Development of a novel dual-loop optimization method for the engine electric turbocompound system based on particle swarm algorithm

Abstract

Electric turbocompounding (ETC) plays a key role in fuel saving and emission reduction of internal combustion engines. In this paper, a novel dual-loop optimization method is proposed to rapidly obtain highest turbine efficiency and lowest engine BSFC. Firstly, turbine model and the engine cycle model are established and coupled in MATLAB. The predicted engine torque and BSFC are 4.48 % and 5.05 % deviated from the experimental data. The deviations of turbine swallowing capacity and efficiency are within 1 % and 2 %, respectively. Then, particle swarm optimization (PSO) is used to seek for the optimal ETC expansion/pressure ratio in the outer loop and the optimal turbine geometric parameters in the inner loop. It is shown that 3–4 particles are enough to obtain a good solution for the 2-D ETC system optimization and at least 25 particles are required for the 6-D turbine optimization. At last, case studies are carried out to analyze the impacts of ETC mechanical efficiency and back pressure. It is shown that 5 % higher of ETC mechanical efficiency results in 30 % increase in electric power and 10 kPa higher back pressure leads to 18.1 % lower in electric power. The optimized turbine can obtain efficiency higher than 78 % in all cases.