Lithium-Ion Battery Internal Resistance Model Based on the Porous Electrode Theory

Yang, Jing; Wei, Xuezhe; Dai, Haifeng; Zhu, Jiangong; Xu, Xudong

doi:10.1109/vppc.2014.7007096

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…This impact on the resistance value is expected to change the shape of the temporal temperature response from the heated Li-ion batteries. Many studies have proposed methods or experimental investigations to represent the effect of SOC in the model for the internal resistance [24,39,40,41,42,43,44]. This section proposes a method to consider the effect of SOC and build an internal resistance accordingly.…”

Section: Effect Of Soc On the Li-ion Cells Temperature Evolutionmentioning

confidence: 99%

Accuracy assessment of an internal resistance model of Li-ion batteries in immersion cooling configuration

Solai

Beaugendre

Bieder

et al. 2023

Applied Thermal Engineering

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Section: Effect Of Soc On the Li-ion Cells Temperature Evolutionmentioning

confidence: 99%

Accuracy assessment of an internal resistance model of Li-ion batteries in immersion cooling configuration

Solai

Beaugendre

Bieder

et al. 2023

Applied Thermal Engineering

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“…ECMs are widely used in the real-time battery management system (BMS) due to low calculation burden, flexible modeling and high accuracy. At present, the integer order ECM mainly includes: R int model [29], PNGV model [30], Thevenin model [10] and second-order RC equivalent circuit model [31]. Compared with other models, the second-order RC equivalent circuit model has been widely concerned due to its high accuracy and moderate complexity, its ECM and EIS diagram are shown in Figure 2.…”

Section: Fractional Order Equivalent Circuit Modelmentioning

confidence: 99%

A novel online identification algorithm of lithium‐ion battery parameters and model order based on a fractional order model

Sun

Ren

et al. 2021

IET Renewable Power Gen

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A fractional order equivalent circuit model with variable order can better reflect the internal reaction mechanism of a lithium-ion battery, however, it is not easy to simultaneously identify the parameters and order of the fractional order model online. In this paper, based on the electrochemical impedance spectroscopy of the lithium-ion battery, the FOM and its discretization are analysed in detail, and a fractional order repeated prediction recursive least square (FORPRLS) method is proposed, the approximate error of fractional derivative is corrected by the repeated identification process with changing the order, so that the parameters and order of the fractional order model can be identified online and accurately at the same time. For the second-order RC equivalent circuit model and fractional order model, different online identification algorithms including the forgetting factor recursive least square and FORPRLS are carried out under the dynamic stress test experiment, and the predicted terminal voltages are compared with the actual measured terminal voltages to judge the identification accuracy of these algorithms. The experimental results verify that the FORPRLS algorithm is superior to the FFRLS algorithm, and the identified order can better match the reaction process of the lithium-ion battery.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Thus, the lithium-ion battery model can usually be established by referring to that of the lead-acid batteries. At present, the battery equivalent circuit model mainly includes R int model [7], PNGV (Partnership for a New Generation of Vehicles) model [8], Thevenin model [9], and n-order RC equivalent circuit model [10]. The R int model is an internal resistance model consisting of a DC source and an internal resistance.…”

Section: Introductionmentioning

confidence: 99%

“…are the difference between the terminal voltage and the open circuit voltage at the time of k − 1 and k − 2. I(k), I(k − 1), and I(k − 2) are input currents at the time of k, k − 1, and k − 2.Suppose that a, b, c, d, f are represented by(7).…”

mentioning

confidence: 99%

Adaptive Forgetting Factor Recursive Least Square Algorithm for Online Identification of Equivalent Circuit Model Parameters of a Lithium-Ion Battery

et al. 2019

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With the popularity of electric vehicles, lithium-ion batteries as a power source are an important part of electric vehicles, and online identification of equivalent circuit model parameters of a lithium-ion battery has gradually become a focus of research. A second-order RC equivalent circuit model of a lithium-ion battery cell is modeled and analyzed in this paper. An adaptive expression of the variable forgetting factor is constructed. An adaptive forgetting factor recursive least square (AFFRLS) method for online identification of equivalent circuit model parameters is proposed. The equivalent circuit model parameters are identified online on the basis of the dynamic stress testing (DST) experiment. The online voltage prediction of the lithium-ion battery is carried out by using the identified circuit parameters. Taking the measurable actual terminal voltage of a single battery cell as a reference, by comparing the predicted battery terminal voltage with the actual measured terminal voltage, it is shown that the proposed AFFRLS algorithm is superior to the existing forgetting factor recursive least square (FFRLS) and variable forgetting factor recursive least square (VFFRLS) algorithms in accuracy and rapidity, which proves the feasibility and correctness of the proposed parameter identification algorithm.

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Lithium-Ion Battery Internal Resistance Model Based on the Porous Electrode Theory

Cited by 5 publications

References 12 publications

Accuracy assessment of an internal resistance model of Li-ion batteries in immersion cooling configuration

Accuracy assessment of an internal resistance model of Li-ion batteries in immersion cooling configuration

A novel online identification algorithm of lithium‐ion battery parameters and model order based on a fractional order model

Adaptive Forgetting Factor Recursive Least Square Algorithm for Online Identification of Equivalent Circuit Model Parameters of a Lithium-Ion Battery

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