研究目的
To design a powertrain controller for a solar photovoltaic-battery powered hybrid electric vehicle that ensures better battery management, load regulation, and maximum power extraction from photovoltaic panels.
研究成果
The proposed powertrain controller effectively manages battery health and maximizes power extraction from solar PV panels in a hybrid electric vehicle. It successfully switches between lower level controllers based on operating conditions without violating battery constraints, as validated by simulation and experimental results. The approach ensures optimal performance and can be adapted to other HEVs with customized battery management constraints.
研究不足
The simulation and experiments are conducted under constant irradiation and temperature conditions (1000 W/m2 and 25°C), which may not represent real-world varying environmental conditions. The system uses specific components (e.g., lead-acid batteries, BLDC motor) that might not be optimal for all HEV applications. The control algorithm's effectiveness in dynamic or partial shading scenarios is not fully explored.
1：Experimental Design and Method Selection:
The study involves designing a hierarchical control system with lower level controllers for MPPT, battery charging (using PI controllers), and load regulation, and a high-level algorithm to switch between them based on operating conditions. Mathematical modeling of the system is performed using state space and transfer function models.
2：Sample Selection and Data Sources:
A prototype system is built with solar PV modules, lead-acid batteries, buck converters, and a BLDC motor. Data on parameters like voltage, current, and SOC are collected through sensors.
3：List of Experimental Equipment and Materials:
Solar PV modules (specified in tables), lead-acid batteries, buck converters, BLDC motor, loading arrangement with PMAC generator and rheostat, voltage and current sensors (Hall effect transducers), and a dedicated real-time controller.
4：Experimental Procedures and Operational Workflow:
The load is varied intentionally to trigger different operating modes (e.g., MPPT, CCC, CVC, load regulation). Parameters are monitored continuously, and the high-level algorithm switches controllers based on predefined constraints (e.g., SOC levels, current thresholds).
5：Data Analysis Methods:
Simulation and experimental data are compared to validate the control strategy. Parameters like Vpv, ipv, Vbatt, ibatt, SOC, and iload are analyzed graphically to assess performance.
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