The Research Of Energy Management Strategy For Plug-In Hybrid Electric Vehicles

The energy shortage and environmental pollution are not only big global challenges in the 20th century, but key elements to restrict the sustainable development of automotive industry. The energy conservation and emission reduction technologies of conventional fuel vehicle can not meet the requirement of the sustainable social development, so it has become the chief task for present automotive industry to develop new style vehicles with low fuel consumption and emissions. Based on conventional hybrid electric vehicle (HEV), Plug-In hybrid electric vehicle (PHEV) as a new generation of hybrid electric vehicle emerged. The most prominent feature of PHEV is that it can use the power grid to charge up the vehicle battery. As the most promising hybrid electric vehicle, PHEV has great economic benefits and social benefits, and it has attracted more and more attention from automobile enterprises, research institutes, national governments. In the field of new energy vehicles, PHEV is becoming focus of study.The excellent performance of hybrid electric vehicle is based on energy management strategy (EMS). The core issue of energy management strategy is to control the size and direction of energy flow among the components, such as motor, engine, battery, etc. The energy management strategy could distribute the energy reasonably. Under the prerequisite of satisfying the driving requirements, the energy management strategy could improve fuel economy and emissions performance of the hybrid electric vehicle. The energy management strategy is one of the core technologies of Plug-In parallel hybrid electric vehicle. The vehicle driving ability, economic and emission performances are influenced directly by the quality of the energy management strategy. In this paper, energy management strategy for Plug-In parallel hybrid electric vehicle is researched, and the main contents are described as follows:Energy management strategy for Plug-In parallel hybrid electric vehicle based on battery energy observation is designed. Battery energy observation unit is established based on the characteristic of Plug-In parallel hybrid electric vehicle. Based on established battery energy observation unit, equivalent fuel consumption minimization strategy is applied in the energy management strategy for Plug-In hybrid electric vehicle. The equivalent fuel consumption minimization strategy is real-time optimization strategy, it can achieve real-time optimal control.Fuzzy Rule-Based energy management strategy for Plug-In parallel hybrid electric vehicle is designed. Fuzzy rules for different drive cycles are designed separately. Vehicle require torque and battery state of charge (SOC) are the input variables of the energy management fuzzy controller, request engine output torque is the output variable of the energy management fuzzy controller. Fuzzy Rule-Based energy management strategy is robust, good timeliness and very practical. In the next study of this paper, based on the study of fuzzy Rule-Based energy management strategy, the driving cycle recognition is applied in the energy management strategy.The driving cycle recognition is applied in the energy management strategy. Driving cycle recognition based on BP neural network (NN), driving cycle recognition based on probabilistic neural network and driving cycle recognition based on support vector machine (SVM) are applied in the energy management strategy separately.Simulation is performed based on electric vehicle simulator ADVISOR. Compare with deterministic Rule-Based energy management strategy, energy management strategy based on battery energy observation and fuzzy Rule-Based energy management strategy could distribute the torque more reasonably between engine and motor, and improve fuel economy effectively. The application of the driving cycle recognition achieves favorable effect in the energy management strategy. Based on the type of the drive cycle identified by the driving cycle recognizer, the appropriate energy management strategy is choosed. It can improve fuel economy effectively. Best effect will be obtained when driving cycle recognition based on support vector machine is applied in the energy management strategy.