Reinforcement Learning Technique for Parameterization in Powertrain Controls
As climate change looms large, the automotive industry gears up for an Electric Vehicle (EV) transition to pull down our net global greenhouse emissions to zero together with the clean energy transition. It becomes the need of the hour to optimize the use of our resources and meet the requirements of time, effort, cost, accuracy and transient performance brought in by the stringent emission norms and the Real Driving Emissions (RDE) test. The authors present a Reinforcement learning technique to address the real-world challenges for accelerated product development. Reinforcement Learning was used to parameterize a time varying electromechanical system and proved effective in modelling the stochastic nature of processes in powertrain development.
- Record URL:
-
Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/01487191
-
Supplemental Notes:
- Abstract reprinted with permission of SAE International.
-
Authors:
- Sidharthan, Gautham
- Venkobarao, Vivek
-
Conference:
- Symposium on International Automotive Technology
- Location: Pune , India
- Date: 2021-9-29 to 2021-10-1
- Publication Date: 2021-9-22
Language
- English
Media Info
- Media Type: Web
- Features: References;
-
Serial:
- SAE Technical Paper
- Publisher: Society of Automotive Engineers (SAE)
- ISSN: 0148-7191
- EISSN: 2688-3627
- Serial URL: http://papers.sae.org/
Subject/Index Terms
- TRT Terms: Greenhouse gases; Machine learning; Product development
- Subject Areas: Highways; Vehicles and Equipment;
Filing Info
- Accession Number: 01828996
- Record Type: Publication
- Source Agency: SAE International
- Report/Paper Numbers: 2021-26-0045
- Files: TRIS, SAE
- Created Date: Dec 9 2021 10:38AM