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Graph-Based Modeling, Control, and Optimization for Multi-Domain and Multi-Timescale Energy Systems

T0 review · reviewed 2026-06-29 · grok-4.3

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Pith's one-line read Graph-based models combine energy conservation laws with explicit network representations to handle multi-domain systems.

desk verdict This is a tutorial consolidating a decade-old graph-based modeling method for multi-domain energy systems plus an open-source toolbox, with no new results or derivations. read the letter →

arxiv 2605.27017 v1 pith:UQQ7WCVV submitted 2026-05-26 eess.SY cs.SY

Joseph M. Pisani , Christopher T. Aksland , Philip M. Renkert , Joseph Broniszewski , Vismay Vyas , Andrew G. Alleyne , Donald J. Docimo , Justin P. Koeln
show 2 more authors
Neera Jain Herschel C. Pangborn
This is my paper · ORCID
classification eess.SYcs.SY
keywords graph-basedmodelingmulti-domainenergysystemsmodelpredictivecontrolconservationnetworkrepresentationsystemoptimizationtutorial
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This tutorial presents a graph-based modeling method for energy systems that operate across electrical, thermal, and mechanical domains at multiple timescales. The approach links transient conservation principles directly to a mathematical description of how energy is stored and transferred through the system network. Developed and tested over more than a decade, the method is shown through component and system examples from thermal and electro-mechanical applications. The paper surveys its use in control, estimation, and optimization tasks and describes an associated open-source toolbox.

What carries the argument

The graph-based model, which encodes energy storage and transfer as a network of nodes and edges while enforcing conservation laws across domains.

What would settle it

Direct comparison of graph-model predictions versus measured time-series data on a multi-domain testbed, such as an electro-thermal vehicle subsystem, that reveals systematic mismatches in transient behavior at relevant timescales.

Watch

Extended reading notes

Core claim

The graph-based approach combines transient energy conservation with an explicit mathematical representation of the network by which energy is stored and transferred within a system to facilitate the modeling, analysis, control, estimation, optimization, and design of multi-domain energy systems.

Load-bearing premise

The same graph structure delivers adequate accuracy for dynamics in every physical domain without requiring separate adjustments that would break the unified representation.

Editorial extensions

If this is right

  • Decentralized and hierarchical model predictive control becomes feasible for high-dimensional multi-domain systems.
  • Design optimization and control co-design can be performed within the same modeling framework.
  • Component models for single-phase thermal, two-phase thermal, and electro-mechanical systems follow from the same network construction rules.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A shared modeling language across domains could reduce the need for custom interfaces when integrating subsystems from different engineering teams.
  • The open-source toolbox may allow practitioners to generate and test controllers for new energy architectures without starting from domain-specific simulators.
  • Further case studies on built infrastructure or vehicle fleets would test whether the network representation holds when component interactions span more than two physical domains simultaneously.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

0 major / 0 minor

Summary. The paper is a tutorial presenting a graph-based modeling approach for multi-domain and multi-timescale energy systems in vehicles and built infrastructure. It claims that combining transient energy conservation with an explicit mathematical representation of the energy storage and transfer network facilitates modeling, analysis, control, estimation, optimization, and design. The manuscript provides a mathematical overview, examples of component and system models from the literature (single-phase thermal, two-phase thermal, electro-mechanical), a survey of applications in decentralized/hierarchical MPC, design optimization and control co-design, and describes an open-source toolbox.

Significance. If the claims hold, the work offers a unified framework for high-dimensional multi-physics energy systems that has been matured over more than a decade of research across institutions and companies. A notable strength is the open-source toolbox for model generation and analysis, which supports reproducibility. The survey of control and optimization applications demonstrates practical reach.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive assessment of the manuscript, recognition of its significance as a matured framework, and recommendation to accept. We are pleased that the tutorial's coverage of the mathematical foundation, component examples, control/optimization applications, and open-source toolbox was viewed favorably.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity identified

full rationale

The paper is a tutorial surveying a graph-based modeling framework matured over more than a decade across multiple institutions and companies. Its claims rest on external prior research, literature examples, and an open-source toolbox rather than any new derivations, predictions, or uniqueness theorems that reduce by construction to fitted parameters or self-citations within this document. No load-bearing steps exhibit self-definitional, fitted-input, or ansatz-smuggling patterns.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

The approach relies on standard physical conservation laws and graph representations from prior literature; no free parameters, invented entities, or ad-hoc axioms are identifiable from the abstract alone.

assumptions (2)
  • domain assumption Transient energy conservation laws apply to the multi-domain systems under consideration.
    The abstract states the approach combines transient energy conservation with network representation.
  • standard math Graph theory provides an explicit mathematical representation of energy storage and transfer networks.
    The core of the graph-based modeling as described.

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Cite this review

Pith. "Pith review of Graph-Based Modeling, Control, and Optimization for Multi-Domain and Multi-Timescale Energy Systems." pith.science (2026). https://pith.science/paper/UQQ7WCVV

@misc{pith2026260527017,
  author       = {Pith},
  title        = {Pith review of: Graph-Based Modeling, Control, and Optimization for Multi-Domain and Multi-Timescale Energy Systems},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UQQ7WCVV}},
  note         = {Machine review of arXiv:2605.27017}
}
read the original abstract

Modern energy systems in vehicles and built infrastructure are governed by high-dimensional dynamics spanning multiple physical domains (e.g., electrical, thermal, mechanical) and timescales. This tutorial paper presents a graph-based modeling approach created to facilitate the modeling, analysis, control, estimation, optimization, and design of these systems. Matured and validated through more than a decade of research spanning multiple academic institutions and companies, the graph-based approach combines transient energy conservation with an explicit mathematical representation of the network by which energy is stored and transferred within a system. Following a mathematical overview of graph-based models, examples of multi-domain component and system models from the recent literature are presented, including single-phase thermal systems, two-phase thermal systems, and electro-mechanical systems. This is followed by a survey of recent applications for decentralized and hierarchical model predictive control, design optimization, and control co-design. Lastly, the paper describes an open-source toolbox created to facilitate the generation and analysis of graph-based models.

Figures

Figures reproduced from arXiv: 2605.27017 by the authors.

Figure 1
Figure 1. Notional example of a graph-based model. [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Visualization of vertex (top) and edge (bottom) [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 5
Figure 5. Example graph-based model for a cold plate with [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗
Figures from the paper (11 more)
Figure 4
Figure 4. Figure 4: Schematic (top) and corresponding graph-based [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 6
Figure 6. Figure 6: Example graph-based model for a cold plate with [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]
Figure 7
Figure 7. Figure 7: Example graph-based models for (a) a mass-spring [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]
Figure 8
Figure 8. Figure 8: Block diagram showing the negative feedback con [PITH_FULL_IMAGE:figures/full_fig_p008_8.png]
Figure 9
Figure 9. Figure 9: Block diagram showing the negative feedback con [PITH_FULL_IMAGE:figures/full_fig_p008_9.png]
Figure 10
Figure 10. Figure 10: Electric vehicle powertrain subsystem design [PITH_FULL_IMAGE:figures/full_fig_p010_10.png]
Figure 11
Figure 11. Figure 11: Design options (black) and Pareto front (cyan) [PITH_FULL_IMAGE:figures/full_fig_p010_11.png]
Figure 13
Figure 13. Figure 13: Algebraic model with boundary condition and alge [PITH_FULL_IMAGE:figures/full_fig_p011_13.png]
Figure 12
Figure 12. Figure 12: System graph corresponding to formulation with [PITH_FULL_IMAGE:figures/full_fig_p011_12.png]
Figure 14
Figure 14. Figure 14: 3D histogram depicting the distribution of 164 sys [PITH_FULL_IMAGE:figures/full_fig_p011_14.png]
Figure 15
Figure 15. Figure 15: Example Simulink library block mask. Both partial derivatives are evaluated at the linearization point x0 and u0 and are written as A = ∂f(x,u) ∂x [PITH_FULL_IMAGE:figures/full_fig_p016_15.png]

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Reference graph

Works this paper leans on

66 extracted references · 1 canonical work pages

  1. [1]

    INVENT modeling, simulation, analysis and optimization,

    E. A. Walters and S. Iden, “INVENT modeling, simulation, analysis and optimization,” in 48th AIAA Aerospace Sciences Meeting , (Or- lando, Florida, USA), pp. 1–11, Jan. 2010

    2010

  2. [2]

    Dynamic thermal management for aerospace technology: Review and outlook,

    J. Doty, K. Y erkes, L. Byrd, J. Murthy, A. Alleyne, M. Wolff, S. Heister, and T. S. Fisher, “Dynamic thermal management for aerospace technology: Review and outlook,” Journal of Thermophysics and Heat Transfer , vol. 31, pp. 86–98, Jan. 2017

    2017

  3. [3]

    Control as an enabler for electrified mobility,

    A. G. Alleyne and C. T. Aksland, “Control as an enabler for electrified mobility,” Annual Review of Control, Robotics, and Autonomous Systems, vol. 5, pp. 659–688, May 2022

    2022

  4. [4]

    J. T. Wen and S. Mishra, eds., Intelligent Building Control Systems . Springer, 2018

    2018

  5. [5]

    Bond-graph modeling,

    P . J. Gawthrop and G. P . Bevan, “Bond-graph modeling,”IEEE Control Systems Magazine , vol. 27, pp. 24–45, Apr. 2007

    2007

  6. [6]

    Port-Hamiltonian systems theory: An introductory overview,

    A. van der Schaft and D. Jeltsema, “Port-Hamiltonian systems theory: An introductory overview,” F oundations and Trends in Systems and Control, vol. 1, pp. 173–378, Jun. 2014

    2014

  7. [7]

    Modelica-a general object-oriented language for continuous and discrete-event system modeling and simulation,

    P . Fritzson and P . Bunus, “Modelica-a general object-oriented language for continuous and discrete-event system modeling and simulation,” Proceedings of the 35th Annual Simulation Symposium , pp. 365–380, Apr. 2002

    2002

  8. [8]

    MA TLAB version: 25.1.0 (2025a),

    The MathWorks Inc., “MA TLAB version: 25.1.0 (2025a),” 2025

    2025

Show all 66 references
  1. [9]

    Experimental validation of graph-based modeling for thermal fluid power flow systems,

    J. P . Koeln, M. A. Williams, H. C. Pangborn, and A. G. Alleyne, “Experimental validation of graph-based modeling for thermal fluid power flow systems,” in Dynamic Systems and Control Conference , (Minneapolis, Minnesota, USA), Oct. 2016

    2016

  2. [10]

    A novel framework for simultaneous topology and sizing optimization of complex, multi-domain systems-of-systems,

    D. J. Docimo, Z. Kang, K. A. James, and A. G. Alleyne, “A novel framework for simultaneous topology and sizing optimization of complex, multi-domain systems-of-systems,” Journal of Mechanical Design, vol. 142, Sep. 2020

    2020

  3. [11]

    Combined design and open-loop control optimization for propulsion, power, and thermal management of hybrid-electric aircraft,

    A. F. Thompson, A. J. Iezzi, H. C. Pangborn, R. K. Madabhushi, and O. V . Atassi, “Combined design and open-loop control optimization for propulsion, power, and thermal management of hybrid-electric aircraft,” in 2023 IEEE Conference on Control Technology and Ap- plications (C...

    2023

  4. [12]

    Dynamical graph models of aircraft electrical, thermal, and turboma- chinery components,

    M. A. Williams, J. P . Koeln, H. C. Pangborn, and A. G. Alleyne, “Dynamical graph models of aircraft electrical, thermal, and turboma- chinery components,” Journal of Dynamic Systems, Measurement, and Control, vol. 140, Apr. 2017

    2017

  5. [13]

    Electro-thermal graph-based mod- eling for hierarchical control with application to an electric vehicle,

    D. J. Docimo and A. G. Alleyne, “Electro-thermal graph-based mod- eling for hierarchical control with application to an electric vehicle,” in IEEE Conference on Control Technology and Applications (CCTA) , (Copenhagen, Denmark), pp. 812–819, Aug. 2018

    2018

  6. [14]

    Framework for integrated plant and control optimization of electro-thermal systems: An energy storage system case study,

    C. Laird, Z. Kang, K. A. James, and A. G. Alleyne, “Framework for integrated plant and control optimization of electro-thermal systems: An energy storage system case study,” Energy, vol. 258, Nov. 2022

    2022

  7. [15]

    Graph-based design and control optimization of a hybrid electrical energy storage system,

    C. Laird, D. Docimo, C. T. Aksland, and A. G. Alleyne, “Graph-based design and control optimization of a hybrid electrical energy storage system,” in ASME 2020 Dynamic Systems and Control Conference , (Virtual, Online), Oct. 2020

    2020

  8. [16]

    Graph-based electro-mechanical modeling of a hybrid unmanned aerial vehicle for real-time applications,

    C. T. Aksland, T. W. Bixel, L. C. Raymond, M. A. Rottmayer, and A. G. Alleyne, “Graph-based electro-mechanical modeling of a hybrid unmanned aerial vehicle for real-time applications,” in 2019 American Control Conference (ACC) , (Philadelphia, PA, USA), pp. 4253–4259, July 2019

    2019

  9. [17]

    Graph-based hierarchical control of electrified aircraft systems with automated timescale decom- position,

    Y . Y u, S. Park, D. Huang, and H. Pangborn, “Graph-based hierarchical control of electrified aircraft systems with automated timescale decom- position,” in AIAA A VIATION 2023 F orum, (San Diego, CA, USA), June 2023

    2023

  10. [18]

    Hierarchical control of aircraft electro-thermal systems,

    J. P . Koeln, H. C. Pangborn, M. A. Williams, M. L. Kawamura, and A. G. Alleyne, “Hierarchical control of aircraft electro-thermal systems,” IEEE Transactions on Control Systems Technology , vol. 28, pp. 1218–1232, July 2020

    2020

  11. [19]

    Hierarchical control of multi-domain power flow in mobile systems: Part II aircraft application,

    M. A. Williams, J. P . Koeln, and A. G. Alleyne, “Hierarchical control of multi-domain power flow in mobile systems: Part II aircraft application,” in Dynamic Systems and Control Conference , (Columbus, OH, USA), Oct. 2015

    2015

  12. [20]

    Hierarchical model-based predictive controller for a hybrid uav powertrain,

    C. T. Aksland and A. G. Alleyne, “Hierarchical model-based predictive controller for a hybrid uav powertrain,” Control Engineering Practice , vol. 115, p. 104883, Oct. 2021

    2021

  13. [21]

    Hierarchical predictive control of an unmanned aerial vehicle integrated power, propulsion, and thermal management system,

    C. T. Aksland, P . J. Tannous, M. J. Wagenmaker, H. C. Pangborn, and A. G. Alleyne, “Hierarchical predictive control of an unmanned aerial vehicle integrated power, propulsion, and thermal management system,” IEEE Transactions on Control Systems Technology , vol. 31, pp. 1280–...

    2023

  14. [22]

    Modeling, cross- validation, and optimization of a shipboard integrated energy system cooling network,

    S. Y ang, M. B. Chagas, and J. C. Ordonez, “Modeling, cross- validation, and optimization of a shipboard integrated energy system cooling network,” Applied Thermal Engineering , vol. 145, pp. 516– 527, Dec. 2018

    2018

  15. [23]

    Plant and controller optimization for power and energy systems with model predictive control,

    D. J. Docimo, Z. Kang, K. A. James, and A. G. Alleyne, “Plant and controller optimization for power and energy systems with model predictive control,” Journal of Dynamic Systems, Measurement, and Control, vol. 143, Aug. 2021

    2021

  16. [24]

    Topology and sizing optimization of thermal and electric energy systems for battery electric vehicle,

    F. Belkacem, H. Schäfer, T. Hellberg, and M. Meywerk, “Topology and sizing optimization of thermal and electric energy systems for battery electric vehicle,” in 2025 FKFS Conference on V ehicle Aerodynamics and Thermal Management , (Leinfelden-Echterdingen, Germany), Oct. 2025

    2025

  17. [25]

    A design framework with embedded hierarchical con- trol architecture optimization,

    D. J. Docimo, “A design framework with embedded hierarchical con- trol architecture optimization,” in 2022 American Control Conference (ACC), (Atlanta, GA, USA), pp. 3184–3191, June 2022

    2022

  18. [26]

    Hi- erarchical estimation for complex multi-domain dynamical systems,

    P . J. Tannous, D. J. Docimo, H. C. Pangborn, and A. G. Alleyne, “Hi- erarchical estimation for complex multi-domain dynamical systems,” in 2019 Annual American Control Conference (ACC) , (Philadelphia, PA, USA), July 2019

    2019

  19. [27]

    A multi-state graph-based framework for dynamic modeling of turboma- chinery components,

    A. Bolander, T. Bird, W. A. Malatesta, K. McCarthy, and N. Jain, “A multi-state graph-based framework for dynamic modeling of turboma- chinery components,” in AIAA SCITECH 2024 F orum , (Orlando, FL, USA), Jan. 2024

    2024

  20. [28]

    Dynamical graph-based models of brayton cycle systems,

    R. D. Smith and A. G. Alleyne, “Dynamical graph-based models of brayton cycle systems,” in 2022 American Control Conference (ACC) , (Atlanta, GA, USA), pp. 4802–4807, June 2022

    2022

  21. [29]

    A graph-based approach for dynamic compressor modeling in vapor compression systems,

    C. T. Aksland, J. P . Koeln, and A. G. Alleyne, “A graph-based approach for dynamic compressor modeling in vapor compression systems,” in ASME 2017 Dynamic Systems and Control Conference , (Tysons, V A, USA), Oct. 2017

    2017

  22. [30]

    A second law approach to dynamic optimization of enumerated aircraft thermal management system architectures,

    A. Bolander, T. J. Bird, R. Manion, M. Glebocki, K. McCarthy, and N. Jain, “A second law approach to dynamic optimization of enumerated aircraft thermal management system architectures,” IEEE Transactions on Transportation Electrification , vol. 11, pp. 12657– 12666, Oct. 2025

    2025

  23. [31]

    An approach to simultaneous topology, parametric, and feedback control co-design: Applications to conservation-based energy systems,

    C. T. Aksland and A. G. Alleyne, “An approach to simultaneous topology, parametric, and feedback control co-design: Applications to conservation-based energy systems,” IEEE Control Systems Magazine , vol. 45, pp. 28–55, Jun. 2025

    2025

  24. [32]

    Control-oriented graph-based modelling of building energy system: a conservation-based framework for multi-zone buildings,

    Z. Echreshavi, E. Sisti, M. F. Palangari, R. Carli, and M. Rampazzo, “Control-oriented graph-based modelling of building energy system: a conservation-based framework for multi-zone buildings,” in 5th REHVA HVAC World Congress - CLIMA 2025, (Milan, Italy), pp. 137– 147, June 2025

    2025

  25. [33]

    Decomposition-based control co- design of energy systems using graph models,

    K. R. Smith and A. G. Alleyne, “Decomposition-based control co- design of energy systems using graph models,” in 2025 American Control Conference (ACC) , (Denver, CO, USA), pp. 4743–4749, July 2025

    2025

  26. [34]

    Development of a graph- based modeling framework for transient exergy analysis,

    A. R. Manion, W. A. Malatesta, and N. Jain, “Development of a graph- based modeling framework for transient exergy analysis,” in 2022 21st IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (iTherm) , (San Diego, CA, USA), pp. 1–10, May 2022

    2022

  27. [35]

    Experimental validation of graph-based hierarchical control for ther- mal management,

    H. C. Pangborn, J. P . Koeln, M. A. Williams, and A. G. Alleyne, “Experimental validation of graph-based hierarchical control for ther- mal management,” Journal of Dynamic Systems, Measurement, and Control, vol. 140, Oct. 2018

    2018

  28. [36]

    Graph-based dynamic modeling of a PAO loop for aircraft thermal management systems,

    V . Vyas, N. Jain, K. McCarthy, and A. Bolander, “Graph-based dynamic modeling of a PAO loop for aircraft thermal management systems,” in AIAA SCITECH 2026 F orum , (Orlando, FL, USA), Jan. 2026

    2026

  29. [37]

    Graph-based dynamic modeling of two-phase heat exchangers in vapor compression systems,

    K. M. Russell, C. T. Aksland, and A. G. Alleyne, “Graph-based dynamic modeling of two-phase heat exchangers in vapor compression systems,” International Journal of Refrigeration , vol. 137, pp. 244– 256, May 2022

    2022

  30. [38]

    Graph-based hierarchical control of thermal-fluid power flow sys- tems,

    H. C. Pangborn, M. A. Williams, J. P . Koeln, and A. G. Alleyne, “Graph-based hierarchical control of thermal-fluid power flow sys- tems,” in 2017 American Control Conference (ACC) , (Seattle, W A, USA), pp. 2099–2105, May 2017

    2017

  31. [39]

    Graph-based modelling and simulation of liquid immersion cooling systems,

    M. Lionello, M. Rampazzo, A. Beghi, D. V aragnolo, and M. V ester- lund, “Graph-based modelling and simulation of liquid immersion cooling systems,” Energy, vol. 207, Sept. 2020

    2020

  32. [40]

    Hierarchical hybrid MPC for management of distributed phase change thermal energy storage,

    H. C. Pangborn, C. E. Laird, and A. G. Alleyne, “Hierarchical hybrid MPC for management of distributed phase change thermal energy storage,” in 2020 American Control Conference (ACC) , (Denver, CO, USA), pp. 4147–4153, July 2020

    2020

  33. [41]

    Lifted graph-based modeling for linear predictive control of nonlinear energy systems,

    S. Park and H. C. Pangborn, “Lifted graph-based modeling for linear predictive control of nonlinear energy systems,” in IEEE Conference on Control Technology and Applications (CCTA) , (Bridgetown, Bar- bados), pp. 926–933, Aug. 2023

    2023

  34. [42]

    Modeling and simulation of vapor compression systems using a graph-based toolbox,

    E. Sisti, M. Rampazzo, and A. Beghi, “Modeling and simulation of vapor compression systems using a graph-based toolbox,” in Proceed- ings of the 15th REHVA HVAC World Congress - CLIMA 2025 , (Milan, Italy), pp. 1008–1017, June 2025

    2025

  35. [43]

    Optimal flow control and single split architecture ex- ploration for fluid-based thermal management,

    S. R. T. Peddada, D. R. Herber, H. C. Pangborn, A. G. Alleyne, and J. T. Allison, “Optimal flow control and single split architecture ex- ploration for fluid-based thermal management,” Journal of Mechanical Design, vol. 141, p. 083401, Apr. 2019

    2019

  36. [44]

    Control-oriented design framework for heat pump- based thermal management systems of electric vehicles,

    C. Ahn and J. Sun, “Control-oriented design framework for heat pump- based thermal management systems of electric vehicles,” Control Engineering Practice , vol. 172, July 2026

    2026

  37. [45]

    Multilevel hierarchical estimation for thermal management systems of electrified vehicles with experimental validation,

    P . J. Tannous and A. G. Alleyne, “Multilevel hierarchical estimation for thermal management systems of electrified vehicles with experimental validation,” Journal of Dynamic Systems, Measurement, and Control , vol. 142, Nov. 2020

    2020

  38. [46]

    Opti- mal sensor placement methods in active high power density electronic systems with experimental validation,

    S. R. T. Peddada, P . J. Tannous, A. G. Alleyne, and J. T. Allison, “Opti- mal sensor placement methods in active high power density electronic systems with experimental validation,” Journal of Mechanical Design , vol. 142, Feb. 2020

    2020

  39. [47]

    Model-based temperature estimation of power electronics systems,

    P . J. Tannous, S. R. Peddada, J. T. Allison, T. Foulkesc, R. C. Pilawa- Podgurskic, and A. G. Alleyne, “Model-based temperature estimation of power electronics systems,” Control Engineering Practice , vol. 85, Apr. 2019

    2019

  40. [48]

    Fault detection and isolation for complex thermal management systems,

    P . J. Tannous and A. G. Alleyne, “Fault detection and isolation for complex thermal management systems,” Journal of Dynamic Systems, Measurement, and Control , vol. 141, June 2019

    2019

  41. [49]

    Dynamic temperature estimation of power electronics systems,

    P . J. Tannous, S. R. T. Peddada, J. T. Allison, T. Foulkes, R. C. Pilawa- Podgurski, and A. G. Alleyne, “Dynamic temperature estimation of power electronics systems,” in 2017 American Control Conference (ACC), (Seattle, W A, USA), May 2017

    2017

  42. [50]

    Opti- mal sensor placement methods for active power electronic systems,

    S. R. T. Peddada, P . J. Tannous, A. G. Alleyne, and J. T. Allison, “Opti- mal sensor placement methods for active power electronic systems,” in ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference , (Cleve- l...

    2017

  43. [51]

    Multi-level hierarchical estimation for thermal management systems of electrified vehicles,

    P . J. Tannous and A. G. Alleyne, “Multi-level hierarchical estimation for thermal management systems of electrified vehicles,” in ASME 2020 Dynamic Systems and Control Conference , (Virtual, Online), Oct. 2020

    2020

  44. [52]

    P . J. Tannous, Estimation and Fault Diagnosis for V ehicle Energy Systems. PhD thesis, University of Illinois at Urbana-Champaign, 2020

    2020

  45. [53]

    Design and validation of a state-dependent riccati equation filter for state of charge estimation in a latent thermal storage device,

    M. Shanks, U. Inyang-Udoh, and N. Jain, “Design and validation of a state-dependent riccati equation filter for state of charge estimation in a latent thermal storage device,” Journal of Dynamic Systems, Measurement, and Control , vol. 145, no. 9, p. 091002, 2023

    2023

  46. [54]

    Hierarchical control of multi-domain power flow in mobile systems: Part I framework development and demonstration,

    J. P . Koeln, M. A. Williams, and A. G. Alleyne, “Hierarchical control of multi-domain power flow in mobile systems: Part I framework development and demonstration,” in Dynamic Systems and Control Conference, (Columbus, OH, USA), Oct. 2015

    2015

  47. [55]

    Robust hierarchical model predictive control of graph-based power flow systems,

    J. P . Koeln and A. G. Alleyne, “Robust hierarchical model predictive control of graph-based power flow systems,” Automatica, vol. 96, pp. 127–133, Oct. 2018

    2018

  48. [56]

    Stability of decentralized model predictive control of graph-based power flow systems via passivity,

    J. P . Koeln and A. G. Alleyne, “Stability of decentralized model predictive control of graph-based power flow systems via passivity,” Automatica, vol. 82, pp. 29–34, Aug. 2017

    2017

  49. [57]

    Passivity and decentralized MPC of switched graph-based power flow systems,

    H. C. Pangborn, J. P . Koeln, and A. G. Alleyne, “Passivity and decentralized MPC of switched graph-based power flow systems,” in 2018 Annual American Control Conference (ACC) , pp. 198–203, June 2018

    2018

  50. [58]

    H. K. Khalil, Nonlinear systems . Upper Saddle River, N.J.: Prentice Hall, 2002

    2002

  51. [59]

    Cooperativity and hierarchi- cal MPC of state-constrained switched power flow systems,

    H. C. Pangborn and A. G. Alleyne, “Cooperativity and hierarchi- cal MPC of state-constrained switched power flow systems,” in 2019 American Control Conference (ACC) , (Philadelphia, PA, USA), pp. 4245–4252, July 2019

    2019

  52. [60]

    H. C. Pangborn, Hierarchical Control for Multi-Domain Coordination of V ehicle Energy Systems with Switched Dynamics . PhD thesis, University of Illinois at Urbana-Champaign, 2019

    2019

  53. [61]

    Monotone Control Systems,

    D. Angeli and E. D. Sontag, “Monotone Control Systems,” IEEE Transactions on Automatic Control , vol. 48, no. 10, pp. 1684–1698, 2003

    2003

  54. [62]

    Learning networked dynamical system models with weak form and graph neural networks,

    Y . Y u, D. Huang, S. Park, and H. C. Pangborn, “Learning networked dynamical system models with weak form and graph neural networks,” arXiv 2407.16779 , 2024

    2024

  55. [63]

    Hardware-in-the-loop validation of advanced fuel thermal management control,

    H. C. Pangborn, J. E. Hey, T. O. Deppen, A. G. Alleyne, and T. S. Fisher, “Hardware-in-the-loop validation of advanced fuel thermal management control,” Journal of Thermophysics and Heat Transfer , vol. 31, pp. 901–909, Oct. 2017

    2017

  56. [64]

    Usage impact on data center electric- ity needs: A system dynamic forecasting model,

    M. Koot and F. Wijnhoven, “Usage impact on data center electric- ity needs: A system dynamic forecasting model,” Applied Energy , vol. 291, June 2021

    2021

  57. [65]

    A study on control co-design for optimizing microgrid sustainability,

    T. R. Jahan, A. S. Ouedraogo, and D. J. Docimo, “A study on control co-design for optimizing microgrid sustainability,” IF AC- PapersOnLine, vol. 58, no. 28, pp. 636–641, 2024

    2024

  58. [66]

    Improved mass conservation of control-oriented models of two-phase thermal systems using neural networks,

    A. M. Gomez, J. Shaikh, and J. P . Koeln, “Improved mass conservation of control-oriented models of two-phase thermal systems using neural networks,” in 2025 American Control Conference (ACC), (Denver, CO, USA), July 2025

    2025

Pith tools

Reviewed June 29, 2026 · model on record in the stance chip above.