DeepFEC: Energy consumption prediction under real-world driving conditions for smart cities
S Elmi, KL Tan - Proceedings of the Web Conference 2021, 2021 - dl.acm.org
The status of air pollution is serious all over the world. Analysing and predicting vehicle
energy consumption becomes a major concern. Vehicle energy consumption depends not …
energy consumption becomes a major concern. Vehicle energy consumption depends not …
Fine-grained RNN with transfer learning for energy consumption estimation on EVs
Electric vehicles (EVs) are increasingly becoming an environmental-friendly option in
current transportation systems, thanks to reduced fossil fuel consumption and carbon …
current transportation systems, thanks to reduced fossil fuel consumption and carbon …
Development of an energy consumption prediction model for battery electric vehicles in real-world driving: a combined approach of short-trip segment division and …
Y Pan, W Fang, W Zhang - Journal of Cleaner Production, 2023 - Elsevier
Due to the excellent energy-saving and environmental protection features, electric vehicles
(EVs) are gaining significant market penetration, especially in densely populated urban …
(EVs) are gaining significant market penetration, especially in densely populated urban …
[HTML][HTML] A system for electric vehicle's energy-aware routing in a transportation network through real-time prediction of energy consumption
S Modi, J Bhattacharya - Complex & Intelligent Systems, 2022 - Springer
To tackle the problem of range anxiety of a driver of an electric vehicle (EV), it is necessary
to accurately estimate the power/energy consumption of EVs in real time, so that drivers can …
to accurately estimate the power/energy consumption of EVs in real time, so that drivers can …
Vehicle energy dataset (VED), a large-scale dataset for vehicle energy consumption research
We present Vehicle Energy Dataset (VED), a large-scale dataset of fuel and energy data
collected from 383 personal cars in Ann Arbor, Michigan, USA. This open dataset captures …
collected from 383 personal cars in Ann Arbor, Michigan, USA. This open dataset captures …
A two-stage deep learning based approach for predicting instantaneous vehicle speed profiles on road networks
Prediction of vehicle speed profiles is vital to many transportation and vehicular applications.
However, accurately predicting these profiles remains challenging due to the complex and …
However, accurately predicting these profiles remains challenging due to the complex and …
Energy management for electric vehicles in smart cities: a deep learning approach
We propose a solution for Electric Vehicles (EVs) energy management in smart cities, where
a deep learning approach is used to enhance the energy consumption of electric vehicles by …
a deep learning approach is used to enhance the energy consumption of electric vehicles by …
A preference-aware meta-optimization framework for personalized vehicle energy consumption estimation
Vehicle Energy Consumption (VEC) estimation aims to predict the total energy required for a
given trip before it starts, which is of great importance to trip planning and transportation …
given trip before it starts, which is of great importance to trip planning and transportation …
Data-driven prediction of route-level energy use for mixed-vehicle transit fleets
Due to increasing concerns about environmental impact, operating costs, and energy
security, public transit agencies are seeking to reduce their fuel use by employing electric …
security, public transit agencies are seeking to reduce their fuel use by employing electric …
Ensembled Traffic-Aware Transformer-Based Predictive Energy Management for Electrified Vehicles
The predictive energy management strategy (PEMS) offers potential advantages in
enhancing the driving economy of electrified vehicles using vehicle speed prediction …
enhancing the driving economy of electrified vehicles using vehicle speed prediction …