*1.2. Contributions and Paper Structure*

Although there are many studies on energy management strategies (EMSs) for HEVs [12–14], there is almost no work that has focused on velocity prediction methods combined with traffic risk prediction and assessment of HEVs. In the literature, studies on driving prediction of predictive energy management of plug-in hybrid electric vehicles are put forward [15] without introducing traffic risk prediction and assessment of HEVs.

Thus, the inspiration behind this article is to conduct a brief review on vehicle speed prediction based on traffic environment and vehicle lateral risk assessment. Prospective designers of NEVs will benefit from a number of approaches in the field where they can better establish their solutions. In response to the above analysis, the contributions of this paper are as follows:


This survey is structured as follows. A review of vehicle speed prediction methods for NEVs, with an emphasis on macroscopic traffic flow models, data-based traffic flow models, and influence of vehicle lateral dynamic on speed prediction is introduced in Section 2. In Section 3, research status and analysis of the development of vehicle speed prediction methods for NEVs are established. The application field of speed prediction is discussed in Section 4. Lastly, conclusions and future trends are summarized in Section 5.

#### **2. Review of Vehicle Speed Prediction Methods for NEVs**

The velocity of a vehicle is closely related to its traffic environment. Therefore, it is important to accurately predict the change of traffic flow parameters for improving the accuracy of vehicle speed prediction. We now introduce macroscopic traffic flow models, data-based traffic flow models, and the influence of vehicle lateral dynamics on speed prediction in this sequel.
