Across the world, communities are rapidly urbanizing. These growing cities are characterized by a tightly woven infrastructure where mobility and energy networks are diversifying and merging. For example, electrified transportation creates unique mobility options and constraints while simultaneously imposing new energy demands and storage opportunities. Maximizing the efficiency of such interconnected systems requires strong fundamental science for modeling, estimation, and control, contextualized within energy and mobility applications.
Typical residential HVAC systems employ mechanical or hard-coded deadband control behaviors that are unresponsive to changing energy costs and weather conditions. In this paper, we investigate the potential of electric baseboard heaters to maintain a comfortable temperature while optimizing electricity consumption given weather forecasts and price data. We first propose a distributed system architecture that utilizes mobile application platforms. We then develop, assemble, and deploy a sensor network and Internet server to collect real-time temperature data from an apartment. With these sensor streams, we identify a thermal model of the apartment. Finally, we propose a model predictive control algorithm and perform a software-in-the-loop simulation of the cloud-based system to demonstrate the economic advantage.
Lithium-ion (Li-ion) battery faults or failure mechanisms are potentially hazardous to battery health, safety and performance. Thermal fault mechanisms represent a critical subset of such failures. To ensure safety and reliability, battery management systems must have the capability of diagnosing these thermal failures. In line with this requirement, we present a Partial Differential Equation (PDE) model-based scheme for diagnosing thermal faults in Li-ion batteries. For this study, we adopt a distributed parameter one-dimensional thermal model for cylindrical battery cells. The diagnostic scheme objective is to detect and estimate the size of the thermal fault. The scheme consists of two PDE observers arranged in cascade with measured surface temperature feedback. The first observer, denoted as Robust Observer, estimates the distributed temperature inside the cell under nominal (healthy) and faulty conditions. The second observer, denoted as Diagnostic Observer, receives this estimated temperature distribution, and in turn outputs a residual signal that provides the fault information. Furthermore, the residual signal is evaluated against non-zero thresholds to achieve robustness against modeling and measurement uncertainties. Lyapunov stability theory has been utilized to verify the analytical convergence of the observers under heathy and faulty conditions. Simulation studies are presented to illustrate the effectiveness of the proposed scheme.
This paper studies a nonlinear predictive energy management strategy for a residential building with a rooftop photovoltaic (PV) system and second-life lithium-ion battery energy storage. A key novelty of this manuscript is closing the gap between building energy management formulations, advanced load forecasting techniques, and nonlinear battery/PV models. Additionally, we focus on the fundamental trade-off between lithium-ion battery aging and economic performance in energy management. The energy management problem is formulated as a model predictive controller (MPC). Simulation results demonstrate that the proposed control scheme achieves 96%-98% of the optimal performance given perfect forecasts over a long-term horizon. Moreover, the rate of battery capacity loss can be reduced by 25% with negligible losses in economic performance, through an appropriate cost function formulation.