The global Positioning System (GPS) provides location and time information as long as there are unobstructed lines of sight to four or more GPS satellites. However, when this is not the case the signal may be inaccurate or sometimes even completely blocked. In these situations the Inertial Navigation System (INS) is an appropriate choice for positioning.  An INS has already been proposed in a previous thesis by Erik Andersson and the objective of this thesis is to fuse the GPS with the INS in a proper way. A part of this project is to decide the reliability of the GPS.Three methods for GPS reliability detection have been proposed. One method based on the statistical properties of each of the separate systems, and two methods based on the statistical properties of the residuals between the GPS and INS. Two methods for GPS/INS integration have been proposed. One method based on a bank of parallel running Kalman filters and one method based on an adaptive observer.The method based on Kalman filter diverged. By adding a state that was suppose to represent the bias of the noise an attempt was to fix this problem made. The filter still diverged and was not examined any further. Among the other two algorithms did the one that uses both magnetometer and gyroscope presents a better result than the one that uses only gyroscope. However, the result differences between the two algorithms were not big and the result may change if a better INS is used.

Indiana University-Purdue University Indianapolis (IUPUI) / A gradient free function optimization technique, namely particle swarm optimization (PSO) algorithm, is utilized in parameter identification of the electrochemical model of a Lithium-Ion battery having a LiCoO2 chemistry. Battery electrochemical model parameters are subject to change under severe or abusive operating conditions resulting in, for example, Navy over-discharged battery, 24-hr over-discharged battery, and over-charged battery. It is important for a battery management system to have these parameters changes fully captured in a bank of battery models that can be used to monitor battery conditions in real time. In this work, PSO methodology has been used to identify four electrochemical model parameters that exhibit significant variations under severe operating conditions. The identified battery models were validated by comparing the model output voltage with the experimental output voltage for the stated operating conditions. These identified conditions of the battery were then used to monitor condition of the battery that can aid the battery management system (BMS) in improving overall performance. An adaptive estimation technique, namely multiple model adaptive estimation (MMAE) method, was implemented for this purpose. In this estimation algorithm, all the identified models were simulated for a battery current input profile extracted from the hybrid pulse power characterization (HPPC) cycle simulation of a hybrid electric vehicle (HEV). A partial differential algebraic equation (PDAE) observer was utilized to obtain the estimated voltage, which was used to generate the residuals. Analysis of these residuals through MMAE provided the probability of matching the current battery operating condition to that of one of the identified models. Simulation results show that the proposed model based method offered an accurate and effective fault diagnosis of the battery conditions. This type of fault diagnosis, which is based on the models capturing true physics of the battery electrochemistry, can lead to a more accurate and robust battery fault diagnosis and help BMS take appropriate steps to prevent battery operation in any of the stated severe or abusive conditions.