Simulation Study On Control Strategy Of PHEV Power Source

Hybrid Electric Vehicle can be abbreviated to HEV. HEV is the offspring of conventional vehicle and electric vehicle. It owns the EV's strong suit that cost less fuel, also, it owns better power ratio and energy ratio compared with oil fuel which improves the conventional vehicle's emission and fuel economy distinctly, and increases the continue-distance. HEV acts as a connecting link between the conventional vehicle and electric vehicle.Control strategy, as one of the key techniques of HEV, is the algorithm to realize vehicle energy management and powertrain control. The characteristics of high efficiency, low emissions and high performance of the HEV depend largely upon the control strategy.The control strategy is a complicated problem involving decision making of complex problem and time varying nonlinear system control. Because of the complexity of the hybrid powertrain itself and the synergetic operation of different components, it is difficult to construct an accurate mathematical model of the hybrid powertrain. The unpredictability of driving conditions and driver's operation and the difficulty of driving intention judgment resulted from the diversity of driving style increase the difficulty of the design of the control strategy.With the prerequisite of satisfying the performance and drivability, it distributes optimally the driving power among the electric motor controller (MC), internal combustion engine (ICE), energy storage system (ESS) and other power components, commands each subsystem to operate synergetically to obtain the optimal energy management and the balance between efficiency and emissions.First of all, a simulation model of the HEV power Source is constructed including FC model, MC model and ESS model, using empirical modeling approach with the aid of theoretical modeling, and M file is programmed to beintegrated with ADVISOR. Some of main modules in ADVISOR are presented in detail, e.g. vehicle module, FC module, MC module and ESS module. Simulation results demonstrate the model built can accurately analyze fuel cost and performance absolutely has the ability to weigh the performance of control strategy.On this simulation platform, control strategies for PHEV, especially electronic assist control strategy and its design procedure are presented. Based on the operating modes of the hybrid powertrain, the efficiency characteristics of the ICE and the MC and the internal resistance characteristics of the battery, the energy management strategy of PHEV and the transition conditions for different operating modes are deeply analyzed.Considered both of fuel economy and emission characteristic, a Fuzzy Optimization Control Strategy for PHEV is designed by employing fuzzy logic. A fuzzy inference system with 25 rules is constructed using the ratio of the torque request to the optimal engine torque and the battery state-of-charge (SOC) as the inputs, and the MC torque as the output. It works as the kernel of a fuzzy torque distribution controller to determine the optimal distribution of the torque request between the ICE and the MC.Referred to CA6110, Parameters of drivetrain, such as ICE, MC and battery are designed. Besides those parameters imported into GUI, we also embed two control strategies above into ADVISOR successfully and complete vehicle's power simulation. Simulation results reveal that, compared with the electric assist control strategy, the proposed fuzzy optimization control strategy improves fuel economy as well as emission performance. Because fuzzy control does not depend on precise system model, control algorithms is not profound, both the robust and real-time performance are definitely excellent as a prospect control algorithms .The above work has laid the foundation for the application of fuzzy logic control in the real HEV.After the validity of fuzzy optimization control strategy is proved, effect factors on performance of PHEV are analyzed:1. Initial SOC. As initial value decrease, fuel cost radio and emission of NOx, CO increase, it means that the lower SOC of battery is, the more output power ICE applies for generating electricity, because ICE operates in the areas of medium and large loads, where the emission of HC is so steady, the emission of HC basically iskept constant.If SOC is not in the perfect range, at the beginning of every driving condition, control algorithm will adaptationally regulate MC output torque so as to maintain SOC within its operation range more effectively. If SOC is too high, MC will increase positive torque for consuming electricity, if SOC is too low, MC will increase negative torque for charging the battery.2. The threshold of SOC is determined by the characteristic of battery, which will affect the choice of operating modes in control strategy, if PHEV operates in different modes, the operation points of ICE, MC and other power components will be varied, the torque distribution will be changed, fuel cost of the powertrain will be different. Simulation results reveal that the lower threshold is in the same range, the less fuel costs.3. After the adaptability of fuzzy optimization control strategy is tested by simulations above in the same driving condition with different configuration parameters, the universality and robust performance will be improved in different driving conditions. The result that fuzzy optimization control strategy is less sensitive than electronic assist control strategy indicates its excellent robust in controlling under different driving conditions efficiently.