Range prediction is a standard feature in most modern road vehicles, allowing drivers to
make informed decisions about when to refuel. Most vehicles make range predictions …

This paper introduces an innovative approach to addressing a critical challenge in the
electric vehicle (EV) industry—the accurate estimation of the state of charge (SOC) of EV …

Electric vehicles (EVs) seem to be an eminent alternative for ground transportation. Yet,
human drivers may suffer from EV's range anxiety, which is engendered by EV's limited …

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 …

This paper examines the potential of AutoML for predicting the range and State of Charge
(SOC) of Electric Vehicles (EVs). Unlike traditional SOC estimation methods, such as …

In perspective of their environmental friendliness and energy efficiency, Electric Vehicles
(EVs) are posing a threat to traditional gasoline automobiles. Identifying the future charging …

A deep neural network-based approach of energy demand modeling of electric vehicles
(EV) is proposed in this paper. The model-based prediction of energy demand is based on …

Large-scale and high-precision predictions of the charging energy required for electric
vehicles (EVs) are essential to ensure the safety of EVs and provide reliable inputs for grid …

Battery and energy management methodologies have been proposed to address the design
challenges of driving range and battery lifetime in electric vehicles (EVs). However, the …

Due to sparse charging infrastructure and short driving ranges, drivers of battery electric
vehicles (BEVs) can experience range anxiety, which is the fear of stranding with an empty …