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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.

Approximately 50% energy is consumed during the acceleration of a city bus. Fuel consumption during acceleration is significantly affected by driving behavior. In this study, 13 characteristic parameters were selected to describe driving style based on analysis of how driving influences fuel consumption during acceleration. The 100,000 km real-world vehicle running data of six drivers on three city buses in a particular bus line in Tianjin, China were sampled using a vehicle-on-line data logger. Based on the selected characteristic parameters and collected driving data, an evaluation model of the fuel consumption level of a driver was established by adopting the method of decision tree C4.5. For two-level classification, the model has over 85% prediction accuracy. The model also has the advantages of having a few training samples and strong generalization. As an example of the model application, the fuel-saving potential of a driver under optimal operations was analyzed.

Battery state estimation is one of the most important decision parameters for lithium battery energy management. It plays an important role in improving battery energy utilization, ensuring battery safety and enhancing system reliability. This paper is proposed to provide a dynamic correction of SOC in the full working condition, including static condition and dynamic condition. Based on the Coulomb-counting method, the current SOC value of the battery is calculated. Under the static conditions, the open circuit voltage of the battery is used to directly collect the initial SOC. Under the dynamic working conditions, the open circuit voltage of the battery is estimated by the sliding mode observer. Based on the deviation between the calculated and estimated values of the open circuit voltage, the current coefficient of the Coulomb-counting method is dynamically corrected by PI strategy.