Aging effect on the variation of Li-ion battery resistance as function of temperature and state of charge

Barcellona, Simone; Colnago, Silvia; Dotelli, Giovanni; Latorrata, Saverio; Piegari, Luigi

doi:10.1016/j.est.2022.104658

Cited by 53 publications

(7 citation statements)

References 34 publications

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“…Interpreting aging-related charge transfer kinetic information.-Perhaps the most interesting and easily observed element of the combined EIS and 2nd-NLEIS data is the way it shows the state of health (SoH) and state of charge (SoC) dependence in the charge transfer resistance (R ct ) and the charge transfer asymmetry (ε) parameters for each electrode in the full-cell SSO fits. Aging-related evolution of charge transfer resistance has been widely studied for battery systems using EIS, [55][56][57] but this is the first time 2nd-NLEIS is being used to quantitatively estimate the impact of aging-related changes to reaction symmetry via the model parameter ε at the cathode and anode. Previously, we have shown (qualitatively) that the breaking of charge transfer symmetry on an electrode leads to a 2nd-NLEIS charge transfer arc in experimental data.…”

Section: Resultsmentioning

confidence: 99%

Second-Harmonic Nonlinear Electrochemical Impedance Spectroscopy: Part II. Model-Based Analysis of Lithium-Ion Battery Experiments

Ji,

Schwartz

2024

J. Electrochem. Soc.

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Quantitative analysis of electrochemical impedance spectroscopy (EIS) and 2nd-harmonic nonlinear EIS (2nd-NLEIS) data from commercial Li-ion batteries is performed using the porous electrode half-cell models developed in Part I. Because EIS and 2nd-NLEIS signals have opposite parity, the full-cell EIS model relies on the sum of cathode and anode half-cells whereas the full-cell 2nd-NLEIS model requires subtraction of the anode half-cell from the cathode. The full-cell EIS model produces a low error fit to EIS measurements, but importing EIS best-fit parameters into the 2nd-NLEIS model fails to ensure robust model-data convergence. In contrast, simultaneously fitting opposite parity EIS and 2nd-NLEIS models to the corresponding magnitude-normalized experimental data provides a lower total error fit, more internally self-consistent parameters, and better assignment of parameters to individual electrodes than EIS analysis alone. Our results quantify the extent that mild aging of cells (<1% capacity loss) results in substantial increases in cathode charge transfer resistance, and for the first time, a breakdown in cathode charge transfer symmetry at 30% and lower state-of-charge (SoC). New avenues for model-based analysis are discussed for full-cell diagnostic and we identify several open questions.

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Section: Resultsmentioning

confidence: 99%

Second-Harmonic Nonlinear Electrochemical Impedance Spectroscopy: Part II. Model-Based Analysis of Lithium-Ion Battery Experiments

Ji,

Schwartz

2024

J. Electrochem. Soc.

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“…The increasing internal battery resistance with battery aging is one of the critical aspects limiting the lifetime of batteries. It is well-known that battery internal resistance changes with temperature and SoC [125]. In particular, high temperatures will also accelerate some irreversible chemical reactions.…”

Section: State Of Health Estimation Methodologiesmentioning

confidence: 99%

Review on Battery State Estimation and Management Solutions for Next-Generation Connected Vehicles

Di Luca,

Di Blasio,

Gimelli

et al. 2023

Energies

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The transport sector is tackling the challenge of reducing vehicle pollutant emissions and carbon footprints by means of a shift to electrified powertrains, i.e., battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs). However, electrified vehicles pose new issues associated with the design and energy management for the efficient use of onboard energy storage systems (ESSs). Thus, strong attention should be devoted to ensuring the safety and efficient operation of the ESSs. In this framework, a dedicated battery management system (BMS) is required to contemporaneously optimize the battery’s state of charge (SoC) and to increase the battery’s lifespan through tight control of its state of health (SoH). Despite the advancements in the modern onboard BMS, more detailed data-driven algorithms for SoC, SoH, and fault diagnosis cannot be implemented due to limited computing capabilities. To overcome such limitations, the conceptualization and/or implementation of BMS in-cloud applications are under investigation. The present study hence aims to produce a new and comprehensive review of the advancements in battery management solutions in terms of functionality, usability, and drawbacks, with specific attention to cloud-based BMS solutions as well as SoC and SoH prediction and estimation. Current gaps and challenges are addressed considering V2X connectivity to fully exploit the latest cloud-based solutions.

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“…In addition, some researchers have also made fractional order improvements to the Thevenin and DP models to ensure high simulation and computational efficiency [54]. Simone et al [55] analyzed the variation in internal resistance with SOC and temperature in various aging scenarios based on electrochemical impedance spectroscopy. They established a mathematical model to forecast how battery internal resistance would change in response to aging, SOC, and temperature.…”

Section: Modelmentioning

confidence: 99%

Advances in the Study of Techniques to Determine the Lithium-Ion Battery’s State of Charge

Liu,

Gao,

Marma

et al. 2024

Energies

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This study explores the challenges and advances in the estimation of the state of charge (SOC) of lithium-ion batteries (LIBs), which are crucial to optimizing their performance and lifespan. This review focuses on four main techniques of SOC estimation: experimental measurement, modeling approach, data-driven approach, and joint estimation approach, highlighting the limitations and potential inaccuracies of each method. This study suggests a combined approach, incorporating correction parameters and closed-loop feedback, to improve measurement accuracy. It introduces a multi-physics model that considers temperature, charging rate, and aging effects and proposes the integration of models and algorithms for optimal estimation of SOC. This research emphasizes the importance of considering temperature and aging factors in data-driven approaches. It suggests that the fusion of different methods could lead to more accurate SOC predictions, an important area for future research.

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Aging effect on the variation of Li-ion battery resistance as function of temperature and state of charge

Cited by 53 publications

References 34 publications

Second-Harmonic Nonlinear Electrochemical Impedance Spectroscopy: Part II. Model-Based Analysis of Lithium-Ion Battery Experiments

Second-Harmonic Nonlinear Electrochemical Impedance Spectroscopy: Part II. Model-Based Analysis of Lithium-Ion Battery Experiments

Review on Battery State Estimation and Management Solutions for Next-Generation Connected Vehicles

Advances in the Study of Techniques to Determine the Lithium-Ion Battery’s State of Charge

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