Energy management in order to reduce fuel consumption for parallel Plug-in Hybrid Electric vehicles based on fuzzy predictive control

Nowadays, Hybrid Electric Vehicles (HEVs) are introduced in order to reduce fuel consumption and emissions. The issue that is very important in HEVs, is how to split power between main components of powertrain. Best energy management can be obtained when all future conditions are available. With the advancement of the intelligent systems, access to the road conditions, traffic and other online information has been provided up to the limited prediction horizon. In this paper, a combination of predictive control and Dynamic Programming methods have been used for obtaining online sub-optimal trajectory. Change in the state of traffic in the path has great effect on reduction of fuel consumption. Therefore, According to the state of traffic, a fuzzy logic system is proposed for the online estimating of the vehicle speed. Unlike many energy management methods that use historical data, the proposed strategy leads to reducing the dependence of the controller on the drive cycle. The simulation is implemented on a Plug-in Hybrid Electric Vehicle with parallel structure. The proposed method is compared with Dynamic Programming and instantaneous optimization. Evaluation of results shows that the proposed method, while simplicity and avoiding complicated mathematical relationships, in addition to fuel consumption reduction compared with instantaneous optimization, can manage SOC, properly. The results of this method are close to the global optimal solution of Dynamic Programming.