Control oriented thermal modeling of lithium ion batteries

Lithium ion batteries have been widely used in consumer electronics and are beginning to move to the forefront of the automotive and power grid industries. Lithium ion batteries are desirable in these applications because they are high energy density and high specific energy cells, while remaining inexpensive and lightweight. Safety is a concern in every consumer application; therefore, in order for lithium ion battery use to continue growing, advances in battery management systems are needed. Thermal management of lithium ion batteries is currently a critical issue. Applications are becoming more dependent on active liquid thermal management systems. The development of precise battery active liquid thermal management systems begins with an accurate temperature model applicable to control design. This work is focused on the development of a dynamic active liquid cooled battery cell thermal model through the coupling of a lumped energy balance and a single particle electrochemical heat generation model. A fluid channel is added to the bottom of the cell and an aluminum heat sink is added to the side of the cell. Results demonstrate that fluid temperature has more effect on the cell temperature than fluid mass flow rate. The dynamic model developed in this work has an order of 135 and; therefore, is not applicable to controller design. Linearization about an equilibrium trajectory and model order reduction via the Global Arnoldi Algorithm (GAA) is applied. Results show good agreement between the first order reduced system and the non-linear system --Abstract, page iv

Reduced-order modeling of power electronics components and systems

This dissertation addresses the seemingly inevitable compromise between modeling fidelity and simulation speed in power electronics. Higher-order effects are considered at the component and system levels. Order-reduction techniques are applied to provide insight into accurate, computationally efficient component-level (via reduced-order physics-based model) and system-level simulations (via multiresolution simulation). Proposed high-order models, verified with hardware measurements, are, in turn, used to verify the accuracy of final reduced-order models for both small- and large-signal excitations. At the component level, dynamic high-fidelity magnetic equivalent circuits are introduced for laminated and solid magnetic cores. Automated linear and nonlinear order-reduction techniques are introduced for linear magnetic systems, saturated systems, systems with relative motion, and multiple-winding systems, to extract the desired essential system dynamics. Finite-element models of magnetic components incorporating relative motion are set forth and then reduced. At the system level, a framework for multiresolution simulation of switching converters is developed. Multiresolution simulation provides an alternative method to analyze power converters by providing an appropriate amount of detail based on the time scale and phenomenon being considered. A detailed full-order converter model is built based upon high-order component models and accurate switching transitions. Efficient order-reduction techniques are used to extract several lower-order models for the desired resolution of the simulation. This simulation framework is extended to higher-order converters, converters with nonlinear elements, and closed-loop systems. The resulting rapid-to-integrate component models and flexible simulation frameworks could form the computational core of future virtual prototyping design and analysis environments for energy processing units