Review of computational parameter estimation methods for electrochemical models

Miguel, Eduardo de; Plett, Gregory L.; Trimboli, M. Scott; Oca, Laura; Iraola, Unai; Bekaert, Émilie

doi:10.1016/j.est.2021.103388

Cited by 39 publications

(8 citation statements)

References 72 publications

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“…All diffraction peaks can be well indexed within the space group of R-3m, and the lattice parameters calculated for Bi 2 Se 3 are a = 4.1993(6) Å, c = 28.656(7) Å, with the cell volume of 437.62(2) Å 3 . For Bi 2 Se 3−x Te x @NC (x = 1), a = 4.2238(7) Å, c = 29.433(8) Å, with the cell volume = 454.77(2) Å 3 . The carbon in Bi 2 Se 3 @NC and Bi 2 Se 3−x Te x @NC materials was characterized by Raman spectroscopy.…”

Section: Results and Discussion Characterizationmentioning

confidence: 99%

“…The tremendous endeavors have been dedicated to improvements to lithium-ion batteries for both electric vehicle applications and stationary energy storage [2]. Multidisciplinary approaches, including electrochemistry, materials engineering, dynamic modeling [3,4], and systems or controls engineering [5,6], have demonstrated their capability to enhance either performance or reliable operation during the battery system's lifetime [7].…”

Section: Introductionmentioning

confidence: 99%

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Te-Doped Bi2Se3@NC Nanocomposites for High-Performance Li-Ion Battery Anodes

Zhu,

Wu,

Zhao

et al. 2023

Sustainability

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Bismuth-based anodes are promising for lithium-ion batteries owing to their high theoretical capacity, garnering significant attention in recent decades. Despite its intrinsic advantages, this anode material has the shortcoming of excessive volume expansion during the charging and discharging processes, which significantly reduces the cycling stability. In this work, a nitrogen-doped carbon-coated Bi2Se3−xTex composite (Bi2Se3−xTex@NC) with excellent electrochemical properties was synthesized by the solvothermal method followed by heat treatment. Benefiting from its unique structure and Te-doping properties, the nitrogen-doped carbon-coated Bi2Se3−xTex composite maintains a discharge specific capacity of 464.0 mAh g−1 after 60 cycles at a current density of 0.1 A g−1. This research presents an efficient approach for the preparation of nitrogen-doped carbon-coated anode materials and a fresh perspective on battery electrode material design.

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Section: Results and Discussion Characterizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Te-Doped Bi2Se3@NC Nanocomposites for High-Performance Li-Ion Battery Anodes

Zhu,

Wu,

Zhao

et al. 2023

Sustainability

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“…In the current paper, the LG Chem-INR21700-M50 cell with a NMC cathode is modelled. The physics-based models warrant properly estimated values of computational parameter values for predictions with reasonable accuracy [18]. For the current cell under study, for electrodes and electrolyte, the physical (overall cell/anode/cathode/separator/ current-collector dimensions), chemical (composition of anode/cathode/electrolyte) and electrochemical (open circuit voltage, ionic/electronic conductivity, diffusivity, etc.)…”

Section: Coupled Thermal-electrochemical Model Developmentmentioning

confidence: 99%

Development of a Fast Running Equivalent Circuit Model with Thermal Predictions for Battery Management Applications

Damodaran,

Paramadayalan,

Natarajan

et al. 2024

Batteries

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Equivalent circuit modelling (ECM) is a powerful tool to study the dynamic and non-linear characteristics of Li-ion cells and is widely used for the development of the battery management system (BMS) of electric vehicles. The dynamic parameters described by the ECM are used by the BMS to estimate the battery state of charge (SOC), which is crucial for efficient charging/discharging, range calculations, and the overall safe operation of electric vehicles. Typically, the ECM approach represents the dynamic characteristics of the battery in a mathematical form with a limited number of unknown parameters. Then, the parameters are calculated from voltage and current information of the lithium-ion cell obtained from controlled experiments. In the current work, a faster and simplified first-order resistance–capacitance (RC) equivalent circuit model was developed for a commercial cylindrical cell (LGM50 21700). An analytical solution was developed for the equivalent circuit model incorporating SOC and temperature-dependent RC parameters. The solution to the RC circuit model was derived using multiple expressions for different components like open circuit voltage (OCV), instantaneous resistance (R0), and diffusional parameters (R1 and C1) as a function of the SOC and operating temperature. The derived parameters were validated against the virtual HPPC test results of a validated physics-based electrochemical model for the voltage behavior. Using the developed RC circuit model, a polynomial expression is derived to estimate the temperature increase of the cell including both irreversible and reversible heat generation components. The temperature predicted by the proposed RC circuit model at different battery operating temperatures is in good agreement with the values obtained from the validated physics model. The developed method can find applications in (i) onboard energy management by the BMS and (ii) quicker evaluation of cell performance early in the product development cycle.

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“…The first is based on battery mechanistic models, which summarize the internal active behavior of the battery by establishing its mechanistic model and utilizing directly measured battery parameters to build mathematical models to predict the battery's SOH based on the characteristics of internal physical and chemical processes of the battery [14]. Liping Chen et al [15] combined equivalent circuit models (ECMs) with deep learning networks to enhance prediction accuracy and generalization by data features.…”

Section: Introductionmentioning

confidence: 99%

State of Health (SOH) Estimation of Lithium-Ion Batteries Based on ABC-BiGRU

Li,

Chen,

Wei

et al. 2024

Electronics

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As a core component of new energy vehicles, accurate estimation of the State of Health (SOH) of lithium-ion power batteries is essential. Correctly predicting battery SOH plays a crucial role in extending the lifespan of new energy vehicles, ensuring their safety, and promoting their sustainable development. Traditional physical or electrochemical models have low accuracy in measuring the SOH of lithium batteries and are not suitable for the complex driving conditions of real-world vehicles. This study utilized the black-box characteristics of deep learning models to explore the intrinsic correlations in the historical cycling data of lithium batteries, thereby eliminating the need to consider the internal chemical reactions of lithium batteries. Through Pearson correlation analysis, this study selects health indicators (HIs) from lithium battery cycling data that significantly impact SOH as input features. In the field of lithium batteries, this paper applies ABC-BiGRU for the first time to SOH prediction. Compared with other recursive neural network models, ABC-BiGRU demonstrates superior predictive performance, with maximum root mean square error and mean absolute error of only 0.016799317 and 0.012626847, respectively.

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Review of computational parameter estimation methods for electrochemical models

Cited by 39 publications

References 72 publications

Te-Doped Bi2Se3@NC Nanocomposites for High-Performance Li-Ion Battery Anodes

Te-Doped Bi2Se3@NC Nanocomposites for High-Performance Li-Ion Battery Anodes

Development of a Fast Running Equivalent Circuit Model with Thermal Predictions for Battery Management Applications

State of Health (SOH) Estimation of Lithium-Ion Batteries Based on ABC-BiGRU

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