Research on Energy Management Method of Plug-In Hybrid Electric Vehicle Based on Travel Characteristic Prediction

Abstract

In the research on energy management methods of plug-in hybrid electric vehicles, it is expected that a future trend will be to optimize energy management using the information provided by the global positioning system (GPS) and intelligent transportation system (ITS), which is relatively scarce in current research. This study proposes a PHEV energy management method based on travel characteristic prediction. Firstly, this study processes the historical travel data of a certain driver obtained by GPS and ITS and uses the established Markov trajectory prediction model based on key points to predict the trajectory and mileage. Then, on the basis of characteristics analysis of historical travel data, while considering traffic information to form a target cycle, the driving cycles are classified and identified based on traffic information predictions. Then, according to the reasonable SOC allocation range of the four typical cycles, the planning algorithm of the SOC reference trajectory is determined and verified. Finally, based on the previous work, an A-ECMS energy management method based on travel characteristic prediction is established. By comparing different energy management methods, the developed energy management method based on travel characteristic prediction can reasonably utilize power batteries. The fuel saving is about 8.95% higher than the rule-based energy management method, which can effectively improve the whole vehicle’s fuel economy and optimization ability.

Research on Energy Consumption Generation Method of Fuel Cell Vehicles: Based on Naturalistic Driving Data Mining

“…Previous studies have shown that vehicle energy consumption is sensitive to driving cycles [6,7]. In view of this hot topic, we have studied the characteristic relationship between energy consumption and the driving cycles of plug-in hybrid electric vehicles (PHEVs) [8,9]. Chlopek et al, based on the energy consumption test data of battery electric vehicles (BEVs) and conventional ICEVs, analyzed the average speed and the average absolute value of the product of speed and acceleration, which were the best characteristics to describe vehicle energy consumption [10].…”

Review of Energy Management Methods for Fuel Cell Vehicles: From the Perspective of Driving Cycle Information

“…Previous studies have shown that driving cycle information can make a difference in vehicle energy management [12][13][14]. In view of this hot topic, we have studied the characteristic relationship between energy management and the driving cycle of PHEV [15]. Driving cycle information discussed in this paper indicates the vehicle speed trajectory, which is an indication of vehicle speed versus sample time [16,17].…”

Prediction of Criminal Suspect Characteristics with Application of Wavelet Neural Networks

“…Then, the historical data on car driving is characterized, and the collected data is identified and classified, taking into account traffic information. The SOC algorithm plans the driving trajectory, and finally, the previously obtained data is managed using the A-ECMS energy management method [24]. Santos, N. D. L. et al investigated the effect of changes in facial expressions of suspects on the prediction of idiosyncratic diagnostic recognition to provide public safety personnel with a characteristic image of the suspect.…”