Projects
What We Offer
The Sustainable Mobility and Advanced Research on Transportation (SMART) Lab is at the forefront of developing cutting-edge solutions for the future of transportation. We specialize in innovating electric and hybrid powertrains, zero-emission truck designs, and autonomous vehicle systems while enhancing battery durability and efficiency through state-of-the-art battery SOx estimation. By leveraging technologies such as reinforcement learning, connected vehicle networks, and next-generation energy management systems, SMART Lab offers advanced solutions for intelligent transportation systems. Our work in computer-vision-based traffic estimation and integrated thermal management ensures that the mobility solutions we design are sustainable and optimized for real-world performance. Our research and innovations are poised to accelerate the adoption of zero-emission technologies, improve fleet operations, and meet global demands for greener, smarter transportation.
Research Areas Include:
Electric & hybrid vehicles
Autonomous vehicles
Electrified powertrains design
Battery SOX estimation
Hardware-in-the-loop
Zero-emission truck
Off-highway vehicle electrification
Reinforcement learning-based control
Connected vehicles
Computer-vision-based traffic estimation
Energy storage systems
Energy management systems
Advanced driver-assistance systems (ADAS)
Artificial intelligence & machine learning
Integrated thermal management
Modelling, simulations, and control
List of Research Projects
Energy and Thermal Management of Electric Vehicles
The design of integrated thermal and energy management systems for electric vehicles (EVs) focuses on optimizing the interaction between the battery, powertrain, and HVAC (heating, ventilation, and air conditioning) systems. This project aims to reduce energy consumption, improve energy efficiency and battery durability, and extend vehicle range by integrating thermal and energy management.
PI: Atriya Biswas
Timeline: 2024 - 2027
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Electric Vehicle Battery SOH estimation in Real-world
A Lithium-Ion battery's state of health (SOH) measures its aging and is key to extending its lifespan, reliability, and safety. Unlike state of charge (SOC), SOH is harder to assess due to its dependence on the aging process. Real-time SOH estimation at the pack level is critical for electric vehicles (EVs). This project aims to develop a robust method for predicting EV battery SOH using advanced machine learning and electrochemical models, leveraging real-world data and lab-based aging tests.
PI: Atriya Biswas
Timeline: 2024 - 2027
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Computer Vision-Based Traffic Prediction and Predictive Energy Optimization for Connected Vehicles
The project focuses on developing a computer vision-based system that uses real-time video processing and object detection to predict traffic conditions and optimize energy consumption in electric vehicles (EVs) through V2X connectivity. By forecasting traffic and adjusting vehicle inputs, it enhances EV efficiency, reduces emissions, and improves urban mobility, supporting sustainability and smarter transportation.
PI: Atriya Biswas
Timeline: 2024 - 2027
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Mission to Enable Zero-emission Trucking with Eco-driving and Fuel cell Powertrain
This project focuses on reducing transportation-related externalities in freight trucks on Indian highways by exploring eco-driving strategies and fuel cell-based powertrain design. The project will investigate the potential of eco-driving—a strategy that improves fuel efficiency by maintaining steady speeds, minimizing acceleration and braking, and reducing idling time. Additionally, the design of fuel cell-based powertrains will be explored to further cut emissions and improve long-term energy sustainability in freight transport.
PI: Anmol Pahwa, Co-PI: Atriya Biswas
Timeline: 2025 - 2027
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Application of Reinforcement Learning in Powertrain Electrification and Intelligent Transportation
This project aims to leverage reinforcement learning (RL) techniques to optimize the performance of electric and hybrid powertrains, as well as enhance intelligent transportation systems (ITS). RL algorithms will be used to develop real-time decision-making models that improve energy efficiency, power management, and vehicle control in electric vehicles (EVs). Additionally, the project will focus on optimizing traffic flow, vehicle routing, and eco-driving strategies within connected and autonomous vehicle systems.
PI: Atriya Biswas
Timeline: 2025 - 2027
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Off-highway Vehicle Electrification
The "Off-Highway Vehicle Electrification" project focuses on developing and implementing electric powertrains for off-highway vehicles such as construction equipment, agricultural machinery, and mining vehicles. These vehicles, traditionally reliant on diesel engines, contribute significantly to fuel consumption and emissions. The project aims to design and optimize electric and hybrid powertrain solutions that enhance the energy efficiency, reduce emissions, and improve the operational performance of these vehicles.
PI: Atriya Biswas
Timeline: 2025 - 2027
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