Electrification of transportation systems is increasing, in particular city buses raise enormous
potential. Deep understanding of real-world driving data is essential for vehicle design and …

This paper presents a novel probabilistic data-driven approach to trip-level energy
consumption estimation of battery electric vehicles (BEVs). As there are very few electric …

Energy consumption modeling for electric vehicles is conducive to achieving eco-driving
optimization and alleviating driver 'range anxiety'. However, it remains challenging due to …

Locomotive traction energy consumption is a multivariate coupled nonlinear system closely
related to many factors such as locomotive properties, routing, line conditions, and operating …

The optimal control based on the forecast of vehicle speed in the future is of great
significance to vehicle safety system and energy management systems of hybrid electric …

With the development of technology, speed prediction has become an important part of
intelligent vehicle control strategies. However, the time-varying and nonlinear nature of …

The state of charge (SoC) of a vehicle battery can tend to vary depending on the driver's
driving patterns and circumstances. To accurately predict the SoC level, it is necessary to …

Vehicle speed prediction plays an important role in hybrid and electric vehicle energy
management. However, vehicle speed prediction is influenced by various factors such as …

Vehicle velocity prediction is of great significance in electric vehicle (EV) energy
consumption evaluation. However, vehicle velocity prediction is complicated due to the …

For a data-driven bus line energy consumption prediction model, building it with statistical
indicators on some variables appeared in the whole bus route, such as average speed …