An Economical Route Planning Method for Plug-In Hybrid Electric Vehicle in Real World

Abstract

Relieving the adverse effects of automobiles on the environment and natural resources has drawn the attention of numerous researchers. This paper seeks a new path to reach a target by focusing on the synergy of the vehicle and the environment. A real-time economical route planning method for a plug-in hybrid electric vehicle (PHEV) is proposed. Three main contributions have been made. Firstly, a real comparison test is performed to provide rudimentary understanding of the difference in energy usage and route planning between PHEVs and conventional vehicles. Secondly, an approach to obtain PHEV customized data is developed for road weight calculation, which is the essential step in route planning. This method incorporates traffic data from conventional vehicles with the PHEV simulation model, obtaining the required data. Thirdly, the travel expense estimation model (TEEM) is designed. The TEEM could be applied to calculate the road weight of each road segment considering the impact on energy consumption with respect to environmental factors, providing the grounds for route planning. The proposed method to plan an economical route is evaluated, and the results justify its validation and ability to improve fuel economy.

A Hierarchical Energy Management Strategy Based on Model Predictive Control for Plug-In Hybrid Electric Vehicles

“…These techniques, however, cannot guarantee ideal accuracy given the existing limitations. As an alternative to some existing efforts that predict the future velocity profile by a single method [28], we aim to improve the prediction accuracy by employing the strengths of different methods in various fields by proposing the SVPP method.…”

“…Benefiting from the evolved sensor networks and communication techniques, traffic data shared by vehicles on the road can be deeply utilized to predict the velocity profile. The PSD-based method has been applied in real applications [28]. This method can provide the general behaviors of future driving through statistical calculation.…”

“…where v std and v ave are the velocity deviation and average speed of the control horizon, respectively; λ is the reference battery SOC change slope; T is the total time of travel; and SOC f is the target battery SOC at the end of the journey. In (28), a larger v std and smaller v ave signify that the vehicle is in urban driving, demanding that more electric energy be consumed and leading to the battery SOC declining faster and vice versa. Fig.…”

“…Fig. 6 provides a result of the terminal battery SOC constraint estimated by (28). CTC denotes the conventional terminal battery SOC constraining method, while NTC is the proposed terminal battery SOC constraining method in this section.…”

“…According to the battery SOC reference planned by NTC, the MPC-based energy management strategy prompts the engine to operate more frequently on the highway than it would under CTC control. In the NTC method, the battery SOC reference is adjusted according to (28) to take advantage of the ideal performance of the engine during highway driving.…”