This dissertation combines electrochemistry-based battery models and optimal control theory to study power management in energy storage/conversion systems. This topic is motivated by the need to enhance the performance and longevity of battery electric systems. In particular, the rapid progress in battery material science and energy conversion presents a highly relevant opportunity to bridge the knowledge gap between electrochemistry and control. Ultimately, this dissertation elucidates the key physical phenomena in battery-powered systems which enable opportunities to improve battery performance and health through control. We address this topic in three phases.

First we provide an overview of battery fundamentals and relevant degradation mechanisms. Then we develop mathematical models for the electrochemical battery phenomena, plug-in hybrid vehicle drivetrain dynamics, and stochastic drive cycle dynamics. A battery-in-the-loop experimental test system is fabricated to identify the electrochemical battery model.

Second, we investigate the battery-health conscious power management problem for plug-in hybrid electric vehicles (PHEVs). This effort designs controllers to split engine and battery power to minimize both fuel/electricity consumption costs and battery state-of-health degradation. Mathematically, this problem is formulated as a stochastic dynamic program. The degradation phenomena considered include anode-side solid electrolyte interphase film growth and the "Ah-processed" model. This work is the first to utilize fundamental electrochemical battery models to optimize power management.

The final phase proposes a novel battery pack management strategy which investigates the potential health advantages of allowing unequal yet controlled charge levels across batteries connected in parallel. Mathematically, this problem is formulated as a deterministic dynamic program. The optimal solutions reveal that capacity fade can be mitigated through controlled charge unequalization if concavity properties exist in the health degradation dynamics. The sensitivity of these results are analyzed across various degradation models derived from existing literature and experimental data.

In total, this dissertation utilizes physics-based battery models to optimize power management in energy storage systems. The unique overarching contribution is a systematic optimal control approach for elucidating the physical electrochemical properties one can exploit through control to enhance battery performance and life. The second and third phases described above demonstrate how this approach can be very useful for PHEV and battery pack management applications.