Numerical Analysis of Optimal Hybridization in Parallel Hybrid Electric Powertrains for Tracked Vehicles

Abstract

Tracked vehicles are integral for maneuvering diverse terrains, with hybrid propulsion systems offering potential benefits in terms of fuel efficiency and performance. However, research in hybrid electric tracked vehicles remains limited, thus necessitating a comprehensive analysis to maximize their advantages. This study presents a numerical analysis focusing on optimizing hybridization in speed-coupled parallel hybrid electric powertrains for tracked vehicles. A dynamic programming algorithm and custom drive cycle are utilized to determine optimal hybridization factors and assess parameter sensitivities. The study reveals that a hybridization factor of 0.48 is optimal for speed-coupled parallel configurations. Furthermore, the sensitivity analysis underscores the substantial impact of factors such as the engine displacement and bore-to-stroke ratio on the fuel economy, with a 10% change in these parameters potentially influencing the fuel economy by up to 2%, thus emphasizing the importance of thorough consideration during powertrain sizing. Parallel hybrid configurations exhibit considerable potential for tracked vehicles, thus highlighting the viability of choosing them over series configurations.