Research On Control Strategies Of Full Hybrid Electric Vehicle With An Integrated Starter Generator

Full hybrid electric vehicle (FHEV) with an integrated starter generator (ISG) is akind of HEV integrated with the engine, the ISG and the electric motor/generator(EMG). The system has the advantages of PHEV includes both single shaft andparallel shaft structure. It is important to improve the efficiency of the vehicle bystudying the control strategies for drive system. It also meets the needs of new energyvehicles industry.Firstly, we build the models of major components for the complex ISG-FHEV.These models include engine model, the ISG and the EMG model, battery model. Andthe central power splitter model, the electric power splitter model in different drivemodes. We also consider the transmission ratio and energy loss to build the total torqueand speed requirement model. Then we analyze the power-balancing problem in therunning processes of the complex ISG-FHEV. And we designed the strategy forcontrolling the engine within its peak-efficiency region, the minimum fuelconsumption control strategy for the engine, the control strategy for electrical motordrive system and the charging-discharging control strategy for batteries. We alsoanalyze the disadvantages of above strategies. On this base, we design the fuzzy logiccontrol strategy in accordance with the characters of the complex ISG-FHEV duringdriving or regenerative braking, which aims to reducing the dependency on the ISG forthe engine controlling, and improving the efficiency for the electric motor drivesystem. We establish the simulation model and the control strategies in ADVISOR.The control strategies for drive system are demonstrated by the separate simulationand the comprehensive simulation results in different driving cycles.Simulation results show that the efficiencies of the engine and the motor areimproved to the ISG-FHEV controlled by the strategy for peak-efficiency controlstrategy, compared the PHEV controlled by benchmark control strategy. In the comprehensive driving cycle, the engine efficiency is improved9%, the motorefficiency is improved11.4%, and the final SOC is increased by0.1. The fuelconsumption per100km is reduced by around9.98%. When simulated in threedifferent driving cycles, compared with peak-efficiency control strategy, the minimumfuel consumption control strategy can improve the engine efficiency and reduce thefuel consumption. However, it also reduces the efficiency of electric motor drivesystem and increases the electric energy consumption. When compared with the abovetwo control strategies, the fuzzy logic control strategy that in driving condition canimprove the efficiency of battery charging-discharging, the engine efficiency isimprove up to1%, fuel consumption per100km is reduced by around3.2%. When theinitial SOC is set to0.35, the fuzzy logic control strategy that in regenerative brakingcondition can increase the SOC quickly and avoid the low SOC of batteries, theefficiency of battery charging-discharging is up to86.9%. At the same time, itdecreases the inertial torque loss of engine in regenerative braking condition. The fuelconsumption per100km is down to3.55L.