Modern traffic prediction technologies enable real-time velocity planning of vehicles for less fuel consumption and polluting emissions by reducing the frequency of acceleration/deceleration, idle time, the number of stop, and variation of vehicle speeds. The fuel economy could be further improved if the optimal control strategy parameter could be used in the real-time velocity planning. However, it is difficult to find the optimal value of the control strategy parameter in this real-time velocity planning of vehicles. This paper aims to develop an advising system for control strategy parameters of HEVs in velocity planning. With this aim, the characteristics of the optimal control strategy parameters for various velocity profiles obtained from predictive velocity planning are studied in a parallel HEV. The optimal control strategy parameters with the effect of the average speed, stop frequency, and the traveling distance are investigated. The observed characteristics of the optimal parameters are obtained and can be used in the advising system to improve fuel economy in real-time velocity planning of HEVs.
Designing for Control Strategy by Particle Swarm Optimization in Parallel Hybrid Electric Vehicles for Economical Fuel Consumption
