DEBABRATA DEY, a second-year student of the Department of Green Science and Engineering, has won the ‘Best Paper Award’ award at the IEEE International Conference on Smart Power, Energy, Renewables, and Transportation (IEEE-SPERT) 2025.

He shared the following remarks upon receiving the award:
Receiving the ‘Best Paper Award’ at IEEE SPERT-2025 is a significant milestone in my professional and academic journey. I am deeply indebted to Prof. Masafumi Miyatake of Sophia University for his expertise and constant encouragement in developing the Pseudospectral Dynamic Programming framework for OESS-equipped trains. I am also grateful to the Ministry of Railways, Government of India, and the Japanese Ministry (MEXT) for supporting my research in the vital field of Green Railways. This award validates the importance of bridging advanced optimization techniques with practical railway operations. As an IRSEE officer, this recognition further strengthens my resolve to implement sustainable and resilient energy solutions within Indian Railways. I am committed to continuing this research to help achieve our national goals of carbon neutrality and world-class rail infrastructure.

• Name of Conference：IEEE International Conference on Smart Power, Energy, Renewables, and Transportation (IEEE-SPERT) 2025
• Title of Award：Best Paper Award
• Achievement Subject：A Novel Framework for Optimal Emergency Operation of Trains Equipped With OESS Using Pseudospectral Dynamic Programming
• Name of Award Recipient：DEBABRATA DEY
• Date of Award Received：18th January 2026
• Name of Instructor：Prof. Masafumi Miyatake (Faculty of Science and Technology)
• Site Related：IEEE-SPERT 2025 website

This research focuses on making trains more energy-efficient, especially those with on-board energy storage systems like batteries or fuel cells. The main challenge is that current methods for optimizing energy use are too complex and slow. This paper introduces a new approach that simplifies this problem, making calculations much faster. It considers important factors like battery charge levels, the non-linear behavior of batteries and fuel cells, and regenerative braking (which recovers energy during braking). By using this new method, the study shows that trains can save more energy and the optimization process is significantly quicker compared to traditional methods. Essentially, it’s about finding a smarter, faster way to manage train power to save energy.

The research proposes a Pseudospectral Dynamic Programming (PS-DP) framework for the emergency operation of trains equipped with On-Board Energy Storage Systems (OESS). It addresses the “curse of dimensionality” in conventional DP by discretizing non-convex state variables at Legendre–Gauss–Lobatto (LGL) points and using Gauss–Lobatto quadrature for energy minimization.
The study models high-fidelity battery (Li-ion), supercapacitor, and hybrid (Fuel Cell + Battery) systems. By transforming the problem into the pseudospectral domain, the method achieves 8.3x faster computation than traditional DP while maintaining 11% higher energy efficiency than standard SQP methods, enabling real-time optimal control during power outages.