Electric and hybrid-electric vehicles lean heavily on intricate control algorithms to provide smooth, reliable, and secure operations under any driving conditions. Three distinct supervisory control strategies have been developed, each aiming to improve reliability and vehicle performance of a dual-motor electric vehicle equipped with an all-wheel-drive, fully electric powertrain. These algorithms are adept at dynamically modulating and constraining the torque provided to the wheels, leveraging two autonomous permanent magnet electric drive units. This study utilizes a vehicle model jointly provided by MathWorks and General Motors in partnership with industry sponsors. The these strategies were implemented in the model and enhanced the performance, vehicle range, energy consumption, regenerated energy using specific EDUs provided by sponsors. Adhering to a systematic engineering iterative method, the emphasis was heavily placed on simulation and modeling during the development and validation of these strategies. Simulations ensured robust testing before field implementation, emphasizing software modeling's vital role.

This diploma thesis is focused on development and initial run of a measurement system whose aim is to measure photovoltaic panel efficiency. Experimental photovoltaic panel is installed outside the laboratory. Measurement system consists of the solar radiation measurement module (pyranometer), photovoltaic panel load module (panel power measurement) and panel climate measurement module. Data from measurement module is being captured via control computer for subsequent analysis of panel efficiency and climatic impacts on it.