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Uncrewed Aircraft Systems (UAS) are becoming prevalent in a wide variety of meteorological investigations. UAS fill an important atmospheric observational gap, namely observations between ground-based sensors and higher altitudes where manned aircraft can safely operate. This paper explores the hardware and software design used for a multi-vehicle atmospheric data collection campaign. The Mobility Virtual Environment (MoVE) is a software framework designed specifically to collect data from multiple vehicles and present a coherent, summary view of a complex scenario. Using both a 2D map and a live updating table, multiple vehicles can be monitored simultaneously to make real-time decisions and quickly assess the mission's effectiveness. MoVE is the software framework used to gather live telemetry inputs before, during, and after flight. MoVE is also the set of tools used to post-process multiple data logs from days of flight experiments into 3D and 4D visualizations over the surrounding terrain. The results are visualizations of otherwise invisible quantities like T, P, RH, and especially vector wind velocities, , captured during flight with drone-based sensors. The open-source software and procedures described here can help the atmospheric research, and broader scientific community, achieve greater understanding when using drone-based sensors.

This paper presents a launching performance comparison between three hybrid electric control strategies under maximum acceleration conditions. The result demonstrates viability of the electric launch control approach and is the first known implementation of launch control in a series hybrid electric vehicle (HEV). Experimental data is presented that shows a 0.49 s reduction in initial vehicle motion (IVM) to 60 mph time in the same vehicle and conditions with only the new launching control method engaged. Performance improvements were achieved by using the engine-generator to pre-charge the HV bus which increased bus voltage throughout the test. This novel method generated an IVM-60 mph time of 9.85 s which is 4% faster than the baseline 10.25 s. Test data with electrical and mechanical power system results are presented. Shaft power and energy estimates from the traction motor are evaluated and compared.