Tata Elxsi Ltd, a global technology services provider for the automotive and transportation industry, has signed a Memorandum of Understanding (MoU) with the Indian Institute of Technology, Guwahati (IIT-G) to jointly work on developing and commercialising state-of-the-art solutions for the electric mobility market. This collaboration will bring together researchers and experts for advanced research in material science, digital twins, and deep artificial intelligence and machine learning techniques.
As per Manoj Raghavan, CEO and MD, Tata Elxsi, this collaboration will bring together the best minds from Tata Elxsi and IIT Guwahati to envisage and develop future-looking solutions for the fast-evolving space of electric mobility. The fault analysis solution is an excellent example of how industry-academia collaboration can bring together original thinking and application of the latest digital technologies to solve very specific industry needs from operators, OEMs and system suppliers in the transportation industry.
The collaboration between Tata Elxsi and IIT Guwahati will enable both collaborators to apply their research capabilities to real-world problems, such as advancing state-of-the-art predictive maintenance.
As per Prof. Parameswar K. Iyer, Officiating Director, IIT Guwahati, electric vehicles are being increasingly considered the solution to carbon emissions from the transportation sector, and there is an essential need to create more future-ready solutions in the EV automotive and transportation industry. The shared knowledge between IIT Guwahati researchers and Tata Elxsi team will help in building a research ecosystem in this field, and its commitment to further strengthen the partnership going forward will help in achieving the Government of India’s mission of making the country ‘Atma Nirbhar’.
One key area of work under this collaboration will be the digital analysis of electrical signature data for traction motors which underpins EV mobility across segments, including automotive and rail. The solution will provide deep insights for proactive fault prediction, maintenance schedule formulation, and design and manufacturing defects traceability.