Donald Docimo

  • Curriculum Vitae
  • LinkedIn
  • ddocimo@illinois.edu
  • Ph.D. – The Pennsylvania State University, 2017
  • M.S. – The Pennsylvania State University, 2015
  • B.S. – The College of New Jersey, 2012
  • Research Interests: Hierarchical control, electro-thermal systems, EVs, building energy systems, photovoltaics

 

Donald Docimo, Ph.D. conducts research in the area of controls as applied to energy systems. His focus is use of tools from control, model reduction, optimization, and estimation theory to address major challenges in applications that span electrified vehicles, aircraft, and advanced thermal management devices. He is an experienced educator, having instructed junior- and senior-level controls courses, developed online multimedia courses, and mentored students.

 

Current Research

Research supported by:

  • The National Science Foundation Engineering Research Center for Power Optimization of Electro-Thermal Systems (POETS)
  • The Center for Integrated Thermal Management of Aerospace Vehicles (CITMAV)

Ongoing work includes extensions of hierarchical control strategies for energy management, topology optimization, and testbed development to validate electro-thermal device performance. Military electrified aircraft and commercial electric vehicles (EVs) contain thermal and electrical systems of varying timescales, creating computational and accuracy challenges for centralized and decentralized controllers. Hierarchical control has been recognized as a powerful tool to improve the management of these electro-thermal interactions. Use of a hierarchical control framework permits top-down management of the components of the EV while reducing computation time as compared to algorithms such as centralized control. A graphical model of the vehicle allows for rule-based system decomposition drawn directly from graph theory. Recent extensions add detailed electrical component models to a graph-based modeling framework already consisting of thermal and hydraulic models. Figure 1 shows how a graphical model lends itself to system decomposition, yielding a multilevel hierarchy. Model predictive control (MPC) is applied to each level’s subsystems to yield a more tightly controlled vehicle that provides increased available power while keeping components within thermal constraints.

Figure 1. Left: Graph-based model of a vehicle. Right: Hierarchy with three levels.

 

Previous Research

Battery systems. For the purpose of improving EV reliability and cost, it is ideal to remove heterogeneity between battery cells in order to extend pack lifespan. Fig. 2 presents a battery pack architecture that is able to manipulate balancing currents between cells to accomplish this. The growth or decay of heterogeneity can be determined by applying model reduction to a battery pack model and analyzing this model with control theory tools. By doing this, it is found that traditional voltage balancing does not remove capacity heterogeneity in a reasonable time period. A developed novel multivariable feedback controller can balance capacities within a year and increase pack lifespan by up to 9.2%.

Figure 2. Potential battery pack configuration for N cell modules in series with balancing hardware.

 

Building energy systems. It is noted in the literature that populations of thermostatically controlled loads (TCLs) with heterogeneous distributions of parameters (e.g., thermal capacitance, thermal resistance) are better suited for demand response. By developing a reduced-order model of the power demand for a heterogeneous population of TCLs, it is determined that only certain types of parameter heterogeneities create an apparent damping effect within the power demand of the population. The model insights determined through analysis are poised to improve demand response control algorithms and system design.

Renewable energy generation. The optimal input voltage of a photovoltaic (PV) module is directly correlated to the incident irradiation level and temperature of the PV module. In order to mitigate the impact of fluctuating irradiation levels on maximum power point tracking (MPPT), a temperature and irradiation state estimator was developed, and a subsequent extension developed a controller to utilize the state estimates. Figure 3 shows that this dynamic method of MPPT leads to increase in daily energy production by up to 8.5%.

Figure 3. Top: Real (black) and estimated (red dashed) irradiation levels. Bottom: Difference between maximum possible and generated power using the novel control algorithm.

 

Publications (Journal)

  1. D.J. Docimo and H.K. Fathy, “Analysis and Control of Charge and Temperature Imbalance Within a Lithium-Ion Battery Pack,” IEEE Transactions on Control System Technology, 2018 (accepted).
  2. D.J. Docimo and H.K. Fathy, “Demand Response Using Heterogeneous Thermostatically Controlled Loads: Characterization of Aggregate Power Dynamics,” J. of Dynamic Systems, Measurement, and Control, vol. 139, no. 10, pp. 101009, 2017.
  3. D.J. Docimo, M. Ghanaatpishe, and A. Mamun, “Extended Kalman Filtering to Estimate Temperature and Irradiation for Maximum Power Point Tracking of a Photovoltaic Module,” Energy, vol. 120, pp. 47-57, 2017.
  4. D.J. Docimo and H.K. Fathy, “Multivariable State Feedback Control as a Foundation for Lithium-Ion Battery Pack Charge and Capacity Balancing,” J. of the Electrochemical Society, vol. 164, no. 2, pp. A61-A70, 2017.
  5. M.J. Rothenberger, D.J. Docimo, M. Ghanaatpishe, and H.K. Fathy, “Genetic Optimization and Experimental Validation of a Test Cycle that Maximizes Parameter Identifiability for a Li-Ion Equivalent-Circuit Battery Model,” J. of Energy Storage, vol. 4, pp. 156-166, 2015.

Publications (Magazine)

  1. D.J. Docimo, M. Ghanaatpishe, M.J. Rothenberger, C.D. Rahn, and H.K. Fathy, “The Lithium-Ion Battery Modeling Challenge: A Dynamic Systems and Control Perspective,” Dynamic Systems and Control Mag., vol. 136, no. 6, pp. 7-14, June, 2014.

Publications (Conference)

  1. P.J. Tannous, D.J. Docimo, H.C. Pangborn, and A.G. Alleyne, “Hierarchical Estimation for Complex Multi-Domain Dynamical Systems,” in 2019 American Control Conf., 2019 (submitted).
  2. D.J. Docimo, H.C. Pangborn, and A.G. Alleyne, “Hierarchical Control for Electro-Thermal Power Management of an Electric Vehicle Powertrain,” in ASME 2018 Dynamic Systems and Control Conf., 2018.
  3. D.J. Docimo and A.G. Alleyne, “Electro-Thermal Graph-Based Modeling for Hierarchical Control with Application to an Electric Vehicle,” in IEEE Conference on Control Technology and Applications, 2018.
  4. D.J. Docimo and H.K. Fathy, “Using a Linear Quadratic Regulator to Attenuate Cell-to-Cell Heterogeneity within a Lithium-Ion Battery Pack,” in IEEE Conference on Control Technology and Applications, 2018.
  5. D.J. Docimo and H.K. Fathy, “Characterization of Damping and Beating Effects Within the Aggregate Power Demand of Heterogeneous Thermostatically Controlled Loads,” in ASME 2015 Dynamic Systems and Control Conf., 2015.
  6. D.J. Docimo, M. Ghanaatpishe, and A. Mamun, “Feedback-Based Temperature and Irradiation Estimation for Photovoltaic Modules,” in ASME 2015 Dynamic Systems and Control Conf., 2015.
  7. D. Docimo, M. Ghanaatpishe, and H. K. Fathy, “Development and Experimental Parameterization of a Physics-Based Second-Order Lithium-Ion Battery Model,” in ASME 2014 Dynamic Systems and Control Conf., 2014.