Mingqiang Pan

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About

Mingqiang Pan is a researcher whose work bridges intelligent robotics and precision engineering, with a particular focus on automated systems for electric vehicle (EV) infrastructure. His most impactful contribution addresses a critical bottleneck in EV adoption: the autonomous charging process. In his highly cited 2020 study (33 citations), Pan developed an automatic recognition and location system for EV charging ports in complex environments. By integrating image processing to determine charging port posture with robotic arm control for precise insertion, his system enables fully automated charging—a key step toward practical, hands-free EV refueling. Pan’s expertise, however, extends beyond robotics into advanced signal processing for machinery health monitoring. His foundational 2002 work (25 citations) systematically compared three major time-frequency analysis algorithms—windowed Fourier transform, Wigner-Ville distribution, and wavelet analysis—using synthetic signals. This rigorous benchmarking provided engineers with clear guidance on selecting the optimal technique for transient-based condition monitoring, a cornerstone of predictive maintenance. With a career spanning from foundational signal processing theory to applied robotic automation, Pan demonstrates a rare ability to move from analytical fundamentals to real-world engineering solutions. His work continues to influence both the reliability of industrial machinery and the future of electric vehicle infrastructure.