[Truncated introduction] A major contribution of this thesis is the observation that the N port impedance parameters for the distribution planes can be modelled as simple LC series elements in the frequency range over which the interesting interactions between the loading elements and the planes occur. Loosely speaking, the C represents the inter-plane capacitance and the L is associated with a first order frequency trend of the transfer and input impedances associated with the planes. In the literature, values for L have been obtained for power ground plane structures using curve fitting techniques [38]. In this thesis, formulae are developed for L based on the modal summation expression. As for the impedance case, the approach developed in the thesis that removes the singular behaviour, results in computational efficient expressions. Preliminary results on the simple LC model were presented by the author in [42, 43] The results reported in the thesis extend this work in the light of the new impedance model proposed. The simple LC characterisation enables the development of new low frequency expression for the input and transfer impedance for ports on planes loaded with many decoupling capacitors. The expressions are based on a one off frequency independent decomposition of the inductance matrix associated with the placement of the capacitors. The eigen-mode decomposition eliminates the need for matrix inversion at each frequency point and leads to an efficient computational procedure for calculating the impedance of loaded planes. Furthermore, the interaction between the capacitors and planes is clearly seen in the analytical expressions. This has led to new insights regarding the interaction of multiple capacitors with supply planes in terms of location, resonance mechanisms, pole locations and damping. These insights are beneficial to the understanding and optimisation of printed circuit board power distribution systems.

Indiana University-Purdue University Indianapolis (IUPUI) / Lithium ion (Li-ion) batteries have become integral parts of our lives; they are widely used in applications like handheld consumer products, automotive systems, and power tools among others. To extract maximum output from a Li-ion battery under optimal conditions it is imperative to have access to the state of the battery under every operating condition. Faults occurring in the battery when left unchecked can lead to irreversible, and under extreme conditions, catastrophic damage. In this thesis, an adaptive fault diagnosis technique is developed for Li-ion batteries. For the purpose of fault diagnosis the battery is modeled by using lumped electrical elements under the equivalent circuit paradigm. The model takes into account much of the electro-chemical phenomenon while keeping the computational effort at the minimum. The diagnosis process consists of multiple models representing the various conditions of the battery. A bank of observers is used to estimate the output of each model; the estimated output is compared with the measurement for generating residual signals. These residuals are then used in the multiple model adaptive estimation (MMAE) technique for generating probabilities and for detecting the signature faults. The effectiveness of the fault detection and identification process is also dependent on the model uncertainties caused by the battery modeling process. The diagnosis performance is compared for both the linear and nonlinear battery models. The non-linear battery model better captures the actual system dynamics and results in considerable improvement and hence robust battery fault diagnosis in real time. Furthermore, it is shown that the non-linear battery model enables precise battery condition monitoring in different degrees of over-discharge.

Indiana University-Purdue University Indianapolis (IUPUI) / There is a strong urge for advanced diagnosis method, especially in high power battery packs and high energy density cell design applications, such as electric vehicle (EV) and hybrid electric vehicle segment, due to safety concerns. Accurate and robust diagnosis methods are required in order to optimize battery charge utilization and improve EV range. Battery faults cause significant model parameter variation affecting battery internal states and output. This work is focused on developing diagnosis method to reliably detect various faults inside lithium-ion cell using electrochemical model based observer and fuzzy logic algorithm, which is implementable in real-time. The internal states and outputs from battery plant model were compared against those from the electrochemical model based observer to generate the residuals. These residuals and states were further used in a fuzzy logic based residual evaluation algorithm in order to detect the battery faults. Simulation results show that the proposed methodology is able to detect various fault types including overcharge, over-discharge and aged battery quickly and reliably, thus providing an effective and accurate way of diagnosing li-ion battery faults.