This study contains a survey on the architecture of electric vehicles and an artificial neural
network application for prediction of energy consumption in all-electric vehicles. In this study …

As an emerging technology, photovoltaic/thermal (PV/T) systems have been gaining
attention from manufacturers and experts because they increase the efficiency of …

This study proposes a feedforward deep neural network to predict the parameters of the
lithium-ion battery in electric vehicles. Correlation analysis is used to select the candidate …

This article explores the crucial task of estimating State of Charge (SOC) in Hybrid Electric
Vehicles (HEVs) and examines the applicability of various regression models for this …

In this study, supervised machine learning techniques like random forest (RF), K-nearest
neighbour (KNN), multiple linear regression (MLR), and artificial neural networks (ANNs) …

The all-wheel drive (AWD) hybrid system is a research focus on high-performance new
energy vehicles that can meet the demands of dynamic performance and passing ability …

This study examines the use of artificial neural networks (ANNs) in real-time adaptive control
for electric vehicle (EV) propulsion systems, with the goal of enhancing performance and …

Accurate estimation of energy consumption for electric vehicles plays a crucial role in
effectively mitigating mileage anxiety for drivers and managing energy usage. A novel …

Accurate estimation of energy consumption (EC) for electric vehicles (EVs) plays a crucial
role in effectively mitigating mileage anxiety for drivers, optimizing route planning, and …