A Novel Mechanical Analogy-Based Battery Model for SoC Estimation Using a Multicell EKF

Paschero, Maurizio; Storti, Gian Luca; Rizzi, Antonello; Mascioli, Fabio Massimo Frattale; Rizzoni, Giorgio

doi:10.1109/tste.2016.2574755

Cited by 64 publications

(20 citation statements)

References 31 publications

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“…Thus, the identification procedure took into account the temperature effect but not the battery aging. In addition, in [127], a circuit with two RC branches was taken into consideration. In this paper, an innovative definition of the SoC was given on the basis of a mechanical analogy.…”

Section: Online Identification Methodsmentioning

confidence: 99%

Lithium Ion Battery Models and Parameter Identification Techniques

2017

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Nowadays, battery storage systems are very important in both stationary and mobile applications. In particular, lithium ion batteries are a good and promising solution because of their high power and energy densities. The modeling of these devices is very crucial to correctly predict their state of charge (SoC) and state of health (SoH). The literature shows that numerous battery models and parameters estimation techniques have been developed and proposed. Moreover, surveys on their electric, thermal, and aging modeling are also reported. This paper presents a more complete overview of the different proposed battery models and estimation techniques. In particular, a method for classifying the proposed models based on their approaches is proposed. For this classification, the models are divided in three categories: mathematical models, physical models, and circuit models.

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Section: Online Identification Methodsmentioning

confidence: 99%

Lithium Ion Battery Models and Parameter Identification Techniques

2017

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“…However, some parameters cannot be measured directly through experiments [12]. Many algorithms, including the gradient method [33], the gradientfree method [34][35][36][37][38] and the Kalman filter method [4,39] have been proposed for the parameter identification of LIBs. The Leverberg-Marquadt (LM) algorithm is a classic gradient-based nonlinear regression method.…”

Section: Introductionmentioning

confidence: 99%

A Karhunen-Loeve Galerkin Online Modeling Approach for the Thermal Dynamics of Li-Ion Batteries

Shen

Deng³

et al. 2020

IEEE Access

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The thermal dynamics of Li-ion batteries are very complicated, and the battery temperature is spatially distributed imbalanced from the battery interior to the surface. The thermal dynamics are commonly modeled by partial differential equations (PDEs); however, parameter identification for PDEs is difficult and time consuming. In this work, a Karhunen-Loeve Galerkin method is proposed to obtain a simple but effective low-order model for the distributed thermal dynamics of Li-ion batteries. The Karhunen-Loeve decomposition method is applied to capture the most representative spatial modes of the dynamics, while the Galerkin method is used to obtain the corresponding temporal modes. All the uncertain physical parameters in the temporal modes are identified by the Levenberg-Marquardt algorithm. The updated temporal modes synthesized with spatial modes can offer a fast estimation of the temperature distribution on the battery surface and thus has the potential to provide distributed temperature prediction for the battery management system. The proposed modeling scheme is tested on a 60Ah Li-ion battery cell, and the simulation result shows an excellent match in the temperature distribution and a faster computing speed than the rigorous physical model.

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“…Kalman filters [2][3][4][5], Sliding mode observers [6,7], techniques and others have been used heavily to estimate battery state of charge. The performance of these methods relies heavily on the accuracy of the battery model.…”

Section: Introductionmentioning

confidence: 99%

Robust Parameter Estimation of an Electric Vehicle Lithium-Ion Battery Using Adaptive Forgetting Factor Recursive Least Squares

Elmarghichi¹,

Bouzi²,

Ettalabi³

2020

IJIES

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In this paper, a battery model suitable for electric vehicle application is analyzed. Open circuit voltage is described by an adaptation of Nernst equation. Thevenin circuit is used to depict the instantaneous and transient regime. Hysteresis effect is outlined by a zero-state correction term. We propose a new algorithm AFFRLS (adaptive forgetting factor recursive least squares) to extract the parameter of the battery model, then to predict the output voltage, and compare it to the original FFRLS (forgetting factor recursive least squares). To evaluate these algorithms, we used experimental data conducted by CALCE Battery Research Group on the Samsung INR 18650-20R battery cell. We fed the data to the algorithms and compared the estimated output voltage for two dynamic tests on MATLAB. Results show that AFFRLS has low distribution in high error range up to 4% less than FFRLS, this means that AFFRLS has a better parameter identification than FFRLS.

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A Novel Mechanical Analogy-Based Battery Model for SoC Estimation Using a Multicell EKF

Cited by 64 publications

References 31 publications

Lithium Ion Battery Models and Parameter Identification Techniques

Lithium Ion Battery Models and Parameter Identification Techniques

A Karhunen-Loeve Galerkin Online Modeling Approach for the Thermal Dynamics of Li-Ion Batteries

Robust Parameter Estimation of an Electric Vehicle Lithium-Ion Battery Using Adaptive Forgetting Factor Recursive Least Squares

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