研究目的
To develop and optimize a solar power conversion system for space applications, focusing on maximizing the specific power (W/kg) through multi-objective optimization and evaluating different DC-DC converter topologies.
研究成果
The non-inverting buck-boost converter within the DMPPT architecture is identified as the optimal topology for space applications due to its flexibility in fault and shading conditions and high specific power. The optimized system achieves 35.56 W/kg, and the prototype demonstrates high efficiency (98.4%), power density (3.54 W/cm3), and reliability under low-temperature cycles, validating the design for space use.
研究不足
The study assumes specific metrics (e.g., conductor density, PV panel mass metric) that may not account for all real-world variations. The optimization is based on models that might not capture all nonlinearities or environmental factors. The prototype uses parameters close to but not exactly the optimized values, and testing is limited to laboratory conditions, not actual space environments. Low-temperature testing revealed MCU failures, indicating potential reliability issues with control components in extreme cold.
1：Experimental Design and Method Selection:
The study uses a multi-objective optimization approach with a Genetic Algorithm to optimize system parameters (battery voltage, PV maximum power, PV MPP voltage, number of panels per string) for a DMPPT architecture. Four DC-DC converter topologies (buck, boost, buck-boost, non-inverting buck-boost) are modeled and compared based on efficiency and loss models.
2：Sample Selection and Data Sources:
The optimization assumes a 500kW system for space use cases, with parameters bounded based on practical ranges (e.g., battery voltage 300-800V, panel power 100-600W). PV panel performance is modeled using a single-diode model in MATLAB Simulink.
3：List of Experimental Equipment and Materials:
Components include eGaN FETs (e.g., GS66508T), planar inductors (e.g., ER32/6/25-3F36 core), capacitors, and a control board with an STM32F108C8T6 microcontroller. A hardware prototype is built for testing.
4：Experimental Procedures and Operational Workflow:
The optimization flow involves defining variables, calculating losses using formulae (e.g., conduction loss, switching loss), and running the GA to find Pareto-optimal designs. The prototype is tested for efficiency using a DC source, electronic load, and power analyzer, and for low-temperature reliability in a chamber with cycles from 0°C to -140°C.
5：Data Analysis Methods:
Efficiency is calculated based on loss models and verified experimentally. System weight and power are computed to derive the W/kg metric. Low-temperature performance is assessed through efficiency measurements and cycle testing.
独家科研数据包，助您复现前沿成果，加速创新突破
获取完整内容
