorithm is designed in such a way that it can in future be embedded into a low-cost microcontroller by taking into account the relevant computational and memory limitations. During experimental validation with a commercial Li-ion battery, the root-mean-square error of the simulated voltage during diverse static and dynamic loads is reduced by over 50% compared to a benchmark algorithm without the proposed approach.

Determination of electrochemical impedance of lithium ion battery from time series data by wavelet transformation -Uncertainty of resolutions in time and frequency domains-

This paper presents theory and method of impedance determination by the wavelet transformation. The complex Morlet mother wavelet (CMMW), which is a product of sinusoidal wave and Gaussian time-window, was used to calculate the complex numbers of wavelet coefficient from the time domain data. An impedance spectrum was determined from wavelet coefficients transformed from current and voltage time series. The frequency resolution was defined by using the standard deviation, and could be controlled by the band parameter fb in CMMW. The large fb level means high frequency resolution and low time resolution. This fact means an uncertainty of resolutions in time and frequency domains. The proposed impedance determination from simple signals like a current step and the voltage response was applied to lithium ion battery (LIB). An impedance spectrum of LIB was determined form the time series data of discharge current and voltage response by the WT. And the amplitude of frequency resolution was indicated by error bar in the Bode diagram of impedance spectrum.

Sequential Monte Carlo Filter for State-of-Charge Estimation of Lithium-Ion Batteries Based on Auto Regressive Exogenous Model

The state of charge (SOC) of lithium-ion batteries (LIBs) is an important evaluation index in battery management system of energy storage systems. However, it is always a challenging task to accurately estimate SOC of LIBs, because of the existence of nonlinear characteristics and significant temperature effects. To improve the SOC estimation accuracy and robustness, a model-based estimation approach for SOC and impedance of LIBs is proposed against uncertain loading profiles and ambient temperatures. First an auto regressive exogenous model for online model order determining and parameters identification is established to monitor parameters variations based on numerical subspace state space system identification method. Second, a sequential Monte Carlo filter is employed to overcome the nonlinear and non-Gaussian error distribution state estimation problems caused by complicated open-circuit voltage characteristics. Finally, evaluation of the adaptability and generality of the proposed method are verified by different LIBs under different operating conditions. Experimental results indicate that the proposed method shows great performance, whose estimation value converges to real SOC within an error of $\pm \text{3}\%$ , and the battery model can simulate battery dynamics robustly with high accuracy.

Fast Simulation of Nonlinear Electrochemical Impedance of Lithium-Ion Batteries

Electrochemical impedance spectroscopy (EIS) is a widely used technique for monitoring and characterizing lithium-ion batteries. It can be used to obtain significant insights on electrochemical phenomena such as kinetic and mass transport parameters,1 aging effects,2 and the built up of the solid electrolyte interface (SEI).3 Nonlinear electrochemical impedance spectroscopy (NLEIS) is an extension to EIS as it uses a moderately larger amplitude perturbation, such that the battery’s response is in the weakly nonlinear regime. For lithium-ion batteries, higher harmonic responses have been analyzed to study degradation mechanisms such as lithium plating.4 , 5 A model-based approach to examining the variation in second and third harmonic responses to changes in parameters such as diffusion coefficient, reaction rate constant, and double layer capacitance demonstrated the ability of using NLEIS as a characterization and identification tool.6 This is further explored by the increased sensitivity of NLEIS, as compared to EIS, to the battery’s state of charge and state of health, particularly changes in charge transfer symmetry for aged batteries.7 It follows that there is a need to develop computationally fast and accurate tools to model both EIS and NLEIS efficiently for analyzing experimental measurements quantitatively through multi-parameter estimation. A physics-based model to describe the weakly nonlinear response in the frequency domain was introduced as an extension to Doyle’s pseudo two-dimensional (P2D) lithium-ion battery impedance model.8 , 9 In a similar manner to previous work done on solving for the linear impedance response,10 we present a hybrid analytical-collocation approach to solve the model for higher harmonics for NLEIS. The spherical diffusion equations for linear and second harmonic responses are solved analytically for solid phase diffusion in the electrode, while the boundary value problem across the electrodes and separator is solved numerically using orthogonal collocation. We compare the accuracy to the numerical solution obtained by solving the original model in COMSOL, and present an executable that takes parameters and frequencies as inputs and returns both the linear and nonlinear impedance responses for a lithium-ion battery. References P. Yu, B. N. Popov, J. A. Ritter, and R. E. White, J. Electrochem. Soc., 146, 8–14 (1999).U. Tröltzsch, O. Kanoun, and H. R. Tränkler, Electrochim. Acta, 51, 1664–1672 (2006).E. Barsoukov, J. H. Kim, J. H. Kim, C. O. Yoon, and H. Lee, J. Electrochem. Soc., 145, 2711–2717 (1998).N. Harting, N. Wolff, F. Röder, and U. Krewer, Electrochim. Acta, 248, 133–139 (2017).N. Harting, N. Wolff, and U. Krewer, Electrochim. Acta, 281, 378–385 (2018).N. Wolff, N. Harting, M. Heinrich, F. Röder, and U. Krewer, Electrochim. Acta, 260, 614–622 (2018).M. D. Murbach, V. W. Hu, and D. T. Schwartz, J. Electrochem. Soc., 165, A2758–A2765 (2018).M. Doyle, J. P. Meyers, and J. Newman, J. Electrochem. Soc., 147, 99–110 (2000).M. D. Murbach and D. T. Schwartz, J. Electrochem. Soc., 164, E3311–E3320 (2017).M. Pathak, M. D. Murbach, C. Pathak, T.-J. Jang, Y. Qi, D. T. Schwartz, and V. R. Subramanian, J. Electrochem. Soc., 165, A1324–A1337 (2018).

Construction of highly conductive network for improving electrochemical performance of lithium iron phosphate

High performance lithium iron phosphate (LFP) cathode materials were synthesized using amorphous carbon, carbon nanotubes (CNTs), and graphene (G) as conductive materials via sand milling and spray drying processes and followed by calcination. The structural characterizations indicated that CNTs and G can well connected with LFP nanoparticles (NPs), which was coated with a thin layer of amorphous carbon, benefiting the construction of three-dimensional efficient conductive network. The electrochemical measurements confirmed that the introduction of CNTs and G can obviously decrease the charge transfer impedance of lithium ion battery and increase the discharge special capacity. The initial discharge special capacity increases in the order of LFP/C (150.3 mAh/g) < LFP/C/CNTs (155.7 mAh/g) < LFP/C/G (159.7 mAh/g) < LFP/C/G/CNTs (164.5 mAh/g) at 0.1 C. LFP/C/G/CNTs also exhibited considerable discharge special capacity (99.5 mAh/g) at high current of 5 C. Compared with LFP/C (248 Ω), LFP/C/G/CNTs (50 Ω) showed obviously decreased charge transfer impedance. Cycle performance test indicated that the specific capacity retention for LFP/C/G/CNTs was 99% after 200 cycles, displaying good cycle stability. The superior electrochemical performance of LFP/C/G/CNTs is attributed to the significantly synergistic effect of CNTs and G on decreasing charge transfer impedance by constructing highly three-dimensional conductive network. Besides, the effect of G to CNTs mass ratio and the calcination temperature on the electrochemical performance of LIBs were also discussed.

The electrochemical double layer and its impedance behavior in lithium-ion batteries.

Interfacial reaction and transport processes contribute crucially to the overall performance and impedance of electrochemical systems. The influence of the electrochemical double layer and the interfacial reactions on the impedance of lithium intercalation batteries is investigated with a modeling approach. Our generic theory for charge and electron transfer reactions at electrified interfaces and its simplified adaptation, a reduced interface model, are compared with the standard for electrochemical interface modeling, the Butler-Volmer ansatz, in terms of numerically simulated impedance spectra. Both of our interface models inherently provide significant impedance characteristics that go beyond the standard approach due to their theoretically consistent derivation. We discuss resistant and capacitive contributions of the double layer to impedance spectra and analyze the effect of strongly correlated interfacial dynamics.

Practical On-Board Measurement of Lithium Ion Battery Impedance Based on Distributed Voltage and Current Sampling

Battery impedance based state estimation methods receive extensive attention due to its close relation to internal dynamic processes and the mechanism of a battery. In order to provide impedance for a battery management system (BMS), a practical on-board impedance measuring method based on distributed signal sampling is proposed and implemented. Battery cell perturbing current and its response voltage for impedance calculation are sampled separately to be compatible with BMS. A digital dual-channel orthogonal lock-in amplifier is used to calculate the impedance. With the signal synchronization, the battery impedance is obtained and compensated. And the relative impedance can also be obtained without knowing the current. For verification, an impedance measuring system made up of electronic units sampling and processing signals and a DC-AC converter generating AC perturbing current is designed. A type of 8 Ah LiFePO4 battery is chosen and the valuable frequency range for state estimations is determined with a series of experiments. The battery cells are connected in series and the impedance is measured with the prototype. It is shown that the measurement error of the impedance modulus at 0.1 Hz–500 Hz at 5 °C–35 °C is less than 4.5% and the impedance phase error is less than 3% at &lt;10 Hz at room temperature. In addition, the relative impedance can also be tracked well with the designed system.