The integration of two Zero-Voltage-Switched Bidirectional Battery Charger Discharger (ZVS-BBCD) units into a space power system is shown. A robust design featuring: four interleaved phases, commandable charge rates, overcurrent protection, overvoltage protection, soft starting, reliable gate drive circuitry, high efficiency, and good dynamics is demonstrated. The ZVS-BBCD is compared to separate hard-switched multimodule charge and discharge units in weight and efficiency and is found to be significantly lighter with comparable losses. The ZVS-BBCD has similar characteristics in discharge and in charge bus regulation modes and allows the use of a common control design for both modes. The two ZVS-BBCDs are integrated into the NASA power system testbed built at the Virginia Power Electronics Center (VPEC) to test their dynamics. The result shows good characteristics including low bus impedance and fast transient response. / Master of Science

Este trabalho apresenta o estudo de um carregador de baterias monofÃsico aplicado a veÃculos elÃtricos. Este carregador à composto por dois estÃgios de processamento de energia e um circuito digital de supervisÃo para controlar a tensÃo sobre o banco de baterias e a corrente de recarga das mesmas. O primeiro estÃgio consiste de um conversor CA-CC bridgeless com caracterÃstica de alto fator de potÃncia, e o segundo estÃgio à representado por um conversor CC-CC fullbridge com isolamento em alta frequÃncia e comutaÃÃo sob tensÃo nula (Zero Voltage Switching â ZVS). Para ambos os conversores, foi realizada uma anÃlise qualitativa e quantitativa, bem como apresentados exemplos de projeto para facilitar o dimensionamento dos componentes. Finalmente, com os componentes escolhidos, foi montado um protÃtipo que permite carregar de uma atà oito baterias de 12 V conectadas em sÃrie. O sistema apresenta como especificaÃÃes: tensÃo de entrada alternada de 220 VÂ15%; tensÃo de saÃda contÃnua de 120 V; corrente de saÃda contÃnua de 20 A; e potÃncia mÃdia de saÃda de 2,4 kW. / This work presents a single-phase battery charger for electric vehicles. This converter is composed by two energy processing stages and a digital circuit to control the voltage across the batteries and their respective charging current. The first stage is a high power factor ACDC bridgeless converter, while the second one consists on a ZVS (Zero Voltage Switching) high frequency isolated DC-DC full-bridge converter. For both converters, the qualitative and quantitative analyses have been performed, as well as design examples have been presented in order to ease the components calculation. Finally, a prototype that allows charging up to eight series-connected 12 V batteries has been built. The system specifications are: AC input voltage of 220 V Â15%; DC output voltage of 120 V; DC output current of 20 A; and average output power of 2.4 kW.

Thesis (M.S.)--Air Force Institute of Technology, 2008. / Title from title page of PDF document (viewed on: Dec 10, 2009).

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.

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.

Indiana University-Purdue University Indianapolis (IUPUI) / This thesis presents a converter topology for photovoltaic panels. This topology minimizes the number of switching devices used, thereby reducing power losses that arise from high frequency switching operations. The control strategy is implemented using a simple micro-controller that implements the proportional plus integral control. All the control loops are closed feedback loops hence minimizing error instantaneously and adjusting efficiently to system variations. The energy management between three components, namely, the photovoltaic panel, a battery and a DC link for a microgrid, is shown distributed over three modes. These modes are dependent on the irradiance from the sunlight. All three modes are simulated. The maximum power point tracking of the system plays a crucial role in this configuration, as it is one of the main challenges tackled by the control system. Various methods of MPPT are discussed, and the Perturb and Observe method is employed and is described in detail. Experimental results are shown for the maximum power point tracking of this system with a scaled down version of the panel's actual capability.