- OPtimization of Electric Vehicle Autonomy
Overview
About the project
To optimization the electric vehicule autonomy
The main objective of the OPEVA project is to explore the benefits that can be obtained from the interaction between the multiple actors involved in the modern “mobility experience” in order to optimize the autonomy of electric vehicles in a modern world which also requires consider sustainability and resource optimization. This translates into developing an energy-efficient power train and dynamic routing, into improving accurate range prediction techniques, improving EV grid integration, developing efficient charging technologies and guaranteeing a wider EV adoption. To accelerate the deployment of sustainable electric vehicles (EV) and improve EV market penetration, the project aims to develop technological solutions involving the overall ecosystem, thereby addressing limiting psychological factors such as range anxiety, high price, limited charging facilities, and duration of charging. The OPEVA will contribute to the key application area on Mobility and a number of major long-term challenges including embedded software, edge computing and embedded artificial intelligence. The project identifies six technology domains, involving 23 key technologies, and four non-technical domains which must be developed to move from conventional EVs to sustainable EVs. The project achievements will be tested in 9 collaborative demonstrators.
Our Objectives
Energy-efficient
power train
Improve the energy efficiency (by 10%) of the powertrain, considering intelligent battery integration, power electronics, advanced modeling and control combined with artificial intelligence (AI) echniques.
Energy-efficient
dynamic routing
Reduce (by 10%) energy consumption by enabling the dynamic routing profile taking into account both external factors of off-vehicle data (weather, road profile, traffic
information, …) and internal vehicle and driver factors (SoC and SoH, driver profile, vehicle power consumption, …).
Accurate range
prediction
Increase (by 10%) the predicted range of electric vehicles by merging internal data (enhanced SoX battery monitoring, auxiliary power consumption, driver profile, …) and external data (weather, road profile, traffic information, …) using safe and secure data acquisition technologies in
and out of the
vehicle.
Improved EV grid
integration
Safer and more efficient integration of large volumes of electric vehicles into the grid with reduced waiting time (by 10%) through new vehicle-to-grid (V2G) interactions and smart charging strategies and management systems
(improved grid planning and
operations).
Efficient charging
technologies
Reduce average charging time (by 10%) through advanced technologies (inductive charging, wireless batteries, and sensing and diagnostic technologies)
Wider EV
adoption
Improving the science and technology, research, innovation and marketing capacity in EV penetration aligned with the European priorities to achieve CO2 neutral, sustainable mobility, enabling electrification, strengthened with dissemination, exploitation and
outreach activities
Project structure
The project is structured into 8 work packages:
Requirement Analysis
Design Architecture Optimization
Smart Data Management
Battery Management System
Intelligent Energy Management
Demonstrators
Dissemination, Exploitation and Communication
Project Management
