A Study On Energy Management Control Strategy For Plug-in Hybrid Electric Vehicle

Resource shortage and environment pollution are big challenges in the word. Plug-in Hybrid Electric Vehicle (PHEV),which can combine the advantage of electric vehicle and conventional internal combustion engine vehicle,becomes research spot recently as a new type of vehicle with low fuel consumption, low emissions, and unlimited driving range. Due to economic and environmental benefits,PHEV development is considered as an effective way to solve these problems. Energy management strategy is the key technique of hybrid electric vehicle control system,which determine driving mode and power distribution among engine and motor to achieve the best vehicle performance under different driving scenario.The dissertation presents parameters matching of powertrain, vehicle modeling and energy management strategy design for a parallel plug-in hybrid electric vehicle. The mathematic analytical and simulation model method were adopted in PHEV control strategy design. The objective is to get reasonable control strategy which can be applied in real HEV. The main contributions in this dissertation are as following:According to the specific objective, the powertain parameters matching and layout, vehicle operation mode and Controller Area Network (CAN) bus control system are studied. Based on powertrain operation principle and theory analysis, the PHEV forward simulation model which include dynamic model, engine model, motor model and drive model were built in MATLAB / Simulink environment using empirical modeling approach with the aid of theoretical modeling. The simulation model was evaluated by real vehicle test results, which provides essential simulation platform for the control strategy design. In this simulation platform, considering PHEV has Charge Depleting (CD) and Charge Sustaining (CS) mode, multi-level logic threshold control strategy was designed which includes electric motor assist control strategy in CD mode and engine optimal curve control strategy in CS mode. In CD mode, in order to reduce the electric system power loss, a simplified mathematical model of the PHEV was constructed to obtain optimal solutions for depleting the battery to a given final SOC under constant vehicle speeds. An optimal engine on/off threshold and motor output mechanical power were obtained from theoretical analysis and simulation for constant speed cases and applied to typical drive cycle simulations. In CS mode, control strategy parameters were optimized offline using multi-objective non-dominated sorting genetic algorithm (NSGA) to minimize fuel consumption and emissions. A set of optimal pareto solutions can be obtained by NSGA. The vehicle designer can decide which is the best solution according to fuel consumption and emissions criterion. On this basis, the fuzzy logic torque control strategy was constructed to adjust for different range and drive cycle. The inputs are vehicle power demand, battery SOC and driving distance. The control rules were trained using improved genetic algorithm. The simulation results indicate that the fuzzy logic control strategy has strong robustness and better fuel economy than rule-based control strategy in different driving conditions.Finally, the dSPACE hardware in the loop simulation platform was built to verify the proposed control strategy and the road test was made for the prototype vehicle. The results show that the vehicle performance has reached the expected requirements.