Physics-informed CoKriging model of a redox flow battery
- URL: http://arxiv.org/abs/2106.09188v1
- Date: Thu, 17 Jun 2021 00:49:55 GMT
- Title: Physics-informed CoKriging model of a redox flow battery
- Authors: Amanda A. Howard, Alexandre M. Tartakovsky
- Abstract summary: 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.
- Score: 68.8204255655161
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Redox flow batteries (RFBs) offer the capability to store large amounts of
energy cheaply and efficiently, however, 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. In the multifidelity model, we
use the Physics-informed CoKriging (CoPhIK) machine learning method that is
trained on experimental data and constrained by the so-called
"zero-dimensional" physics-based model. Here we demonstrate that the model
shows good agreement with experimental results and significant improvements
over existing zero-dimensional models. We show that the proposed model is
robust as it is not sensitive to the input parameters in the zero-dimensional
model. We also show that only a small amount of high-fidelity experimental
datasets are needed for accurate predictions for the range of considered input
parameters, which include current density, flow rate, and initial
concentrations.
Related papers
- Integrating Physics and Data-Driven Approaches: An Explainable and Uncertainty-Aware Hybrid Model for Wind Turbine Power Prediction [1.1270209626877075]
The rapid growth of the wind energy sector underscores the urgent need to optimize turbine operations.
Traditional empirical and physics-based models offer approximate predictions of power generation based on wind speed.
Data-driven machine learning methods present a promising avenue for improving wind turbine modeling.
arXiv Detail & Related papers (2025-02-11T08:16:48Z) - Optimizing Hyperparameters for Quantum Data Re-Uploaders in Calorimetric Particle Identification [11.099632666738177]
We present an application of a single-qubit Data Re-Uploading (QRU) quantum model for particle classification in calorimetric experiments.
This model requires minimal qubits while delivering strong classification performance.
arXiv Detail & Related papers (2024-12-16T23:10:00Z) - A Physics-informed Diffusion Model for High-fidelity Flow Field
Reconstruction [0.0]
We propose a diffusion model which only uses high-fidelity data at training.
With different configurations, our model is able to reconstruct high-fidelity data from either a regular low-fidelity sample or a sparsely measured sample.
Our model can produce accurate reconstruction results for 2d turbulent flows based on different input sources without retraining.
arXiv Detail & Related papers (2022-11-26T23:14:18Z) - 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) - MoEfication: Conditional Computation of Transformer Models for Efficient
Inference [66.56994436947441]
Transformer-based pre-trained language models can achieve superior performance on most NLP tasks due to large parameter capacity, but also lead to huge computation cost.
We explore to accelerate large-model inference by conditional computation based on the sparse activation phenomenon.
We propose to transform a large model into its mixture-of-experts (MoE) version with equal model size, namely MoEfication.
arXiv Detail & Related papers (2021-10-05T02:14:38Z) - Physics-constrained deep neural network method for estimating parameters
in a redox flow battery [68.8204255655161]
We present a physics-constrained deep neural network (PCDNN) method for parameter estimation in the zero-dimensional (0D) model of the vanadium flow battery (VRFB)
We show that the PCDNN method can estimate model parameters for a range of operating conditions and improve the 0D model prediction of voltage.
We also demonstrate that the PCDNN approach has an improved generalization ability for estimating parameter values for operating conditions not used in the training.
arXiv Detail & Related papers (2021-06-21T23:42:58Z) - Hybrid Physics and Deep Learning Model for Interpretable Vehicle State
Prediction [75.1213178617367]
We propose a hybrid approach combining deep learning and physical motion models.
We achieve interpretability by restricting the output range of the deep neural network as part of the hybrid model.
The results show that our hybrid model can improve model interpretability with no decrease in accuracy compared to existing deep learning approaches.
arXiv Detail & Related papers (2021-03-11T15:21:08Z) - Robust Data-Driven Error Compensation for a Battery Model [0.0]
Today's massively collected battery data is not yet used for more accurate and reliable simulations.
A data-driven error model is introduced enhancing an existing physically motivated model.
A neural network compensates the existing dynamic error and is further limited based on a description of the underlying data.
arXiv Detail & Related papers (2020-12-31T16:11:36Z) - Battery Model Calibration with Deep Reinforcement Learning [5.004835203025507]
We implement a Reinforcement Learning-based framework for reliably and efficiently inferring calibration parameters of battery models.
The framework enables real-time inference of the computational model parameters in order to compensate the reality-gap from the observations.
arXiv Detail & Related papers (2020-12-07T19:26:08Z) - 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)
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.