چكيده به لاتين
One of the major challenges in the development of the plug-in hybrid electric vehicles (PHEVs) is the design of the energy management system. In recent years, adaptive control strategies which improve vehicle performance in different conditions have attracted researchers’ attention.
The present dissertation is dedicated to design the plug-in hybrid electric vehicle control strategy through optimization of reference state of charge (SOC) in different driving conditions. To accomplish this, a detailed lithium-battery model is developed based on various test results and is utilized in line with an experimental engine map to form the PHEV model for simulation. Furthermore, the experimental collected data in real traffic conditions are used in order to investigate the effect of traffic conditions and to develop proper driving cycles.
The reference SOC trajectory is computed in different traffic conditions taking advantage of applying optimization methods including dynamic programming (DP) and Pontryagin’s minimum principle (PMP). On the other hand, the optimal SOC trajectory is estimated considering driving condition under the light of using adaptive Nero-Fuzzy inference system (ANFIS), which is taken into account as the near-optimal reference SOC trajectory in adaptive equivalent consumption minimization strategy (A-ECMS).
The simulation results reveal that by applying this strategy, the fuel consumption is significantly improved in comparison with the A-ECMS. Furthermore, the proposed methodology doesn’t require the knowledge of the entire driving cycle, and only the trip length and the average speed should be known a priori to estimate the optimal reference SOC trajectory in different traffic conditions, and consequently this method is on-line implementable.
