Difference between revisions of "EVE"

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|Title=EVE
 
|Title=EVE
 
|ContactInformation=Slawomir Nowaczyk
 
|ContactInformation=Slawomir Nowaczyk
|ShortDescription=The EVE project aims to develop general lifetime models for all vital components in the electrical driveline of heavy duty vehicles. Hybrid and fully electric buses must take advantage of predictive maintenance services based on Machine Learning (ML) in order to remain competitive in the market despite their increased cost.
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|ShortDescription=Extending life of Vehicles within Electromobility era
 
|Description=The EVE project aims to develop general lifetime models for all vital components in the electrical driveline of heavy duty vehicles. Hybrid and fully electric buses must take advantage of predictive maintenance services based on Machine Learning (ML) in order to remain competitive in the market despite their increased cost. Those new technical solutions are enabled by the possibility of collecting more precise and extensive data. Motivating example is the main battery, a component that is responsible for large fraction of the vehicle cost, and is expected to survive over 10 years. In the project we will create a data-driven model for battery health degradation, based on Transfer Learning (TL) paradigm utilising recent advancements in the field of deep learning, namely Generative Adversarial Networks (GANs). Historical data on failures of electric driveline components is not available due to its technological novelty, thus the right support for drivers, fleet operators and OEMs requires studies and development of new ML techniques. Our goal is to enable predictive and prescriptive maintenance solutions that are capable of adapting to rapid changes in technology and continuous arrival of new observational data. This must be bootstrapped by leveraging available expert knowledge in the form of adaptive models for the evolving technology through AI solutions.
 
|Description=The EVE project aims to develop general lifetime models for all vital components in the electrical driveline of heavy duty vehicles. Hybrid and fully electric buses must take advantage of predictive maintenance services based on Machine Learning (ML) in order to remain competitive in the market despite their increased cost. Those new technical solutions are enabled by the possibility of collecting more precise and extensive data. Motivating example is the main battery, a component that is responsible for large fraction of the vehicle cost, and is expected to survive over 10 years. In the project we will create a data-driven model for battery health degradation, based on Transfer Learning (TL) paradigm utilising recent advancements in the field of deep learning, namely Generative Adversarial Networks (GANs). Historical data on failures of electric driveline components is not available due to its technological novelty, thus the right support for drivers, fleet operators and OEMs requires studies and development of new ML techniques. Our goal is to enable predictive and prescriptive maintenance solutions that are capable of adapting to rapid changes in technology and continuous arrival of new observational data. This must be bootstrapped by leveraging available expert knowledge in the form of adaptive models for the evolving technology through AI solutions.
 
|LogotypeFile=VolvoBus.jpg
 
|LogotypeFile=VolvoBus.jpg

Revision as of 17:40, 21 May 2019



Extending life of Vehicles within Electromobility era

EVE
Project start:
1 May 2019
Project end:
30 April 2023
More info (PDF):
[[media: | pdf]]
Contact:
Slawomir Nowaczyk
Application Area:
Intelligent Vehicles

Involved internal personnel
Involved external personnel
Involved partners
 - Volvo

Abstract

The EVE project aims to develop general lifetime models for all vital components in the electrical driveline of heavy duty vehicles. Hybrid and fully electric buses must take advantage of predictive maintenance services based on Machine Learning (ML) in order to remain competitive in the market despite their increased cost. Those new technical solutions are enabled by the possibility of collecting more precise and extensive data. Motivating example is the main battery, a component that is responsible for large fraction of the vehicle cost, and is expected to survive over 10 years. In the project we will create a data-driven model for battery health degradation, based on Transfer Learning (TL) paradigm utilising recent advancements in the field of deep learning, namely Generative Adversarial Networks (GANs). Historical data on failures of electric driveline components is not available due to its technological novelty, thus the right support for drivers, fleet operators and OEMs requires studies and development of new ML techniques. Our goal is to enable predictive and prescriptive maintenance solutions that are capable of adapting to rapid changes in technology and continuous arrival of new observational data. This must be bootstrapped by leveraging available expert knowledge in the form of adaptive models for the evolving technology through AI solutions.


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