Electric Public Transit Optimization
Simulation and optimization platforms for electric bus fleet operations and charging infrastructure
Posts by Collection
portfolio
Sensor Systems and Data Acquisition
Development of low-cost sensor systems for environmental monitoring and electronic nose applications
City Traffic Simulation and Analysis
Data-driven approaches for calibrating and analyzing urban traffic simulation models
Vehicle-to-Building (V2B) Charging Optimization
Advanced optimization and decision-making frameworks for electric vehicle charging in building environments under uncertainty
publications
Low-Cost Air Pollution Monitoring Device Based on Air Quality Index
Published in 2020 IEEE Applied Signal Processing Conference (ASPCON), 2020
Development of a low-cost air pollution monitoring device utilizing Air Quality Index for environmental monitoring applications.
Recommended citation: R. Bajaj, R. Sen, A. Sengupta, A. Sen, S. Karmakar, S. Ghosh, V. Kumar, B. Tudu, N. Bandyopadhyay, and R. Bandyopadhyay, Low-Cost Air Pollution Monitoring Device Based on Air Quality Index, in 2020 IEEE Applied Signal Processing Conference (ASPCON), 2020.
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Development of an android platform for monitoring QCM sensor-array based Electronic Nose
Published in 2020 IEEE Applied Signal Processing Conference (ASPCON), 2020
An Android-based platform for monitoring QCM sensor-array electronic nose systems for portable odor detection applications.
Recommended citation: N. Debabhuti, A. Sengupta, R. Sen, A. Ghosh, S. Banerjee, P. Sharma, B. Tudu, N. Bandyopadhyay, and R. Bandyopadhyay, Development of an android platform for monitoring QCM sensor-array based Electronic Nose, in 2020 IEEE Applied Signal Processing Conference (ASPCON), 2020.
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Development of the data acquisition system and GUI for QCM sensor based system
Published in 2020 IEEE Calcutta Conference (CALCON), 2020
Development of a data acquisition system and graphical user interface for QCM (Quartz Crystal Microbalance) sensor-based systems.
Recommended citation: N. Debabhuti, A. Sengupta, P. Sharma, R. Sen, B. Tudu, and R. Bandyopadhyay, Development of the data acquisition system and GUI for QCM sensor based system, in 2020 IEEE Calcutta Conference (CALCON), 2020.
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Microcontroller Based Sensor-Array Data Acquisition System for Electronic Nose
Published in 2020 IEEE Calcutta Conference (CALCON), 2020
A microcontroller-based data acquisition system for electronic nose sensor arrays, enabling efficient data collection and processing.
Recommended citation: N. Debabhuti, R. Sen, A. Sengupta, S. Sengupta, P. Sharma, B. Tudu, N. Bandyopadhyay, and R. Bandyopadhyay, Microcontroller Based Sensor-Array Data Acquisition System for Electronic Nose, in 2020 IEEE Calcutta Conference (CALCON), 2020.
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E-Transit-Bench: Simulation Platform for Analyzing Electric Public Transit Bus Fleet Operations
Published in ACM International Conference on Future Energy Systems (e-Energy), 2022
A comprehensive transportation-grid co-simulation platform analyzing the spatiotemporal interaction between electric bus transit operations and power distribution grid, enabling holistic fleet optimization.
Impact: First platform to jointly simulate electric bus fleets with power grid constraints; adopted by academic partners for resilience studies and cited in subsequent transit electrification research.
Recommended citation: R. Sen, A. K. Bharati, S. Khaleghian, M. Ghosal, M. Wilbur, T. Tran, P. Pugliese, M. Sartipi, H. Neema, and A. Dubey, E-Transit-Bench: Simulation Platform for Analyzing Electric Public Transit Bus Fleet Operations, in Proceedings of the Thirteenth ACM International Conference on Future Energy Systems, New York, NY, USA, 2022, pp. 532–541.
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BTE-Sim: Fast simulation environment for public transportation
Published in 2022 IEEE International Conference on Big Data, 2022
Public transit is vital for cities but often stagnates due to static design. BTE-Sim, a fast, multi-layered transit simulation, analyzes population demand, traffic, and road networks to optimize routes, evaluate changes, and improve efficiency with low computation time.
Impact: Achieved dramatically lower computation time than existing multi-agent simulations, enabling rapid iteration on transit route design for Chattanooga and Nashville.
Recommended citation: R. Sen, T. Tran, S. Khaleghian, M. Sartipi, H. Neema, and A. Dubey, BTE-Sim: Fast simulation environment for public transportation, 2022 IEEE International Conference on Big Data, 2022.
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Calibrating real-world city traffic simulation model using vehicle speed data
Published in 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), 2023
Developed data-driven calibration methods for city-scale traffic simulation models using real-world vehicle speed data, significantly improving accuracy and reliability of traffic predictions for urban planning.
Impact: Improved simulation fidelity for Nashville and Chattanooga city models, enabling more reliable infrastructure and transportation policy evaluation before real-world deployment.
Recommended citation: S. Khaleghian, H. Neema, M. Sartipi, T. Tran, R. Sen, and A. Dubey, Calibrating real-world city traffic simulation model using vehicle speed data, in 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), 2023.
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OPTIMUS: Discrete Event Simulator for Vehicle-to-Building Charging Optimization
Published in 2024 IEEE International Conference on Smart Computing (SMARTCOMP), 2024
A comprehensive discrete event simulator for optimizing vehicle-to-building charging operations, enabling detailed analysis of charging strategies and their impact on building energy systems.
Impact: Provides a validated digital twin for stress-testing V2B demand-response strategies under edge-case stochastic inputs; used internally by Nissan and academic partners for resilience benchmarking.
Recommended citation: J. P. Talusan, R. Sen, A. K. Ava Pettet, Y. Suzue, L. Pedersen, A. Mukhopadhyay, and A. Dubey, OPTIMUS: Discrete Event Simulator for Vehicle-to-Building Charging Optimization, in 2024 IEEE International Conference on Smart Computing (SMARTCOMP), 2024.
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Grid-Aware Charging and Operational Optimization for Mixed-Fleet Public Transit
Published in 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), 2024
This study presents a robust hierarchical MILP model that optimizes charging and trip assignments for mixed bus fleets (electric and internal combustion engine buses), cutting costs and enhancing sustainability using real-world data from Chattanooga, Tennessee.
Impact: Reduced energy costs by 14% and GHG emissions by 10 tons on the Chattanooga (CARTA) transit system — directly informing the city’s electric fleet transition strategy.
Recommended citation: R. Sen, A. Sivagnanam, A. Laszka, A. Mukhopadhyay, and A. Dubey, Grid-Aware Charging and Operational Optimization for Mixed-Fleet Public Transit, in 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), 2024.
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Reinforcement Learning-based Approach for Vehicle-to-Building Charging with Heterogeneous Agents and Long Term Rewards
Published in 24th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2025), 2025
[Best Paper Award Finalist] Introduces an RL framework combining DDPG and MILP-guided policy shaping to balance feasibility and long-term cost reduction, validated with field-scale building data (≥ 12% compared to real world costs), in collaboration with the V2X team at Nissan Motor Corp.
Impact: Demonstrated ≥12% cost reduction over real-world baselines; recognized as a Best Paper Award Finalist (Top 5% of AAMAS 2025 submissions).
Recommended citation: F. Liu, R. Sen, J. P. Talusan, A. Pettet, A. Kandel, Y. Suzue, A. Mukhopadhyay, and A. Dubey, Reinforcement Learning-based Approach for Vehicle-to-Building Charging with Heterogeneous Agents and Long Term Rewards, 2025.
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Online Decision-Making Under Uncertainty for Vehicle-to-Building Systems
Published in 16th ACM/IEEE International Conference on Cyber-Physical Systems(ICCPS 2025), 2025
Vehicle-to-Building (V2B) systems optimize EV charging and discharging to cut costs, but complexity arises from pricing, planning, and user constraints; we model it as an MDP and use online search with heuristics, achieving state-of-the-art results in real-world tests using data from an EV manufacturer. Developed in collaboration with the V2X team at Nissan Advanced Technical Center - Silicon Valley.
Impact: Achieved state-of-the-art cost performance on real building data from Nissan, outperforming RL and rule-based baselines in both cost reduction and robustness to uncertainty.
Recommended citation: R. Sen, Y. Zhang, F. Liu, J. P. Talusan, A. Pettet, Y. Suzue, A. Mukhopadhyay, and A. Dubey, Online Decision-Making Under Uncertainty for Vehicle-to-Building Systems, 2025.
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MoveOD: Synthesizing Origin-Destination Commute Distribution from US Census Data
Accepted to 17th ACM/IEEE International Conference on Cyber-Physical Systems(ICCPS 2026), 2026
[2nd Best Poster at INFORMS 2025 Annual Meeting] A novel framework for synthesizing realistic origin-destination commute distribution patterns from US Census data, enabling data-driven transportation planning and traffic simulation with real-world population movement patterns.
Impact: Provides city planners with a free, open-source tool to generate calibrated mobility demand data without expensive travel surveys.
Recommended citation: R. Sen, A. Dubey, A. Mukhopadhyay, S. Samaranayake, and A. Laszka, MoveOD: Synthesizing Origin-Destination Commute Distribution from US Census Data, 2025.
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CONSENT: A Negotiation Framework for Leveraging User Flexibility in Vehicle-to-Building Charging under Uncertainty
Accepted to 25th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2026), 2026
Developed a strategy-proof, MPC-driven negotiation framework (with real user survey) that leverages EV user flexibility to reduce building energy costs (by 6%) and improve user charging affordability (by 22%) under real-world uncertainty, using data and support from Nissan Motor Corp.
Impact: Deployed and validated with real Nissan data; the first V2B negotiation mechanism proven both strategy-proof and practically effective at scale.
Recommended citation: R. Sen, F. Liu, J. P. Talusan, A. Pettet, Y. Suzue, M. Bailey, A. Mukhopadhyay, and A. Dubey, CONSENT: A Negotiation Framework for Leveraging User Flexibility in Vehicle-to-Building Charging under Uncertainty, 2026.
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P-V2B: A Neuro-Symbolic Framework for Leveraging User Persistence in Vehicle-to-Building Charging
Accepted to 17th ACM/IEEE International Conference on Cyber-Physical Systems, 2026
P-V2B is a neuro-symbolic control framework that learns how EV users return to a building each day and uses that persistence to plan charging across multiple days. By combining Monte Carlo MPC with a learned long-horizon value function, it turns routine commuting patterns into a strategic energy buffer that reduces monthly demand peaks and lowers building energy costs while meeting every driver’s required charge.
Impact: Reduces monthly peak demand charges by 3.5% vs. online baselines with guaranteed zero charge-level violations under severe load noise.
Recommended citation: R. Sen, F. Liu, J. P. Talusan, A. Pettet, Y. Suzue, A. Mukhopadhyay, and A. Dubey, P-V2B: A Neuro-Symbolic Framework for Leveraging User Persistence in Vehicle-to-Building Charging, 17th ACM/IEEE International Conference on Cyber-Physical Systems, 2026.
talks
Understanding and Building RC Drones
Published:
A hands-on workshop covering the fundamentals of RC drone design and construction, including aerodynamics, electronic speed controllers (ESCs), flight controllers, and radio communication. Covered component selection, frame assembly, and flight tuning for custom-built quadcopters.
Introduction to Python Programming
Published:
An introductory tutorial on Python programming for engineering students, covering core concepts such as data types, control flow, functions, and libraries for scientific computing (NumPy, Matplotlib). Designed to bridge the gap between theoretical concepts and practical implementation.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.
Embedded Systems Lab — Teaching Assistant
Undergraduate Lab, Vanderbilt University, Department of Electrical and Computer Engineering, 2021
Served as a Graduate Teaching Assistant for the Embedded Systems Lab course during the Fall 2021 semester. Instructed undergraduates in C++ programming fundamentals, guiding them through a progressive lab curriculum that started with core language concepts and culminated in building logic-based, code-controlled cars using microcontrollers.