Predictive eBoost

Description 

The objective of this project is to design new strategies, based on Machine Learning algorithms and data analysis, to improve efficiency and the performance of electric vehicle motors and batteries.

The batteries of electric vehicles are increasingly powerful and have more autonomy but also need proper thermal management. Current strategies only take into account the current temperature of vehicle components when deciding when to cool or heat them. The development of smarter decision-making systems, such as machine learning models, will make it possible to achieve better efficiency and consumption criteria.

The focus of this project is to use vehicle path information, such as the slope of the road or the speed of circulation, to decide when to activate battery cooling and thus be able to improve current strategies.

Duration of the project 
December, 2021 - December, 2022
Technology 
Python,
Matlab,
simulink,
sklearn,
Tensorflow,
Pytorch,
Pandas
Areas of expertise involved in the project 
Project Manager 

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