Multiobjective optimisation of a series hybrid electric vehicle using Direct algorithm

Abstract

With the development of the economy, the city transportation becomes heavier. The urban car is characterized by often stop. Furthermore, fuel consumption is causing a serious problem of pollution in the urban environment. Hybrid electric vehicles (HEV) are considered as a good solution compared to conventional internal combustion engine (ICE) vehicles. In order to solve those problems, components parameters of a series hybrid electric vehicle (SHEV) are selected and matched with the simulation tool ADvanced VehIcle SimulatOR (ADVISOR), software based on Matlab_simulink. In order to optimize the SHEV, the fitness function combines with the ADVISOR and is set up according to a thermostat control strategy (TCS) to minimise the simultaneous the fuel consumption (FC) and the emissions (HC, CO, and NOx) of the engine. As well as, the driving performance requirements are also examined. An urban driving cycles is used to test the vehicle and to fix their optimal control parameters: the urban dynamometer driving schedule (UDDS). Finally, the results show that those steps help to reduce fuel consumption and emissions and guarantee vehicle performance. Compared to the traditional vehicle, SHEV greatly improves fuel economy and reduce the toxic emissions.