Dr. Kiran Bhaskar | Engineering | Best Researcher Award
Controls Engineer, Wabtec Corporation, United States
Dr. Kiran Bhaskar is an accomplished researcher and engineer specializing in battery systems and electrification. Holding a Ph.D. and M.S. in Mechanical Engineering from Pennsylvania State University (GPA 3.98/4), he has contributed significantly to the modeling, fault detection, and optimization of lithium-ion battery systems. Dr. Bhaskar has co-authored impactful publications in Applied Energy and IEEE Transactions on Transportation Electrification. As a Controls Engineer at Wabtec Corporation, he focuses on energy management for hybrid locomotives. With extensive technical expertise in MATLAB, Simulink, and Python, he excels in developing innovative solutions for sustainable energy and transportation systems. ππ
Publication Profile
π Academic Journey
Dr. Kiran Bhaskar has a distinguished academic background in mechanical engineering. He pursued his PhD and MS at The Pennsylvania State University, University Park, PA (2019β2024), achieving an impressive GPA of 3.98/4. Earlier, he graduated from the Indian Institute of Technology (IIT) Madras with a B.Tech (Hons) in Mechanical Engineering and a Minor in Operations Research (2013β2018), along with an M.Tech in Thermal Engineering, earning a GPA of 8.7/10. Additionally, he broadened his horizons through a semester abroad at Czech Technical University, Prague (2017). His academic excellence reflects his dedication to engineering research and innovation. ππ
π¬ Academic Research Experience
Dr. Kiran Bhaskar has extensive research experience in battery systems and energy optimization. At Pennsylvania State University, he developed models for parallel cell degradation, proposing a novel capacity-resistance matching strategy to enhance battery pack performance and minimize aging. His work includes fault detection through Lyapunov-based observers and real-time diagnosis schemes for internal short circuits. He also pioneered data-driven sensor reconstruction techniques, anomaly detection algorithms using PCA, and coupled SoC and SoH estimation for Li-ion batteries. Additionally, he optimized energy management in hybrid-electric locomotives and studied emissions reduction through injection parameter optimization during his tenure at IIT Madras. β‘ππ
π Industry Research Experience
Dr. Kiran Bhaskar has diverse industrial experience in energy management and automotive innovation. As a Controls Engineer at Wabtec Corporation, he developed advanced algorithms for battery pack health monitoring, including SoC, SoH, and anomaly detection techniques. During a summer internship there, he created Simulink models for fault detection and sensor signal reconstruction. At Rivigo Logistics, he optimized market truck utilization, reducing revenue leakage by 5%. Interning at TVS Motor Company, he automated SI engine calibration for BMW engines, while at Ashok Leyland, he modeled airflow estimation in SI engines using Simulink. His work spans energy, logistics, and automotive sectors. πππ
π Awards and Honors
Dr. Kiran Bhaskar has received numerous accolades for his academic excellence and research contributions. He was honored with the prestigious Thomas and June Beaver Award (2024) for outstanding industrially-sponsored research at Pennsylvania State University and recognized as an ASME Dynamic Systems and Control Division Rising Star at the 2023 MECC. Twice a finalist for the Energy Systems Best Paper Award (2024), he also secured runner-up positions in poster sessions at IndustryXchange (2022, 2023). Additionally, he earned the Prime Ministerβs Scholarship for academic excellence and ranked 1666th nationally (99.86 percentile) in the IIT-JEE Advanced Examination. ππποΈ
π Research Focus
Dr. Kiran Bhaskar specializes in battery technology and energy systems, with a focus on lithium-ion batteries. His research includes thermal anomaly detection π₯, state of charge and health estimation β‘, and short-circuit detection in battery packs. Dr. Bhaskar has developed innovative methods for fault detection π οΈ, sensor signal reconstruction π, and diagnosing heterogeneity-induced losses in parallel-connected cells. He also explores advanced techniques for anomaly diagnosis π¨ and health monitoring of battery systems. His contributions enhance battery efficiency and safety, driving progress in electrification and sustainable energy solutions. π±π§π
Publication Top Notes 
- Data-driven thermal anomaly detection in large battery packs π₯ β Cited by 21 (2023)
- State of Charge and State of Health estimation in large lithium-ion battery packs β‘ β Cited by 4 (2023)
- Detecting synthetic anomalies using median-based residuals in lithium-ion cell groups π β Cited by 4 (2022)
- Detection of engine knock using speed oscillations in a single-cylinder spark-ignition engine π β Cited by 3 (2019)
- Heterogeneity-induced power and capacity loss in parallel-connected cells π (2024)
- Short Circuit Estimation in Lithium-Ion Batteries Using Moving Horizon Estimation π§(2024)
- Post-Damage Short Circuit Detection in Lithium-ion Batteries π¨ (2024)
- Faulty sensor signal reconstruction in Li-ion battery packs π οΈ (2024)
- Anomaly diagnosis and health monitoring of lithium-ion battery packs π©Ί (2024)
