Second-Order Effects in Modeling Resonant DC-DC Converters

The rising demand for energy and the depletion of fossil fuels has led to the rapid adoption of renewable energy sources. However, their variability poses challenges. Battery-based storage systems offer a solution, storing excess energy for peak demand or low generation periods. High-gain converters are key in efficiently integrating these systems with the grid by boosting battery voltage. Adding cell-integrated power electronics enhances reliability by providing localized control, reducing the impact of individual cell failures. The study investigates the efficiency of cell-level high-gain DC-DC two-stage converters for integrating renewable energy into the grid. To optimize performance across diverse DC link voltages, a comparison of different first-stage converters is performed based on factors such as component selection and switch losses. Following careful calculations and selection, the chosen converter is paired with an inverter for seamless grid integration. Operating at a power level of 200W, the converters transform low battery cell voltage from 2.5V to 40V into a grid-compatible 360V output. Results demonstrate the selected converter's superior efficiency and voltage regulation, displaying its suitability for grid integration applications. This research underscores the importance of such converters in facilitating reliable renewable energy integration, offering a pathway toward sustainable energy utilization. In the subsequent phase, the investigation delves deeper into assessing the performance of the LLC converter, particularly focusing on how it reacts to the secondary effects of the converter. This investigation gives special attention to a significant factor known as Intra-Winding capacitance, which refers to the unintended capacitance existing within the windings of the converter's transformer in close proximity. Furthermore, this analysis employs the General Harmonic Approximation (GHA) and compared against traditional Fundamental Harmonic Approximation (FHA).