CSMA

Hybrid electric vehicle control strategy is a management approach for the generating, using and saving energy. Therefore, the optimal control strategy is the sticking point to effectively manage the hybrid electric vehicles (HEV). In order to achieve the optimal control strategy, we use a robust evolutionary computation method called “Memetic Algorithm (MA)” to optimize the control parameters in parallel hybrid electric vehicles. “Local search” mechanism implemented in MA greatly enhances search capabilities. In the implementation of the method, the fitness function combines with ADVISOR (Advanced Vehicle Simulator) and is set up according to an electric assist control strategy (EACS) to minimize the fuel consumption (FC) and emissions (HC, CO, and NOx) of the vehicle engine. At the same time, driving performance requirements are also considered in the method. Four different driving cycles, including NEDC, FTP, ECE-EUDC and UDDS is carried out by the proposed method to find out respectively optimal control parameters. The results show that the proposed method effectively helps to reduce fuel consumption and emissions, as well as guarantee vehicle performance.

The results used in this study are freely accessible and provided at https://sites.google.com/site/yhcheng1981/csma for users of interests.