Parameterization of linear equivalent circuit models over wide temperature and SOC spans for automotive lithium-ion cells using electrochemical impedance spectroscopy

Skoog, Stefan; David, Sandeep Nital

doi:10.1016/j.est.2017.08.004

Cited by 53 publications

(37 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, this third semicircle has not been investigated because only frequencies above 1 Hz have been fitted. Thus, we selected the number of RQ networks (see Figure 2) depending on the number of semicircles we could identify in the frequency range of interest (as is also found in Reference [35]). In these conditions, the most appropriate electrical model to describe the behavior of these half-cells at 25 • C is composed of an inductive part (L), a pure resistive component (R 1 ), and two RQ networks ( Figure 2).…”

Section: Negative Electrode (Graphite/li Half-cell)mentioning

confidence: 99%

Impedance Characterization of an LCO-NMC/Graphite Cell: Ohmic Conduction, SEI Transport and Charge-Transfer Phenomenon

Ovejas

Cuadras

2018

Batteries

View full text Add to dashboard Cite

Currently, Li-ion cells are the preferred candidates as energy sources for existing portable applications and for those being developed. Thus, a proper characterization of Li-ion cells is required to optimize their use and their manufacturing process. In this study, the transport phenomena and electrochemical processes taking place in LiCoO2-Li(NiMnCo)O2/graphite (LCO-NMC/graphite) cells are identified from half-cell measurements by means of impedance spectroscopy. The results are calculated from current densities, instead of absolute values, for the future comparison of this data with other cells. In particular, impedance spectra are fitted to simple electrical models composed of an inductive part, serial resistance, and various RQ networks—the parallel combination of a resistor and a constant phase element—depending on the cell. Thus, the evolution of resistances, capacitances, and the characteristic frequencies of the various effects are tracked with the state-of-charge (SoC) at two aging levels. Concretely, two effects are identified at the impedance spectrum; one is clearly caused by the charge transfer at the positive electrode, whereas the other one is presumably caused by the transport of lithium ions across the solid electrolyte interphase (SEI) layer. Moreover, as the cells age, the characteristic frequency of the charge transfer is drastically reduced by a factor of around 70%.

show abstract

Section: Negative Electrode (Graphite/li Half-cell)mentioning

confidence: 99%

Impedance Characterization of an LCO-NMC/Graphite Cell: Ohmic Conduction, SEI Transport and Charge-Transfer Phenomenon

Ovejas

Cuadras

2018

Batteries

View full text Add to dashboard Cite

show abstract

“…Additionally, work to parameterize the battery system for dynamic simulations and loss evaluations in hybrid and pure electric vehicles has been done in [34], [37]- [40]. Here, two main approaches can be distinguished, the recursive time-domain parameter extraction using LF discharge/charge pulses [38], [39] and the electrochemical impedance spectroscopy (EIS) [34].…”

Section: Battery Modeling and Parameter Extraction For Drive Cycle Lomentioning

confidence: 99%

Battery Modeling and Parameter Extraction for Drive Cycle Loss Evaluation of a Modular Battery System for Vehicles Based on a Cascaded H-Bridge Multilevel Inverter

Theliander

Kersten

Kuder

et al. 2020

IEEE Trans. on Ind. Applicat.

View full text Add to dashboard Cite

This article deals with the modeling and the parameterization of the battery packs used in cascaded H-bridge multilevel propulsion inverters. Since the battery packs are intermittently conducting the motor currents, the battery cells are stressed with a dynamic current containing a substantial amount of low-order harmonic components up to a couple of kHz, which is a major difference in comparison to a traditional two-level inverter drive. Different models, such as pure resistive and dynamic RC-networks, are considered to model the energy losses for different operating points (OPs) and driving cycles. Using a small-scale setup, the models' parameters are extracted using both a low-frequency, pulsed current, and an electrochemical impedance spectroscopy (EIS) sweep. The models are compared against measurements conducted on the small-scale setup at different OPs. Additionally, a drive cycle loss comparison is simulated. The simple resistive model overestimates the losses by about 20% and is, thus, not suitable. The dynamic three-time-constant model, parameterized by a pulsed current, complies with the measurements for all analyzed OPs, especially at low speed, with a maximum deviation of 3.8%. Extracting the parameters using an EIS seems suitable for higher speeds, though the losses for the chosen OPs are underestimated by 1.5%-7.9%.

show abstract

“…However, the relationship between the number of RC networks and model accuracy remains unresolved. In fact, with the increment of RC networks, the accuracy of model may not be better [14]. Worse more, excessive RC networks increase the complexity of the model.…”

Section: Introductionmentioning

confidence: 99%

Lithium-Ion Battery Prognostics with Hybrid Gaussian Process Function Regression

et al. 2018

View full text Add to dashboard Cite

The accurate prognostics of lithium-ion battery state of health (SOH) and remaining useful life (RUL) have great significance for reducing the costs of maintenance. The methods based on the physical models cannot perform satisfactorily as the systems become more and more complex. With the development of digital acquisition and storage technology, the data of battery cells can be obtained. This makes the data-driven methods get more and more attention. In this paper, to overcome the problem that the trend fitting deteriorates rapidly when test data are far from the training data for multiple-step-ahead estimation, a prognostic method fusing the wavelet de-noising (WD) method and the hybrid Gaussian process function regression (HGPFR) model for predicting the RUL of the lithium-ion battery is proposed. Gaussian process regression (GPR) is a typical representative for the Bayesian structure with non-parameter expression and uncertainty presentation. In this case, the effects on predictive results are compared and analyzed using the proposed method and the HGPFR model with different lengths of training data. Besides, in consideration of the degradation characteristics for the lithium-ion battery data set, the selections of the wavelet de-noising method are performed with corresponding experimental analyses. Furthermore, we set the hype-parameter for the mean function and co-variance function, and then develop a method for parameter optimization to make the proposed model suitable for the data. Moreover, a numerical simulation based on the data repository of Department of Engineering Science (DES) university of Oxford and Center for Advanced Life Cycle Engineering (CALCE) of University of Maryland is carried out, and the results are analyzed. For the data repository, an accuracy of 2.2% is obtained compared with the same value of 6.7% for the HGPFR model. What is more, the applicability and stability are verified with the prognostic results by the proposed method.

show abstract

Parameterization of linear equivalent circuit models over wide temperature and SOC spans for automotive lithium-ion cells using electrochemical impedance spectroscopy

Cited by 53 publications

References 18 publications

Impedance Characterization of an LCO-NMC/Graphite Cell: Ohmic Conduction, SEI Transport and Charge-Transfer Phenomenon

Impedance Characterization of an LCO-NMC/Graphite Cell: Ohmic Conduction, SEI Transport and Charge-Transfer Phenomenon

Battery Modeling and Parameter Extraction for Drive Cycle Loss Evaluation of a Modular Battery System for Vehicles Based on a Cascaded H-Bridge Multilevel Inverter

Lithium-Ion Battery Prognostics with Hybrid Gaussian Process Function Regression

Contact Info

Product

Resources

About