Sejoon Lim
Papers
2
Total Citations
20
H-Index
2
About
Sejoon Lim is a researcher at the forefront of intelligent vehicle testing and control systems, with a focus on fuel economy optimization for hybrid and plug-in hybrid electric vehicles. His major contributions lie in developing robotic driver systems that automate vehicle testing, replacing human drivers to achieve precise, repeatable results. Lim’s most-cited work, a 2022 paper on deep reinforcement learning for dynamic PI gain auto-tuning, introduces an adaptive control method that enhances the performance of robotic drivers, earning 12 citations. His earlier 2021 study on the Simple Robotic Driver System (SimRoDS) uses fuzzy-PI control to test fuel efficiency, garnering 8 citations and demonstrating a practical, cost-effective solution for the automotive industry. These innovations directly address the growing demand for stringent fuel economy regulations by enabling accurate evaluation of advanced powertrains. Lim’s work bridges control theory and applied robotics, offering tools that help automakers verify efficiency gains in engines, motors, and transmissions. His research is notable for its real-world impact, providing a scalable approach to vehicle testing that supports the development of greener transportation technologies.
Research Focus
Key Achievements
Top Papers
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- 2