- MS Mechanical Engineering Student – University of Illinois
- Bachelor in Mechanical Engineering – Auburn University (2018)
- B.S. in Physics – Auburn University (2018)
- Research Interests: Control and Design Optimization for Energy Storage
Energy storage is a crucial technology in many industries, such as the electric vehicle, building cooling, and renewable energy industries. In particular, the rapidly growing industry of vehicle electrification relies heavily on electrical energy storage for vehicle propulsion, passenger comfort, and support of vital loads. Many electric vehicle applications experience electrical loads with high peak-to-average power requirements, and these high peak loads can be damaging to traditional energy storage devices such as batteries. Due to the inherent linkage between the electrical and thermal domains, electrical loads often produce significant thermal loads, and thus careful attention must be paid to thermal management systems for vehicle applications. Typically, electrical and thermal energy storage are treated separately; my work focuses on combining electrical and thermal energy storage for these applications.
In the electrical domain, hybrid energy storage offers a solution to the problem of high peak power. Hybrid energy storage (HES) in this context refers to combining dissimilar energy storage components for greater overall capabilities. My work has focused on HES systems which combine energy-dense battery cells and power-dense ultracapacitor cells into a system with increased power and energy density over either of the individual storage elements, demonstrated conceptually in Fig. 1. Previous work demonstrated in simulation that electrical HES is a valuable technology for high peak power applications . However, the intrinsic differences between heterogeneous storage elements prompts the need for advanced control strategies for these hybrid systems. In previous collaborative work, I’ve examined model predictive control and iterative learning control for these systems.
In the thermal domain, high peak power loads can damage many electronic components, including energy storage elements such as batteries. Increasing the size of a traditional cooling system to account for these loads can result in a prohibitively heavy thermal management system. As a weight-saving alternative, thermal energy storage (TES) can be used reduce the peak loads experienced by traditional thermal management systems, as shown in Fig. 2. TES often takes the form of phase change materials (PCMs) that rapidly store thermal energy through the phase change phenomena. Previous work has focused on supplementing coolant loops with TES modules containing PCMs to absorb thermal energy from high peak power loads . The inclusion of TES within a thermal management system presents some challenges, such as strict PCM operating temperature requirements, low PCM thermal conductivity, and limited phase change energy storage capabilities. These challenges prompt the need for advanced control strategies to maximize the benefit of TES. However, the development of model-based control strategies for these systems is hindered by the nonlinear thermal dynamics of PCMs. Previous collaborative work developed a modeling strategy for TES modules to allow inclusion of TES within the graph-based modeling framework used by other members of ARG . To develop a model predictive control strategy, switched linear models were used, which approximated the nonlinear dynamics of the PCM over a range of operating conditions. Using these switched linear models, this work demonstrated a hierarchical model predictive control strategy for TES, which leveraged the long-term predictive capabilities of a slow-updating controller as well as the compensation for model error provided by a quickly-updating controller.
Future work will focus on development of control strategies that optimally manage the coupling between electrical and thermal energy storage systems. Additionally, I plan to apply design optimization methods to these closed-loop energy storage systems to determine sizing and configuration options that maximize power density.
 C.E. Laird and A.G. Alleyne, “A Hybrid Electro-Thermal Energy Storage System for High Ramp Rate Power Applications,” in ASME 2019 Dynamic Systems and Control Conf., 2019.
 H.C. Pangborn, C.E. Laird, and A.G. Alleyne, “Hierarchical Hybrid MPC for Management of Distributed Phase Change Thermal Energy Storage Under Pulsed Loading,” accepted to American Control Conference, 2020.
 S.S. Igram, C.E. Laird, and A.G. Alleyne, “Control of a Hybrid Energy Storage System via a Dual Iterative Learning Controller,” submitted to Conference on Control Technology and Applications, 2020.