Abstract

Hybrid electric vehicle (HEV) is one of the main types that will be developed in the next twenty years because of its low fuel consumption, low emissions and comparatively easiness for being merchandized. However, as it has at least two sets of propulsion systems, the hardware structure and the energy management system is complex. The optimal match of various components and the optimal combination of a large number of control parameters are therefore required. The thesis proposes to solve such kinds of HEV propulsion system optimization problems using the evolutionary computation techniques. Single objective optimization and multi-objective optimization will be both explored based on the HEV propulsion system simulation software ADVISOR by treating the typical performance parameters of the components and the major control strategy parameters as optimization variables together. Genetic algorithm (GA) is employed for the single objective optimization. This algorithm has conquered the deficiencies of the traditional optimization methods that require calculating the derivative of the objective function and that are easily trapped in the local optimal areas. But the shortcoming of GA is its poor search efficiency. The thesis suggests integrating the gradient-based algorithm SQP into GA, and thus combining the global search ability of GA and fast convergence ability of SQP, which results in two hybrid algorithms GA-SQP-I and GA-SQP-II. The result of a HEV study case demonstrates that these algorithms improve the fuel economy of the original HEV dramatically. In this part, another global optimization method, DIRECT is also tested. It is a deterministic method, not as GA, therefore possessing relatively stronger robustness. Though the precision of the optimum it finds usually is not high, it has fast convergence rate and is also an appropriate choice for many engineering problems. The topic of the latter half of the thesis is the multi-objective optimization. A multi-objective evolutionary algorithm (MOEA), NSGA-II is employed to optimize the fuel consumption and different emissions (CO，NOx， CO, etc) of a parallel HEV simultaneously. The analysis of the output Pareto-optimal solutions shows that the MOEA not only improves the performances of the original vehicle described above but also provides the users with a large flexibility for the design and control of the HEV, therefore enabling the users choose different components and control strategies according to different requirements. How to accelerate the convergence rate of the MOEAs has been explored based on the idea of combining MOEAs with gradient-based algorithms through epsilon-Constraint method. A hybrid algorithm NSGA-SQP is then developed. Nevertheless, the numeric simulation study shows that for some test problems the hybrid algorithm converges very fast, while for some problems especially for those which have numerous local optimal traps it does not behave better than the original NSGA-II, which calls for further studies.