Vehicle Operations
Total results returned: 2
Welcome to the Electric Vehicle Operations page, where you’ll find a range of resources dedicated to optimising the performance and efficiency of electric vehicles. This section provides access to reports, scientific studies, and technical papers that explore topics such as energy management, operational efficiency, and the role of advanced control systems in EV operations. Whether you're studying fleet operations, real-time monitoring, or performance optimisation, these resources offer crucial insights to enhance the way electric vehicles function on the road.
Optimizing Electric Vehicle Operations for a Smart Environment: A Comprehensive Review
This review article examines the deterministic control model and centralized control model, the types of EV models, and their tabular comparison. Additionally, expressing the communication standards to deal with compatibility challenges in charging stations, the effects of EV integration with the power grid, and various methods such as smart charging, dumb charging, and flexible charging are the main goals of this review article.
Electric Vehicle Manufacturers, Electric Vehicle Owners and Consumers, Energy and Utility Companies, Government and Regulatory Bodies, Researchers
Battery Technology, Charging Controllers, Charging Stations, Electric Vehicles, Plug-in Hybrid Electric Vehicle
Link:
Researchgate.net
Data-Driven Approaches to Battery Health Monitoring in Electric Vehicles Using Machine Learning
This article explores battery health monitoring in electric vehicles (EVs) using machine learning to address challenges in battery durability and enable new business models. It introduces a virtual battery prototype that applies supervised learning methods, such as Random Forest and Deep Neural Network regression, to estimate real-time energy slack and monitor battery health. The study also presents a carbon balance optimization application, aiming to minimize carbon emissions and charging costs for EV fleets through grid optimization. The model enables continuous battery health monitoring, opening opportunities for innovative commercial use cases for EV users, fleet managers, and grid operators.
Electric Vehicle Manufacturers, Electric Vehicle Owners and Consumers, Energy and Utility Companies, Fleet Managers and Operators, Government and Regulatory Bodies, Researchers
Artificial Intelligence, Battery Health, Computer Science, Data Science, Data-Driven Approaches, Electric Vehicles, Industry 4.0, Internet of Things, Machine Learning, Smart Manufacturing, Vehicle, Vehicle Reliability
Link:
researchgate.net