Venkata Rajesh Chundru

Venkata Rajesh Chundru, Ph.D.

Senior Research Engineer

Southwest Research Institute, San Antonio, TX

Leading research at the intersection of energy systems, machine learning, and control architectures. Specializing in battery energy storage systems, fuel cell technologies, and ultra-low emissions control with a focus on translating theoretical advances into real-world applications.

Research Areas Google Scholar LinkedIn
162+ Citations
27+ Publications
5 Patents
h-index: 8

About

Bridging theoretical research and practical innovation

I am a Senior Research Engineer at Southwest Research Institute (SwRI) with a Ph.D. in Mechanical Engineering from Michigan Technological University . My research bridges chemical kinetics, physics-based modeling, and machine learning to solve complex energy challenges.

Currently, my research interests include lithium-ion battery modeling for grid storage applications, fuel cell control systems for hydrogen vehicles, and advanced aftertreatment systems for ultra-low NOx emissions. My recent research focused on automated calibration using machine learning has reduced calibration time from 6 weeks to just 2 weeks for ultra low NOx model based control system.

With over 12 years of experience, I have contributed to numerous industry projects with partners including TEPCO, ENGIE, CEZ, Isuzu, Cummins, and Johnson Matthey. My work has resulted in 5 patents, 21+ publications, and significant advancements in sustainable energy and emissions control technologies.

Beyond research, I am passionate about astrophotography and capturing celestial phenomena. This creative pursuit inspires innovative thinking - as I believe, "No idea is too unrealistic. If it is obvious, then it would have been out there already."

Core Expertise

  • Battery Energy Storage Systems
  • Fuel Cell Control Systems
  • Machine Learning & AI
  • Chemical Kinetics Modeling
  • Ultra-Low NOx Technologies
  • Physics-Informed Neural Networks
  • Grid Optimization
  • Hydrogen Technologies

Research Areas

Advancing the frontiers of energy and emissions technology

Battery Energy Storage Systems

Developing physics-based lithium-ion battery models for grid storage applications. Created digital twins for SOC/SOH prediction and dendrite detection algorithms to prevent thermal runaway in NMC 811 chemistry batteries.

Grid Storage Digital Twin Safety Monitoring

Fuel Cell Control Systems

Leading development of supervisory control systems for Toyota Mirai fuel cells. Focus on humidity and temperature management to optimize performance and prevent degradation in heavy-duty vehicle applications.

PEM Fuel Cells Hydrogen Economy Heavy-Duty Vehicles

Ultra-Low NOx Emissions

Pioneering machine learning approaches for SCR system calibration, achieving EPA 2027 standards. Developed predictive urea dosing controls and aging models for diesel and hydrogen aftertreatment systems.

SCR Systems ML Calibration EPA Compliance

Machine Learning & AI

Applying physics-informed neural networks, LLM fine-tuning with LoRA and RAG techniques, and transformer models for control applications. Automated calibration reduces testing time from weeks to hours.

PINNs Deep Learning LLMs

Hydrogen Technologies

Developing novel catalysts and control systems for hydrogen engine aftertreatment. Part of industry led projects advancing hydrogen combustion technologies for zero-emission transportation.

H2 ICE Zero Emission Catalysis

Grid Optimization

Colabrated with partners including TEPCO, ENGIE, CEZ in the SwRI Grid Joint Industry Project. Developing control algorithms for mixed-service grid operations and battery performance monitoring.

Smart Grid Energy Management BESS Control

Publications & Patents

Contributing to the advancement of scientific knowledge

5 Patents
8+ Journal Papers
10+ Conference Papers
27 Peer Reviews

Machine Learning Modelling for Fuel Cell-Battery Hybrid Power System Dynamics

Legala, A., Kubesh, M., Chundru, V.R., Conway, G., Li, X.

Energy and AI, 2024

Development of a Kalman filter estimator for simulation and control of NOx and PM

Chundru, V.R., Parker, G.G., Johnson, J.H.

International Journal of Engine Research, 2021

Impact of Second NH3 Storage Site on SCR NOx Conversion

Chundru, V., Desai, C., Kadam, V., Vernham, B. et al.

SAE Int. J. Adv. & Curr. Prac. in Mobility, 2024

Digital Twin of Battery Energy Storage System Using Physics-Informed State of Health

Chundru, V.R., Deshpande, S.R., Sarlashkar, J.V., Gankov, S.A., Hotz, S.

US Patent Application 18/750,117 (2024)

Systems and methods for reduced nitrous oxide emissions using predictive urea dosing control

Chundru, V.R., Rangarajan, S., Sarlashkar, J.V., Hotz, S.

US Patent No. US20230358157A1 (2023)

Aftertreatment systems and methods for treatment of exhaust gas from diesel engine

Johnson, J.H., Parker, G.G., Chundru, V.R.

US Patent No. 10,690,033 (2020)

Automated Calibration of Heavy-Duty Low NOx Aftertreatment System Controls using Deep Learning

Chundru, V., Deshpande, S.R., Sharp, C., Gankov, S.

SAE Technical Paper 2025-01-0402, 2025

Supervisory Control System Development for a Toyota Mirai FCEV

Chundru, V.R., Kubesh, M., Legala, A.

SAE Technical Paper 2025-01-8586, 2025

System Simulation of H2 ICE Aftertreatment System

Chundru, V.R., Sharp, C., Rahman, M.M., Balakrishnan, A.

SAE Technical Paper 2024-01-2625, 2024

View Full Publication List on Google Scholar

Technical Skills

Comprehensive expertise across multiple domains

Programming Languages

Python MATLAB C/C++ Fortran Java R

Machine Learning

PyTorch PINNs Deep Learning LSTM Transformers LoRA/RAG

Simulation Tools

Simulink GT-Suite ANSYS COMSOL PTV VISSIM IPG CarMaker

Control Systems

ETAS INCA OpenECU dSPACE Kalman Filters MPC State Estimation

Contact

Email

venkata.chundru@swri.org

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Location

San Antonio, TX 78245