Predictive eBoost’s project aims to improve the performance of electric vehicles’ powertrain and batteries
RODIN: Robust methods for statistical inference, data integrity and interference management in 5G communication networks and applications
April 22, 2022
BIOPHOCH: Photonic technologies and machine learning to improve the diagnosis of visual, neurological and hematological diseases
April 8, 2022The inLab FIB of the Universitat Politècnica de Catalunya · BarcelonaTech (UPC) collaborates with SEAT and the Volkswagen Group Innovation (Wolfsburg, Germany) in the Predictive eBoost project.
This project is focused on designing new strategies, based on machine learning algorithms and data analysis, to improve the efficiency and performance of electric vehicle motors and batteries.
Which are the current strategies?
Electric vehicle batteries are becoming more powerful and with more autonomy, but they also need proper thermal management. Current strategies only take into account the current temperature of the vehicle components to decide when to cool or heat them.
Data and machine learning as the way for improvement
The focus of Predictive eBoost is to use the vehicle's journey information, such as road slope or speed, to decide when to activate the battery cooling and thus be able to improve current strategies. This machine learning model will lead to better efficiency and consumption criteria.
Why should a vehicle invest energy in cooling its battery if it finds a long slope in which it can cool on its own in just a few minutes? If it is known that a significant acceleration is approaching, wouldn't it be better to prepare the vehicle in advance to mitigate the effort it will have to make? These, among others, are some of the questions that this project seeks to answer.
In conclusion, if we consider not only the temperature of the components of the vehicle, but also its condition, the environment and the journey, we can optimize the performance and consumption of the battery in order to increase its autonomy and useful life, and reduce the environmental impact. In this way, the results of the project will be an important step forward in the implementation of the electric vehicle as it will allow to extend the distance travelled without having to stop, while providing a more satisfying driving experience.
Signing of the collaboration agreement and configuration of the R&D team
The research group counts with Dr. Gerhard Lux, of SEAT, and Marc Duevel, of Volkswagen Innovation Group, and is led by Professor Ernest Teniente, Director of the inLab FIB, the innovation and research laboratory of the Faculty of Informatics of Barcelona (FIB) of the UPC.
Sector
Would you like to know more?
Projectes Relacionats
- The research group Barcelona Innovative Transportation (BIT) and the innovation and research laboratory of Barcelona School of Informatics (inLab FIB) at the Universitat Politècnica de Catalunya - BarcelonaTech (UPC) is taking part in the project eBRT2030 (European Bus Rapid Transit of 2030: electrified, automated, connected).
- MobilitApp is an application capable of predicting the mode of transport used by citizens using a deep learning tool based on three mobile phone sensors.
- Autonomous vehicles are a reality that we are getting closer to gradually but inevitably. The Centre for Sensors, Instruments and Systems Development (CD6) of the Universitat Politècnica de Catalunya - BarcelonaTech (UPC) has developed technologies for this type of vehicles that improve safety on journeys. To meet the needs of perceiving the environment at long range, a very complete set of sensors has to be developed, including cameras, LIDAR (Light Detection and Ranging) and radar.
- The aim of ANEM, in which SISCOM of the UPC participates, has been to develop, using latest generation models and techniques, a modular platform that can generate synthetic data (that is, not real data but data that can be used to train the artificial intelligence), protected from potential threats in the area of cybersecurity.
