[HTML][HTML] Lithium-ion battery data and where to find it
Lithium-ion batteries are fuelling the advancing renewable-energy based world. At the core
of transformational developments in battery design, modelling and management is data. In …
of transformational developments in battery design, modelling and management is data. In …
Federated learning in intelligent transportation systems: Recent applications and open problems
Intelligent transportation systems (ITSs) have been fueled by the rapid development of
communication technologies, sensor technologies, and the Internet of Things (IoT) …
communication technologies, sensor technologies, and the Internet of Things (IoT) …
An energy‐efficient blockchain approach for secure communication in IoT‐enabled electric vehicles
KBR Bhaskar, A Prasanth… - International Journal of …, 2022 - Wiley Online Library
In today's world, electric vehicles (EVs) play a significant role in transportation automation
systems, and these vehicles are the replacement for fossil fuel usage vehicles. An EV …
systems, and these vehicles are the replacement for fossil fuel usage vehicles. An EV …
Multi-channel profile based artificial neural network approach for remaining useful life prediction of electric vehicle lithium-ion batteries
Remaining useful life (RUL) is a crucial assessment indicator to evaluate battery efficiency,
robustness, and accuracy by determining battery failure occurrence in electric vehicle (EV) …
robustness, and accuracy by determining battery failure occurrence in electric vehicle (EV) …
Federated learning for big data: A survey on opportunities, applications, and future directions
Big data has remarkably evolved over the last few years to realize an enormous volume of
data generated from newly emerging services and applications and a massive number of …
data generated from newly emerging services and applications and a massive number of …
Mobile charging station placements in Internet of Electric Vehicles: A federated learning approach
In Internet of Electric Vehicles (IoEV), mobile charging stations (MCSs) can be deployed to
complement fixed charging stations. Currently, the strategy of MCSs is to move towards the …
complement fixed charging stations. Currently, the strategy of MCSs is to move towards the …
Federated learning-based multi-energy load forecasting method using CNN-Attention-LSTM model
G Zhang, S Zhu, X Bai - Sustainability, 2022 - mdpi.com
Integrated Energy Microgrid (IEM) has emerged as a critical energy utilization mechanism
for alleviating environmental and economic pressures. As a part of demand-side energy …
for alleviating environmental and economic pressures. As a part of demand-side energy …
Optimal Deployment of Electric Vehicles' Fast‐Charging Stations
As climate change has become a pressing concern, promoting electric vehicles'(EVs) usage
has emerged as a popular response to the pollution caused by fossil‐fuel automobiles …
has emerged as a popular response to the pollution caused by fossil‐fuel automobiles …
Deep learning LSTM recurrent neural network model for prediction of electric vehicle charging demand
J Shanmuganathan, AA Victoire, G Balraj, A Victoire - Sustainability, 2022 - mdpi.com
The immense growth and penetration of electric vehicles has become a major component of
smart transport systems; thereby decreasing the greenhouse gas emissions that pollute the …
smart transport systems; thereby decreasing the greenhouse gas emissions that pollute the …
Smart meter-based energy consumption forecasting for smart cities using adaptive federated learning
Forecasting short-term residential energy consumption is critical in modern decentralized
power systems. Deep learning-based prediction methods that can handle the high variability …
power systems. Deep learning-based prediction methods that can handle the high variability …