Fuel consumption optimization of Atkinson cycle of a turbocharged SI- engine using genetic algorithm for a series-parallel hybrid electric vehicle

The pursuit of cleaner fuels and enhanced energy efficiency in the automotive industry has intensified interest in Hybrid Electric Vehicles (HEVs), particularly those utilizing Atkinson cycle engines. This paper investigates the optimization and implementation of a series-parallel HEV equipped with an Atkinson cycle gasoline engine, aiming to reduce fuel consumption and emissions. A genetic algorithm (GA)-based multi-variable optimization framework is employed, focusing on intake valve timing, valve lift, and exhaust gas recirculation (EGR) to improve engine performance across various speed-load conditions. A validated ۱D GT-Power simulation model, coupled with MATLAB/Simulink, is used to evaluate the effects of valve timing strategies on mixture formation, combustion characteristics, and emissions. Experimental and numerical CFD with converge software on a ۱.۶L turbocharged Atkinson engine confirm the simulation findings, showing a ۲-۳% improvement in brake specific fuel consumption (BSFC), an ۱۸% reduction in NOx emissions, and up to ۲.۵% decrease in CO۲ emissions. The results highlight the Atkinson cycle's potential for significant efficiency gains in HEVs, particularly when enhanced with variable valve strategies and GA-based optimization. Recommendations for future research include hybridizing Otto and Atkinson cycles and integrating advanced turbocharging systems to address power density limitations.

Keywords:

Atkinson Cycle Engine , Hybrid Electric Vehicle , Genetic Algorithm Optimization , Fuel Economy , Emission Reduction