Optimal Input Design for Parameter Identification in an Electrochemical Li-ion Battery Model

Park, Saehong; Kato, Dylan; Gima, Zach; Klein, Reinhardt; Moura, Scott J.

doi:10.23919/acc.2018.8431479

Cited by 8 publications

(7 citation statements)

References 18 publications

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“…Finally, parameter fitting is obtained via a gradient-based algorithm. Figure 10b shows the error percentage using two different cycles (one dedicated and one typical 1C constant current [51]), with similar results.…”

Section: Multi Optimization Analysis (Moa)supporting

confidence: 54%

“…This does not change the fundamental identifiability of the model; however, careful design of experiments can improve the informativity of the collected data to determining accurate estimation of values for certain parameters. Electrochemical impedance spectroscopy (EIS) tests, charge and discharge tests conducted at different rates, pulse tests, tests at different temperatures, tests around different cell states of charge, sensitivity analysis, or any combination of these can be used [31,45,38,83,85,86,84,51].…”

Section: Multi Optimization Analysis (Moa)mentioning

confidence: 99%

“…Park et al [51] used a similar methodology to [84,83] in which the general P2D model was used [12]. First, a sensitivity analysis is performed across a variety of profiles including pulses, sinusoids and driving cycles.…”

Section: Multi Optimization Analysis (Moa)mentioning

confidence: 99%

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Review of computational parameter estimation methods for electrochemical models

Miguel

Plett

Trimboli

et al. 2021

Journal of Energy Storage

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Section: Multi Optimization Analysis (Moa)supporting

confidence: 54%

Section: Multi Optimization Analysis (Moa)mentioning

confidence: 99%

See 1 more Smart Citation

Review of computational parameter estimation methods for electrochemical models

Miguel

Plett

Trimboli

et al. 2021

Journal of Energy Storage

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“…Since we do not have information on the true parameters, it is useful to identify the parameters in a cumulative way to prevent overfitting and increase the degrees of freedom during optimization. The authors' previous work also showed that an optimized input reduces the condition number of the objective function's Hessian with respect to the parameters 37. This accelerates gradient descent methods for parameter estimation.…”

mentioning

confidence: 91%

Optimal Experimental Design for Parameterization of an Electrochemical Lithium-Ion Battery Model

Park

Kato

Gima

et al. 2018

J. Electrochem. Soc.

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100

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We consider the problem of optimally designing an excitation input for parameter identification of an electrochemical Li-ion battery model. The conventional approach to performing parameter identification uses standard test cycles. In contrast, we optimally design the input trajectory to maximize parameter identifiability in the sense of Fisher information. Specifically, we derive sensitivity equations for the electrochemical model. This approach enables parameter sensitivity analysis and optimal parameter fitting via gradient-based algorithms. This paper presents a general systematic approach to identify the electrochemical parameters in a non-invasive way. First, we group parameters into two sets: (i) equilibrium parameters, and (ii) dynamical parameters. We also divide the dynamical parameters into subsets by calculating orthogonalized sensitivity, which mitigates linear dependence between parameters. A large number of input profiles have been devised to constitute an input library. Then, the optimal inputs are selected from the input library to maximize the Fisher information, via convex programming. Using this framework a number of relevant experiments are obtained to parameterize. To validate our approach experimentally, we consider a 18650 Lithium nickel cobalt aluminum oxide battery. Compared to the conventional approach, our proposal achieves lower voltage RMSE across all experimental testing cycles.

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“…A topic related with identification is experiment design, which is to find out the best input sequences to excite a battery to maximize the parameter identifiability. In [49], [50], optimal input design is performed by maximizing the Fisher information matrices-an identifiability metricinvolved in the identification of the Thevenin model and the SPM, respectively.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Double-Capacitor Model for Rechargeable Batteries: Modeling, Identification, and Validation

Tian

Fang

Chen

et al. 2021

IEEE Trans. Contr. Syst. Technol.

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This paper proposes a new equivalent circuit model for rechargeable batteries by modifying a double-capacitor model proposed in [1]. It is known that the original model can address the rate capacity effect and energy recovery effect inherent to batteries better than other models. However, it is a purely linear model and includes no representation of a battery's nonlinear phenomena. Hence, this work transforms the original model by introducing a nonlinear-mapping-based voltage source and a serial RC circuit. The modification is justified by an analogy with the single-particle model. Two parameter estimation approaches, termed 1.0 and 2.0, are designed for the new model to deal with the scenarios of constant-current and variable-current charging/discharging, respectively. In particular, the 2.0 approach proposes the notion of Wiener system identification based on maximum a posteriori estimation, which allows all the parameters to be estimated in one shot while overcoming the nonconvexity or local minima issue to obtain physically reasonable estimates. An extensive experimental evaluation shows that the proposed model offers excellent accuracy and predictive capability. A comparison against the Rint and Thevenin models further points to its superiority. With high fidelity and low mathematical complexity, this model is beneficial for various real-time battery management applications.

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Optimal Input Design for Parameter Identification in an Electrochemical Li-ion Battery Model

Cited by 8 publications

References 18 publications

Review of computational parameter estimation methods for electrochemical models

Review of computational parameter estimation methods for electrochemical models

Optimal Experimental Design for Parameterization of an Electrochemical Lithium-Ion Battery Model

Nonlinear Double-Capacitor Model for Rechargeable Batteries: Modeling, Identification, and Validation

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