ADAPTIVE ESTIMATION AND CONTROL OF MODELS FOR BATTERY ELECTROCHEMISTRY
Séance du jeudi 16 mai 2013, Salle L 312, 14h.
Scott MOURA, Cymer Center for Control Systems and Dynamics, University of California, San Diego, USA
Energy storage represents one of society's grand challenges for the 21st century. As renewable energy generation becomes more prominent, new solutions for efficiently storing and routing energy must be found. Today's batteries are expensive, oversized, and conservatively operated. Advanced estimation and control can unlock their full potential. That is, real-time state information and optimization enables operation near the physical limits without compromising longevity. This feature is a crucial step towards catalyzing the adoption of energy storage in electrified transportation and smart grids.
In this talk, we focus on the state-of-charge (SOC) and state-of-health (SOH) estimation problem. We cast this problem as a simultaneous state (SOC) and parameter (SOH) estimation design. Unlike most approaches, our design utilizes electrochemical models, thus enabling us to directly estimate the evolution of relevant electrochemical phenomena. The algorithm integrates several recently developed concepts in PDE control theory and adaptive estimation. We then explore how these estimates can be exploited to ensure safe operation, while increasing performance. Time permitting, I will also preview recent results on modeling and estimation problems for demand response in smart grids. Finally, I conclude with an overview of open questions and future research plans in energy systems and control.
Scott MOURA, Cymer Center for Control Systems and Dynamics, University of California, San Diego, USA
Energy storage represents one of society's grand challenges for the 21st century. As renewable energy generation becomes more prominent, new solutions for efficiently storing and routing energy must be found. Today's batteries are expensive, oversized, and conservatively operated. Advanced estimation and control can unlock their full potential. That is, real-time state information and optimization enables operation near the physical limits without compromising longevity. This feature is a crucial step towards catalyzing the adoption of energy storage in electrified transportation and smart grids.
In this talk, we focus on the state-of-charge (SOC) and state-of-health (SOH) estimation problem. We cast this problem as a simultaneous state (SOC) and parameter (SOH) estimation design. Unlike most approaches, our design utilizes electrochemical models, thus enabling us to directly estimate the evolution of relevant electrochemical phenomena. The algorithm integrates several recently developed concepts in PDE control theory and adaptive estimation. We then explore how these estimates can be exploited to ensure safe operation, while increasing performance. Time permitting, I will also preview recent results on modeling and estimation problems for demand response in smart grids. Finally, I conclude with an overview of open questions and future research plans in energy systems and control.