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.