Indiana University-Purdue University Indianapolis (IUPUI) / With the rapid growth of technology, scientists are trying to find ways to make the world a more efficient and eco-friendly place. The research and development of electric vehicles suddenly boomed since natural resource are becoming very scarce. The significance of an electric vehicle goes beyond using free energy, it is environ- mental friendly. The objective of this thesis is to understand what Vehicle to Grid Communication (V2G) for an electric vehicle is, and to implement a model of this highly efficient system into a Hybrid Petri Net. This thesis proposes a Hybrid Petri net modeling of Vehicle to Grid (V2G) Communication topology. Initially, discrete, continuous, and hybrid Petri net's are defined, familiarized, and exemplified. Secondly, the Vehicle and Grid side of the V2G communication system is introduced in detail. The modeling of individual Petri nets, as well as their combination is discussed thoroughly. Thirdly, in order to prove these systems, simulation and programming is used to validate the theoretical studies. A Matlab embedded simulation program known as SimHPN is used to simulate specific scenario's in the system, which uses Depth-first Search (DFS) Algorithm. In addition to SimHPN simulation program, Matlab program is made to output four levels of the reachability tree as well as specifying duplicate and terminate nodes. This code incorporates a technique known as Breadth-first Search (BFS) Algorithm.

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.