Beyond Digital Twin, Innovative Use of AI/ML Technology from Ideation to Design of Next Generation Electric Drive Systems BorgWarner Incorporated

Format:

Book

Conference/Event

Author/Creator:

David, Pascal, author.

Contributor:

Bourniche, Eric

Nanjundaswamy, Harsha

Oueslati, Skander

Conference Name:

WCX SAE World Congress Experience (2024-04-16 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2024

Summary:

Accelerated adoption of electric propulsion system in mobility industry has stressed the time and iterations of product development cycle which was traditionally known to go over multiple iterations and phases. Current market demands a timely introduction of compelling products that brings high value to end user. Further, a growing emphasis over reducing mineral content using sustainable options and process, adds further complexity to multi-objective-optimization of electric drive systems. At BorgWarner our engineers use Digital-Twins, physics-based models which closely represent BorgWarner products in greater dept (physics) thus allowing an improved assessment of product design (components and systems) to target application at very early stage in product development. The spring success with Digital-Twin, BorgWarner furthered enhanced the model through introducing Artificial Intelligent (AI) and Machine Learning (ML) technologies in both modelling and virtual sensing.This paper will provide reader an in-depth view of technology aspects with Digital-Twin and introduce AI and ML algorithms within Digital-Twin, beginning with a brief introduction on AI and ML technologies, the paper will also go in depth on in-use applications of these technologies at BorgWarner, such as, Deep learning virtual sensors usage. The paper will include a clear description of specialty tools and methods adopted by BorgWarner for data cleaning, model training and validation. The paper will provide reader an insight into how such trained AI-ML models were developed and trained using data provided by validated 1D and high-fidelity models from Amesim and COMSOL respectively, where each of those reference datasets were verified using real hardware tested in a vehicle environment. Although scope within BorgWarner product development was surrounding integrated drive module, for the purpose of showcasing an example the paper will provide technical insight to "Deep Learning Virtual Sensor for Power Module". Towards conclusion, our goal is to showcase the method of integrating AI-ML models in Digital Twin and how those learnings can be translated into a product feature which could minimize controls complexity while enhancing accuracy and safety of the electrified propulsion system

Notes:

Vendor supplied data

Publisher Number:

2024-01-2862

Access Restriction:

Restricted for use by site license