Multi-objective optimization of a parallel hybrid electric drive train

Hybrid electric vehicles are saving raw oil but to achieve this other resources as e.g. copper and lithium are needed. Therefore the present paper deals with the optimization of a parallel hybrid electric drive train on both minimal fuel consumption and minimal use of copper for the electrical machine and lithium within the electrical energy storage. Since copper and lithium are decisive factors during the development process and fuel consumption depends on the user the Pareto front will be analyzed looking at different driving cycles. The chosen algorithm is a hybrid multi-objective optimization method of Simulated Annealing, a Genetic Algorithm and Tournament Selection. The achieved results of the Pareto optimized HEV drive train are presented and the interdependency of those goals is analyzed.