Inferring electrochemical performance and parameters of Li-ion batteries based on deep operator networks

• URL: http://arxiv.org/abs/2205.03508v1
• Date: Fri, 6 May 2022 23:55:48 GMT
• Title: Inferring electrochemical performance and parameters of Li-ion batteries based on deep operator networks
• Authors: Qiang Zheng, Xiaoguang Yin, Dongxiao Zhang
• Abstract summary: The Li-ion battery is a complex physicochemical system that generally takes applied current as input and terminal voltage as output. We propose to build a data-driven surrogate for a battery system while incorporating the underlying physics as constraints.
• Score: 1.8369974607582584
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The Li-ion battery is a complex physicochemical system that generally takes applied current as input and terminal voltage as output. The mappings from current to voltage can be described by several kinds of models, such as accurate but inefficient physics-based models, and efficient but sometimes inaccurate equivalent circuit and black-box models. To realize accuracy and efficiency simultaneously in battery modeling, we propose to build a data-driven surrogate for a battery system while incorporating the underlying physics as constraints. In this work, we innovatively treat the functional mapping from current curve to terminal voltage as a composite of operators, which is approximated by the powerful deep operator network (DeepONet). Its learning capability is firstly verified through a predictive test for Li-ion concentration at two electrodes. In this experiment, the physics-informed DeepONet is found to be more robust than the purely data-driven DeepONet, especially in temporal extrapolation scenarios. A composite surrogate is then constructed for mapping current curve and solid diffusivity to terminal voltage with three operator networks, in which two parallel physics-informed DeepONets are firstly used to predict Li-ion concentration at two electrodes, and then based on their surface values, a DeepONet is built to give terminal voltage predictions. Since the surrogate is differentiable anywhere, it is endowed with the ability to learn from data directly, which was validated by using terminal voltage measurements to estimate input parameters. The proposed surrogate built upon operator networks possesses great potential to be applied in on-board scenarios, such as battery management system, since it integrates efficiency and accuracy by incorporating underlying physics, and also leaves an interface for model refinement through a totally differentiable model structure.

Related papers

• PINN surrogate of Li-ion battery models for parameter inference. Part I: Implementation and multi-fidelity hierarchies for the single-particle model [0.0]
  This manuscript is the first of a two-part series that introduces PINN surrogates of Li-ion battery models for parameter inference. A multi-fidelity hierarchical training, where several neural nets are trained with multiple physics-loss fidelities is shown to significantly improve the surrogate accuracy.
  arXiv  Detail & Related papers  (2023-12-28T19:09:56Z)
• Physics-informed machine learning of redox flow battery based on a two-dimensional unit cell model [1.8147447763965252]
  We present a physics-informed neural network (PINN) approach for predicting the performance of an all-vanadium redox flow battery. Our numerical results show that the PINN is able to predict cell voltage correctly, but the prediction of potentials shows a constant-like shift.
  arXiv  Detail & Related papers  (2023-05-31T22:06:30Z)
• Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management [64.17887333976593]
  Avoiding over-pressurization in subsurface reservoirs is critical for applications like CO2 sequestration and wastewater injection. Managing the pressures by controlling injection/extraction are challenging because of complex heterogeneity in the subsurface. We use differentiable programming with a full-physics model and machine learning to determine the fluid extraction rates that prevent over-pressurization.
  arXiv  Detail & Related papers  (2022-06-21T20:38:13Z)
• Dynaformer: A Deep Learning Model for Ageing-aware Battery Discharge Prediction [2.670887944566458]
  We introduce a novel Transformer-based deep learning architecture which is able to simultaneously infer the ageing state from a limited number of voltage/current samples. Our experiments show that the trained model is effective for input current profiles of different complexities and is robust to a wide range of degradation levels.
  arXiv  Detail & Related papers  (2022-06-01T15:31:06Z)
• Enhanced physics-constrained deep neural networks for modeling vanadium redox flow battery [62.997667081978825]
  We propose an enhanced version of the physics-constrained deep neural network (PCDNN) approach to provide high-accuracy voltage predictions. The ePCDNN can accurately capture the voltage response throughout the charge--discharge cycle, including the tail region of the voltage discharge curve.
  arXiv  Detail & Related papers  (2022-03-03T19:56:24Z)
• Nuclear two point correlation functions on a quantum-computer [105.89228861548395]
  We use current quantum hardware and error mitigation protocols to calculate response functions for a highly simplified nuclear model. In this work we use current quantum hardware and error mitigation protocols to calculate response functions for a modified Fermi-Hubbard model in two dimensions with three distinguishable nucleons on four lattice sites.
  arXiv  Detail & Related papers  (2021-11-04T16:25:33Z)
• Physics-informed CoKriging model of a redox flow battery [68.8204255655161]
  Redox flow batteries (RFBs) offer the capability to store large amounts of energy cheaply and efficiently. There is a need for fast and accurate models of the charge-discharge curve of a RFB to potentially improve the battery capacity and performance. We develop a multifidelity model for predicting the charge-discharge curve of a RFB.
  arXiv  Detail & Related papers  (2021-06-17T00:49:55Z)
• Simple statistical models and sequential deep learning for Lithium-ion batteries degradation under dynamic conditions: Fractional Polynomials vs Neural Networks [1.8899300124593648]
  Longevity and safety of Lithium-ion batteries are facilitated by efficient monitoring and adjustment of the battery operating conditions. It is crucial to implement fast and accurate algorithms for State of Health (SoH) monitoring on the Battery Management System. This paper proposes and compares two data-driven approaches: a Long Short-Term Memory neural network, and a Multivariable Fractional Polynomial regression.
  arXiv  Detail & Related papers  (2021-02-16T12:26:23Z)
• Machine learning pipeline for battery state of health estimation [3.0238880199349834]
  We design and evaluate a machine learning pipeline for estimation of battery capacity fade. The pipeline estimates battery SOH with an associated confidence interval by using two parametric and two non-parametric algorithms. When deployed on cells operated under the fast-charging protocol, the best model achieves a root mean squared percent error of 0.45%.
  arXiv  Detail & Related papers  (2021-02-01T13:50:56Z)
• Universal Battery Performance and Degradation Model for Electric Aircraft [52.77024349608834]
  Design, analysis, and operation of electric vertical takeoff and landing aircraft (eVTOLs) requires fast and accurate prediction of Li-ion battery performance. We generate a battery performance and thermal behavior dataset specific to eVTOL duty cycles. We use this dataset to develop a battery performance and degradation model (Cellfit) which employs physics-informed machine learning.
  arXiv  Detail & Related papers  (2020-07-06T16:10:54Z)
• Training End-to-End Analog Neural Networks with Equilibrium Propagation [64.0476282000118]
  We introduce a principled method to train end-to-end analog neural networks by gradient descent. We show mathematically that a class of analog neural networks (called nonlinear resistive networks) are energy-based models. Our work can guide the development of a new generation of ultra-fast, compact and low-power neural networks supporting on-chip learning.
  arXiv  Detail & Related papers  (2020-06-02T23:38:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.