Energetic Macroscopic Representation Based Energy Management Strategy for Hybrid Electric Vehicle Taking into Account Demand Power Optimization

To further explore the potential of fuel economy for hybrid electric vehicle (HEV), a methodology of demand power optimization is proposed. The fuel consumption depends not only on the EMS, but also on the way to operate vehicle. A control strategy to adjust driver’s demand before power splitting is necessary. To get accurate and reliable control strategy, two aspects are the most important. First, a rigorous and organized modeling approach is a base to describe complicated powertrain system of HEV. The energetic macroscopic representation (EMR) is a graphical synthetic description of electromechanical conversion system based on energy flow. A powertrain architecture of HEV is described explicitly via the EMR. Second, the effectiveness of EMS and the reasonability of driving operations are vital. Generally the EMS includes rule based that can be used online with suboptimal solution and optimization based that ensures the minimum fuel consumption with heavy computation duty and requirement of prior knowledge. Combination of two kinds of EMS to trade off optimization and instantaneity is an intelligent selection. The power optimization strategy includes three procedures. Firstly, an offline rules library is established. The global optimization algorithm is utilized to compute optimal demand power for three typical driving cycles. Each set of control rules is extracted by neural network (NN). Secondly, online application strategy based on driving pattern recognition is introduced. Finally, due to driver’s stochastic operation, a modification strategy is developed to guarantee the satisfaction of high demand power in the special case. The Japanese 10-15 mode cycle and a typical real-world cycle are chosen as the random scheduled route to test the effectiveness of proposed strategy.