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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.

Combustion engine driven vehicles operating in a connected and autonomous vehicle (CAVs) environment, the engine drive cycles are run in a regulated manner. This is due to synchronized movement of vehicles operating in connected environment. Hence, developing intelligent and faster control of airpath variable with smooth transient tracking, helps to achieve a synchronized drive cycle. With regards to this author discuss modeling of turbocharger. This is critical for airpath system variable calculation. Due to the hybridized nature of turbocharger models, predicting accurately the position of VTG without introduction of any sensing devices is key, as sensing device induces delay in action. Authors propose a model which improve the performance and capability of VTG position prediction. A neural network based supervised learning model is developed. This model is coupled with engine models which are in series application for performance evaluation.

Functional testing also referred to as the statistical testing or behavioral testing is today recognized as an industry standard for quality assessment of large-scale embedded systems. The measure of the quality is reliability of a system. The key part of functional testing is automatic production of test cases in accordance to a given operational profile of the tested system. This problem has been widely addressed in literature; however there is still the need for additional research efforts in this area. In this paper we present the original model-based working environment for production of functional test cases. The front-end of the environment is the generic modeling environment with our operational profile modeling paradigm registered to it. The back-end of the environment consists of operational profile model interpreter and a generic framework. The generic framework is the key component of the environment.

The paper presents a methodology to identify the most appropriate algorithm for the analysis of this nonlinear instability, and to devise a novel control technique for damping the instabilities and ensuring orderly operation of the system with an improved energy savings. The control is digital in nature and can easily be implemented using digital signal processors (DSPs). In addition to superiority in performance, fault diagnostic and failure are easier using the proposed control method.