Bridging physics-based and equivalent circuit models for lithium-ion batteries

Geng, Zeyang; Wang, Siyang; Lacey, Matthew J.; Brandell, Daniel; Thiringer, Torbjörn

doi:10.1016/j.electacta.2021.137829

Cited by 64 publications

(36 citation statements)

References 33 publications

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“…Some studies seek to account for the effects of hysteresis and temperature on a LiB's electrical dynamics [20][21][22][23]. Others design new ECMs to approximate certain electrochemical models [24][25][26][27]. While ECMs have found increasing popularity, their structural simplicity restricts their accuracy, making them useful only for low to medium C-rates.…”

Section: Literature Reviewmentioning

confidence: 99%

Integrating Physics-Based Modeling with Machine Learning for Lithium-Ion Batteries

Tu¹,

Moura²,

Wang³

et al. 2021

Preprint

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Mathematical modeling of lithium-ion batteries (LiBs) is a primary challenge in advanced battery management. This paper proposes two new frameworks to integrate a physics-based model with machine learning to achieve high-precision modeling for LiBs. The frameworks are characterized by informing the machine learning model of the state information of the physical model, enabling a deep integration between physics and machine learning. Based on the frameworks, a series of hybrid models are constructed, through combining an electrochemical model and an equivalent circuit model, respectively, with a feedforward neural network. The hybrid models are relatively parsimonious in structure and can provide considerable predictive accuracy under a broad range of C-rates, as shown by extensive simulations and experiments. The study further expands to conduct aging-aware hybrid modeling, leading to the design of a hybrid model conscious of the state-of-health to make prediction. Experiments show that the model has high predictive accuracy throughout a LiB's cycle life.

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Section: Literature Reviewmentioning

confidence: 99%

Integrating Physics-Based Modeling with Machine Learning for Lithium-Ion Batteries

Tu¹,

Moura²,

Wang³

et al. 2021

Preprint

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“…This approach enables the simulation of several material properties and the prediction of battery performance under various design parameters. New approaches try to use the high precision of the P2D model by simplification [18,19] or by coupling it with an equivalent circuit model in order to apply it to real-time applications [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Electrochemical Model-Based Investigation of Thick LiFePO4 Electrode Design Parameters

Franke‐Lang

Kowal

2021

Modelling

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The electrification of the powertrain requires enhanced performance of lithium-ion batteries, mainly in terms of energy and power density. They can be improved by optimising the positive electrode, i.e., by changing their size, composition or morphology. Thick electrodes increase the gravimetric energy density but generally have an inefficient performance. This work presents a 2D modelling approach for better understanding the design parameters of a thick LiFePO4 electrode based on the P2D model and discusses it with common literature values. With a superior macrostructure providing a vertical transport channel for lithium ions, a simple approach could be developed to find the best electrode structure in terms of macro- and microstructure for currents up to 4C. The thicker the electrode, the more important are the direct and valid transport paths within the entire porous electrode structure. On a smaller scale, particle size, binder content, porosity and tortuosity were identified as very impactful parameters, and they can all be attributed to the microstructure. Both in modelling and electrode optimisation of lithium-ion batteries, knowledge of the real microstructure is essential as the cross-validation of a cellular and lamellar freeze-casted electrode has shown. A procedure was presented that uses the parametric study when few model parameters are known.

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“…While these models are intuitive and relatively simple to use in control system design and implementation, they do not provide insights on the internal behavior of the battery. In this regard, new approaches have recently been explored, such as the developed distributedparameter ECMs [20,21], in which the models are normally expressed in the form of DAEs, which can be solved rapidly using the proposed method with high accuracy. This represents an improvement on existing physics-based Li-ion battery models, especially in real-time, dynamic environments.…”

Section: Introductionmentioning

confidence: 99%

“…This represents an improvement on existing physics-based Li-ion battery models, especially in real-time, dynamic environments. Physics-based equivalent circuit models combine the benefits of high accuracy physical models with the lower computational cost of empirical ones, for instance, by combining a concise transmission line structure with partial differential equations for the mass transport processes that describe the concentration distributions and that are solved with the finite difference method, avoiding simplifications or approximations, thus guaranteeing the accuracy of the results [20]. Online estimation and prediction of the Remaining Useful Life also often use data-driven empirical methods which, however, have not been commonly exploited for cell design purposes.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Surrogate Models for Fast LIB Performance Predictions

et al. 2021

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Battery Cell design and control have been widely explored through modeling and simulation. On the one hand, Doyle’s pseudo-two-dimensional (P2D) model and Single Particle Models are among the most popular electrochemical models capable of predicting battery performance and therefore guiding cell characterization. On the other hand, empirical models obtained, for example, by Machine Learning (ML) methods represent a simpler and computationally more efficient complement to electrochemical models and have been widely used for Battery Management System (BMS) control purposes. This article proposes ML-based ensemble models to be used for the estimation of the performance of an LIB cell across a wide range of input material characteristics and parameters and evaluates 1. Deep Learning ensembles for simulation convergence classification and 2. structured regressors for battery energy and power predictions. The results represent an improvement on state-of-the-art LIB surrogate models and indicate that deep ensembles represent a promising direction for battery modeling and design.

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Bridging physics-based and equivalent circuit models for lithium-ion batteries

Cited by 64 publications

References 33 publications

Integrating Physics-Based Modeling with Machine Learning for Lithium-Ion Batteries

Integrating Physics-Based Modeling with Machine Learning for Lithium-Ion Batteries

Electrochemical Model-Based Investigation of Thick LiFePO4 Electrode Design Parameters

Ensemble Surrogate Models for Fast LIB Performance Predictions

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