Parameters Design And Performance Optimization Of The Range-Extended Electric Vehicle

Under the policy of developing new energy vehicles in China, the range-extended electric vehicle (REEV), which makes up for the shortage of driving range of the pure electric vehicle and has the advantage of zero emissions, has become an ideal hybrid vehicle scheme.In this dissertation, a range-extended electric vehicle was taken as the research object, firstly a feasible configuration of the driving system was designed and a simulation software platform was developed for this vehicle, then further study was done to optimize the dynamic performance and fuel economy. The main work and conclusions were summarized as following:According to the design requirement and the structure of REEV, the components of the driving system were selected and matched to get an initial configuration. Then dynamic programming theory was adopted to find the optimal fuel consumption in NEDC driving cycle, which could be the evaluation criterion of other control strategies. On the basis of the work modes of REEV, a control strategy based on the regular logical gateway was designed and either the brake strategy based on the ECE braking regulations to maximize the energy recycling during braking.C# and MATLAB mixed programming was applied to develop the simulation software platform for hybrid electric vehicles. A model of the vehicle and control strategy was built on MATLAB/Simulink and the graphical user interface was designed using C# on WPF. The software provides user with looking up and modifying the parameters of main parts of the vehicle, the simulation can be done to evaluate the dynamic performance and fuel economy, and the result data will be processed. The simulation result showed that the initial configuration met the design requirement and the control strategy worked well in braking energy recycling.To further optimize the vehicle performance, firstly on the basis of orthogonal experimental design, experiments of 6 factors,3 levels and 6 goals were conducted to find out the remarkable 4 factors. Then the multi-objective genetic algorithm was applied to optimize the fuel economy and emissions under the dynamic constraints. A combined weighting method was applied to pick out the optimal solution within the Pareto solutions. The simulation result showed that the optimal solution reduced the fuel consumption and emissions by a large margin.