sowmya payyavula

Emphasising System Efficiency and Drivability Requirements. The objective is to identify optimal configurations across weight classes, considering energy consumption, acceleration performance, and gradability. Vehicle requirements are derived from data research. A combination of analytical approach and experimental vehicle parameters are used to evaluate different architectures. Results guide the selection of optimal powertrain configuration and electric machine for in-depth vehicle dynamic analysis.

Modelling a Deep Reinforcement Learning Algorithm for Shifting Vehicle Operating Modes. Familiarised with different areas of e-mobility and developed a controller based on deep reinforcement learning to shift between different operating modes, balancing vehicle behaviour and minimizing energy consumption

1. Engineering Changes: Tool: Unigraphics •The scope of work includes: Incorporating the Engineering changes on the 3D models and drawings using NX. Generation of Change notes (ECN) and working through TCE. •Understanding customer requirements and managing all onshore technical activities.

1. Reverse Engineering: Tool: Solidworks • Reverse-engineered a cooling fan from a physical model. •Designed and analyzed around 15+ varieties of cooling fans using SolidWorks. •Created FAI reports and maintained Quality standards. 2. REACh Analysis & Data Base generation: Tool: Catia V5 •Reviewed around 1000+ engineering drawings. Identified all the materials, Coatings and In-process Solutions used. •Created 3D models of many assemblies and sub-assemblies.