Generic dynamic model of rechargeable batteries

Milocco, Ruben H.; Thomas, Jorge Enrique; Castro, B.E.

doi:10.1016/j.jpowsour.2013.08.006

Cited by 22 publications

(15 citation statements)

References 25 publications

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“…As detailed in the theory, for the closed-loop system (17) and (18), constraints (7) are mapped into constraints in the form (22). The specific vectors and scalars of (22) for the two specific batteries are reported in Table 3.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…The first model is the Doyle-Fuller-Newman model which will be used to have a realistic simulation of the battery dynamics. The second model is a reduced model introduced in [18] known as the equivalenthydraulic model (EHM), which will be used to design the proposed control law. Then, the control law will be validated in simulation on the full-order DFN model.…”

Section: Li-ion Battery Cell Modelsmentioning

confidence: 99%

“…On the basis of this abstraction several models have been derived in the literature. One of the most interesting ones is the equivalent-hydraulic model (EHM) proposed in [18]. This model, depicted in Fig.…”

Section: Reduced-order Modelmentioning

confidence: 99%

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A feedback charge strategy for Li-ion battery cells based on Reference Governor

Romagnoli

Couto

Goldar

et al. 2019

Journal of Process Control

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In this paper, we propose a solution for the control of the state-of-charge of a lithium-ion (Li-ion) battery cell. The aim is to design a control scheme able to charge the battery in a fast way while ensuring that the safety constraints of the electrochemical model (ECheM) of the battery are not violated. The proposed solution is based on a novel computationally efficient formulation of the Reference Governor (RG) able to deal with the union of linear constraints and which has been designed on the basis of a reduced ECheM, namely the equivalent-hydraulic model (EHM). Using suitable safety margins, the satisfaction of the constraints on the EHM ensures the satisfaction of the constraints on the full-order ECheM. Simulation results obtained on an accurate simulator show that the proposed method, if compared with classical charge strategies, is able to considerably improve the charge performance while ensuring the satisfaction of the constraints. These findings are validated experimentally on a Li-ion graphite/LCO battery.

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Section: Numerical Resultsmentioning

confidence: 99%

Section: Li-ion Battery Cell Modelsmentioning

confidence: 99%

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A feedback charge strategy for Li-ion battery cells based on Reference Governor

Romagnoli

Couto

Goldar

et al. 2019

Journal of Process Control

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“…After assuming that the electrolyte concentration is constant, the electrochemical model is simplified through an approximate solution of the diffusion equation in the active material [62]. The diffusion process is represented by a high pass filter and a pure integrator in [63]. The electrical analogy of the charge migration and diffusion equations are utilized for accelerating the simulation time of the electrochemical model.…”

Section: Electrochemical Modelmentioning

confidence: 99%

Overview of Lithium-Ion Battery Modeling Methods for State-of-Charge Estimation in Electrical Vehicles

et al. 2018

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As a critical indictor in the Battery Management System (BMS), State of Charge (SOC) is closely related to the reliable and safe operation of lithium-ion (Li-ion) batteries. Model-based methods are an effective solution for accurate and robust SOC estimation, the performance of which heavily relies on the battery model. This paper mainly focuses on battery modeling methods, which have the potential to be used in a model-based SOC estimation structure. Battery modeling methods are classified into four categories on the basis of their theoretical foundations, and their expressions and features are detailed. Furthermore, the four battery modeling methods are compared in terms of their pros and cons. Future research directions are also presented. In addition, after optimizing the parameters of the battery models by a Genetic Algorithm (GA), four typical battery models including a combined model, two RC Equivalent Circuit Model (ECM), a Single Particle Model (SPM), and a Support Vector Machine (SVM) battery model are compared in terms of their accuracy and execution time.

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“…A battery model capturing all these effects is important for accurate SoC and state of health estimation. It is however difficult to find practical models that interpret the RCE and the RE using electrochemical states [23]. A commonly used model for characterizing the electrochemical mechanism of a Li + battery is presented in [10].…”

Section: A the Working Mechanism Of LI + Batteriesmentioning

confidence: 99%

Adaptive Estimation of the State of Charge for Lithium-Ion Batteries: Nonlinear Geometric Observer Approach

Wang

Fang

Şahinoğlu

et al. 2015

IEEE Trans. Contr. Syst. Technol.

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Fang, H.; Sahinoglu, Z.; Wada, T.; Hara, S. TR2014-094 September 2014Abstract This paper considers the state of charge (SoC) and parameter estimation of lithium-ion batteries. Different from various prior arts, where estimation is based on local linearization of a nonlinear battery model, nonlinear geometric observer approach is followed to design adaptive observers for the SoC and parameter estimation based on nonlinear battery models. A major advantage of the proposed approach is the possibility to establish the exponential stability of the resultant error dynamics of state and parameter estimation. The proposed adaptive observers are shown to be robust with respect to unmodeled process uncertainties. Analysis also shows the design tradeoff between the convergence rate and the robustness of the estimation error dynamics with respect to the measurement noise. Simulation and experimental results validate the effectiveness and main advantages of the proposed approach. Error analysis is presented and explains the experimental results. IEEE Transactions on Control Systems TechnologyThis work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Abstract-This paper considers the State of Charge (SoC) and parameter estimation of Lithium-ion batteries. Different from various prior art, where estimation is based on local linearization of a nonlinear battery model, nonlinear geometric observer approach is followed to design adaptive observers for the SoC and parameter estimation based on nonlinear battery models. A major advantage of the proposed approach is the possibility to establish the exponential stability of the resultant error dynamics of state and parameter estimation. The proposed adaptive observers are shown to be robust with respect to unmodeled process uncertainties. Analysis also shows the design tradeoff between the convergence rate and the robustness of the estimation error dynamics with respect to the measurement noise. Simulation and experimental results validate the effectiveness and main advantages of the proposed approach. Error analysis is presented and explains the experimental results.

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Generic dynamic model of rechargeable batteries

Cited by 22 publications

References 25 publications

A feedback charge strategy for Li-ion battery cells based on Reference Governor

A feedback charge strategy for Li-ion battery cells based on Reference Governor

Overview of Lithium-Ion Battery Modeling Methods for State-of-Charge Estimation in Electrical Vehicles

Adaptive Estimation of the State of Charge for Lithium-Ion Batteries: Nonlinear Geometric Observer Approach

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