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A Distributed Model Identification Algorithm for Multi-Agent Systems: Preprint
In this study, we investigate agent-based approach for system model identification with emphasis on power distribution system applications. Departing from conventional practices of relying on historical data for offline model identification, we adopt online update approach utilizing real-time data by employing the latest data points for gradient computation. This methodology offers advantages including a large reduction in the communication network's bandwidth requirements by minimizing the data exchanged at each iteration and enabling the model to adapt in real-time to disturbances. Furthermore, we extend our model identification process from linear frameworks to more complex non-linear convex models. This extension is validated through numerical studies demonstrating improved control performance for a synthetic IEEE test case.
A Privacy Preserving Distributed Model Identification Algorithm for Power Distribution Systems
Distributed control/optimization is a promising approach for network systems due to its advantages over centralized schemes, such as robustness, cost-effectiveness, and improved privacy. However, distributed methods can have drawbacks, such as slower convergence rates due to limited knowledge of the overall network model. Additionally, ensuring privacy in the communication of sensitive information can pose implementation challenges. To address this issue, we propose a distributed model identification algorithm that enables each agent to identify the sub-model that characterizes the relationship between its local control and the overall system outputs. The proposed algorithm maintains the privacy of local agents by only communicating through dummy variables. We demonstrate the efficacy of our algorithm in the context of power distribution systems by applying it to the voltage regulation of a modified IEEE distribution system. The proposed algorithm is well-suited to the needs of power distribution controls and offers an effective solution to the challenges of distributed model identification in network systems.
A Privacy Preserving Distributed Model Identification Algorithm for Power Distribution Systems: Preprint
Distributed control/optimization is a promising approach for network systems due to its advantages over centralized schemes, such as robustness, cost-effectiveness, and improved privacy. However, distributed methods can have drawbacks, such as slower convergence rates due to limited knowledge of the overall network model. Additionally, ensuring privacy in the communication of sensitive information can pose implementation challenges. To address this issue, we propose a distributed model identification algorithm that enables each agent to identify the sub-model that characterizes the relationship between its local control and the overall system outputs. The proposed algorithm maintains the privacy of local agents by only communicating through dummy variables. We demonstrate the efficacy of our algorithm in the context of power distribution systems by applying it to the voltage regulation of a modified IEEE distribution system. The proposed algorithm is well-suited to the needs of power distribution controls and offers an effective solution to the challenges of distributed model identification in network systems.
Influence of initial conditions on data-driven model identification and information entropy for ideal mhd problems
Data-driven methods of model identification are able to discern governing dynamics of a system from data. Such methods are well suited to help us learn about systems with unpredictable evolution or systems with ambiguous governing dynamics given our current understanding. Many plasma problems of interest fall into these categories as there are a wide range of models that exist, however each model is only useful in a certain regime and often limited by computational complexity. To ensure data-driven methods align with theory, they must be consistent and predictable when acting on data whose governing dynamics are known. Weak Sparse Identification of Nonlinear Dynamics (WSINDy) is a recently developed data-driven method that has shown promise in learning governing dynamics from data with high noise levels [1]. This work examines how WSINDy acts on ideal MHD test problems as the initial conditions are varied and specifies limiting requirements for successful equation identification. Furthermore, it is hard to recover the governing dynamics from data that emphasize a single dominant behavior. In these low information cases, Shannon information entropy is able to pick up on the redundancies in the data that affect recoverability.
Model Identification of Inverter Nonlinear Control Dynamics
To comprehensively predict in silico potential disruptive dynamic performance of converter-based generation and develop solutions, the application of electromagnetic transient programs is desirable. However, these accurate simulations highly depend on inverter models of actual field implementations, which are often unavailable because they are proprietary. To address this problem, we propose a time-domain model identification method that can be used to determine the control models of inverters in continuous time from multiple experimental studies. The focus is on identifying the dynamic representation of controls because they dominate the dynamic response of renewable assets. To illustrate the applicability of the technique, we consider a subclass of inverter controllers for autonomous grid applications that rely on nonlinear oscillators.
Challenging Practices of Algebraic Battery Life Models through Statistical Validation and Model Identification via Machine-Learning
Various modeling techniques are used to predict the capacity fade of Li-ion batteries. Algebraic reduced-order models, which are inherently interpretable and computationally fast, are ideal for use in battery controllers, technoeconomic models, and multi-objective optimizations. For Li-ion batteries with graphite anodes, solid-electrolyte-interphase (SEI) growth on the graphite surface dominates fade. This fade is often modeled using physically informed equations, such as square-root of time for predicting solvent-diffusion limited SEI growth, and Arrhenius and Tafel-like equations predicting the temperature and state-of-charge rate dependencies. In some cases, completely empirical relationships are proposed. However, statistical validation is rarely conducted to evaluate model optimality, and only a handful of possible models are usually investigated. This article demonstrates a novel procedure for automatically identifying reduced-order degradation models from millions of algorithmically generated equations via bi-level optimization and symbolic regression. Identified models are statistically validated using cross-validation, sensitivity analysis, and uncertainty quantification via bootstrapping. On a LiFePO 4 /Graphite cell calendar aging data set, automatically identified models utilizing square-root, power law, stretched exponential, and sigmoidal functions result in greater accuracy and lower uncertainty than models identified by human experts, and demonstrate that previously known physical relationships can be empirically "rediscovered" using machine learning.
Handling noisy data in sparse model identification using subsampling and co-teaching
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Comparative Study of Nonlinear Modal Modeling Identification Techniques from Force Appropriation Experiments
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Quantifying Hall Conductivity Using Data-Driven Model Identification
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Comparative Study of Nonlinear Modal Modeling Identification Techniques from Force Appropriation Experiments
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DEPRECATED AI-Batt-OS (Autonomous Identification of Battery Life Models - Open Source) [SWR 21-17]
DEPRECATED. This repository was archived by the owner on Jun 30, 2026. It is now read-only. Open source implementation of some of the methods utilized by AI-Batt, a battery lifetime modeling and analysis toolkit provided by the National Laboratory of the Rockies (NLR). This software demonstrates the use of bi-level optimization and symbolic regression techniques to semi-autonomously identify algebraic models predicting the capacity fade of lithium-ion batteries during calendar aging. Modeling the degradation of batteries is a complex task, due to the difficulty in separating the time-dependent and time-independent factors impacting cell level degradation, across multiple data series with different numbers of measurements and/or data quality. Bi-level optimization enables model parameters to be optimized to either the entire data set or to individual data series, allowing statistical disambiguation of global behaviors (data series independent) and local behaviors (data series dependent). Symbolic regression is used to automatically search for optimal low-dimesional models predicting the variation of locally optimized parameters versus time-independent experimental variables from millions of possible models, resulting in a more accurate and repeatable model identification process than is possible by a manual search. The provided tools also implement cross-validation and bootstrap resampling schemes, empowering statistical model comparison/selection and quantification of model uncertainties. An example script replicates the results from the manuscript "Challenging Practices of Algebraic Battery Life Models through Statistical Validation and Model Identification via Machine-Learning", submitted to ECS. All code is written in MATLAB. Requires the Statistics and Machine Learning Toolbox. Contact Dr. Paul Gasper at Paul.Gasper@nlr.gov for any questions.
Machine-Learning Assisted Identification of Accurate Battery Lifetime Models with Uncertainty
Reduced-order battery lifetime models, which consist of algebraic expressions for various aging modes, are widely utilized for extrapolating degradation trends from accelerated aging tests to real-world aging scenarios. Identifying models with high accuracy and low uncertainty is crucial for ensuring that model extrapolations are believable, however, it is difficult to compose expressions that accurately predict multivariate data trends; a review of cycling degradation models from literature reveals a wide variety of functional relationships. Here, a machine-learning assisted model identification method is utilized to fit degradation in a stand-out LFP-Gr aging data set, with uncertainty quantified by bootstrap resampling. The model identified in this work results in approximately half the mean absolute error of a human expert model. Models are validated by converting to a state-equation form and comparing predictions against cells aging under varying loads. Parameter uncertainty is carried forward into an energy storage system simulation to estimate the impact of aging model uncertainty on system lifetime. The new model identification method used here reduces life-prediction uncertainty by more than a factor of three (86% ± 5% relative capacity at 10 years for human-expert model, 88.5% ± 1.5% for machine-learning assisted model), empowering more confident estimates of energy storage system lifetime.
A Perspective on Traditional and Data Driven Electrochemical Modeling and Analysis
To understand the behavior of electrochemical systems, we need to reduce the dimensionality of the measured current-voltage-time (I-V-t) data by fitting models, thus enabling us to analyze and compare the governing physics. Traditionally, the process for this is an 'expert first' approach: defining the model and its explicit assumptions based on inductive reasoning or empirical observation, fitting small portions of the I-V-t data where assumptions are most valid or carefully designing experiments to enforce key assumptions, and then interpreting the model parameters. However, modern data-driven methods enable a new paradigm: a 'data first' approach, where the latent behaviors governing the system's measured response are identified directly using machine-learning models that optimize both model structure and parameters from the I-V-t data, guaranteeing that the learned model explains as much of the observed system response as possible. After model identification, the model can then be interrogated by an expert to connect observed behaviors with underlying physics. This talk will review several different types of electrochemical analysis (electrochemical impedance, differential voltage-capacity, electrochemical kinetics) and compare the traditional and data-driven methods for analyzing the data.
Fuzzy Cognitive Model for Identification of Destabilizing Factors
A fuzzy cognitive model of industrial emergencies is proposed. The model permits analysis of possible measures for risk reduction and the elimination of emergencies and accidents at industrial enterprises. The relationships among and within groups of concepts in the fuzzy cognitive model are established. The applicability of the new fuzzy cognitive model is discussed.
Using system identification in modeling the yaw response of tail fins for small wind turbines with bearing friction
Here, we describe three main developments of our previous study of the nonlinear yaw dynamics of the tail fins for small wind turbines [Khedr et al., J. Renewable Sustainable Energy 16, 053305 (2024)]. First, the model constants derived from archived computational and experimental studies are adjusted by employing system identification (SI) to maximize the model's agreement with wind tunnel tests. This adjustment was done for high wind speeds, where yaw bearing friction can be ignored. When starting a turbine at low wind speed, however, friction can become important. Our second development is to implement a model for the frictional resistive torque and use SI to maximize its accuracy. These developments used wind tunnel experiments on generic delta, elliptical, and rectangular planforms that were described by Khedr et al. [J. Renewable Sustainable Energy 16, 053305 (2024)]. Since the aerodynamic and friction models employ a large number of constants, we describe ways to constrain the values using linearized solutions of the response equations for small and large yaw angles. Third, we test the generality of the aerodynamic and friction modeling using the complex planform from a commercial small turbine, for which limited theoretical and computational guidance is available in selecting the model constants. The model implemented with SI is shown to provide an accurate description of the yaw response of the complex planform. Guidelines are given for the use of wind tunnel tests to determine the model constants for tail fins of any planform.
Building thermal dynamics modeling with deep transfer learning using a large residential smart thermostat dataset
Understanding thermal dynamics and obtaining the computational model of residential buildings enable its scaled application in energy retrofits, control optimization and decarbonization. In this paper, we present a deep learning approach to model building thermal dynamics with smart thermostat data collected from residential buildings, with the goal to investigate model generalizability. In the first stage, we developed and compared different Deep Learning architectures including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) models and CNN-LSTM to predict indoor air temperature in a multi-step time horizon. In the second stage, we implemented a Transfer Learning (TL) process, which aims to improve the prediction performance on a new set of buildings (targets), exploiting the knowledge of related or similar buildings (sources). Different TL strategies and source model identification methods were investigated. The study showed that the CNN-LSTM performed the best among the architectures compared, with an average Mean Absolute Error (MAE) of 0.26 °C for one-hour-ahead (twelve 5-min future steps) predictions. Furthermore, the results showed that freezing the LSTM layer and fine-tuning the other layers of the CNN-LSTM achieved the best performance among four TL strategies, which further improved the performance with respect to a machine learning approach by 10%, and proving the effectiveness and generalizability of the proposed approach. A comparison of three different source model identification methods showed that randomly selecting source models constrained by similar building characteristics can provide good TL performance while retaining simplicity comparing with other quantitative source identification methods.