Optimization Energy Management Strategy of Plug-In Hybrid Electric City Bus Based on Driving Cycle Prediction

The fuel economy of plug-in hybrid electric city bus (PHEV) is deeply affected by driving cycle and travel distance. To improve the adaption of energy management strategy, the equivalent coefficient of fuel is the key parameter that needs to be pre-optimized based on the predicted driving cycle. An iterative learning method was proposed and implemented in order to get the best equivalent coefficient based on the predicted driving cycle and battery capacity. In the iterative learning method, the energy model and kinematics model of the bus were built. The ECMS (Equivalent Consumption Minimization Strategy) method was applied to obtain the best fuel economy with the given equivalent coefficient. The driving paths and running time of city buses were relatively fixed comparing with other vehicles, and their driving cycle can be predicted by route content. The proposed optimized strategy was applied on the factory sets of plug-in hybrid electric city bus. In general case, energy management strategies of PHEV bus are often developed by several standard driving cycles, which are different with realistic driving condition. However, in this case, the energy management strategies were pre-optimized based on the future target application environment. The results indicate that the pre-optimized city bus can get better fuel economy and the profiles of battery SOC (State of charge) are well planned. The optimized strategy is also adequate for other types of PHEV vehicles, which are sensitive to driving cycle.