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.

Keywords: Optimal and adaptive control, PDE control, energy storage, smart grid systems, and batteries.

Advanced Battery Management Systems

Today's electric vehicle batteries are expensive and prone to unexpected failure. Batteries are complex systems, and developing techniques to cost-effectively monitor and manage important performance measures while predicting battery cell degradation and failure remains a key technological challenge. There is a critical need for breakthrough technologies that can be practically deployed for superior management of both electric vehicle batteries and renewable energy storage systems.

We are developing battery monitoring and control software to improve the capacity, safety, and charge rate of electric vehicle batteries. Conventional methods for preventing premature aging and failures in electric vehicle batteries involve expensive and heavy overdesign of the battery and tend to result in inefficient use of available battery capacity. The objective is to increase usable capacity and enhance charging rates by improving the ability to estimate battery health in real-time, to predict and manage the impact of charge and discharge cycles on battery health, and to minimize battery degradation.
Partners: Robert Bosch Research and Technology Center | Palo Alto, CA
Prof. Miroslav Krstic | UC San Diego
Funding: DOE ARPA-E AMPED Program
Publications: Adaptive PDE Observer for Battery SOC/SOH Estimation via an Electrochemical Model
S. J. Moura, N. A. Chaturvedi, and M. Krstic
ASME Journal of Dynamic Systems, Measurement, and Control, to appear
Constraint Management in Li-ion Batteries: A Modified Reference Governor Approach
S. J. Moura, N. A. Chauturvedi, and M. Krstic,
2013 American Control Conference; Washington, DC
Invited paper
Monitoring Flexible Loads for Demand Response

This research investigates a new paradigm for monitoring demand response (DR) enabled loads. We examine the application of partial differential equation (PDE) models for estimation and control. Specifically, we model the dynamics of thermostatically controlled load (TCL) populations, instead of modeling each TCL individually. This aggregate modeling approach produces elegant algorithms that are simple to implement, high performance, and robust. Ultimately, these algorithms advance current approaches by requiring minimal sensing infrastructure, providing unprecedented monitoring detail, and enabling predictive control for integrating variable renewable power. This project generates experimental data to test the feasibility of these algorithms. Moreover, it enhances sustainability efforts on UC campuses.
Partners: Prof. Miroslav Krstic | UC San Diego
Prof. Jan Bendsten | Aalborg University, Denmark
Funding: California Energy Commission EISG Program
Publications: Modeling Heterogeneous Populations of Thermostatically Controlled Loads using Diffusion-Advection PDEs
S. J. Moura, V. Ruiz, and J. Bendsten,
2013 ASME Dynamic Systems and Control Conference; Stanford, CA
Invited paper
Observer Design for Boundary Coupled PDEs: Application to Thermostatically Controlled Loads in Smart Grids
S. J. Moura, J. Bendsten, and V. Ruiz
2013 IEEE Conference on Decision and Control; Florence, Italy
Invited paper