In a significant advancement for autonomous driving and electric vehicle (EV) safety, researchers have proposed a multifunctional frequency modulated continuous wave (FMCW) LiDAR system that can simultaneously perform high-precision 3D imaging and multi-parameter sensing. This innovation addresses the critical need for integrated perception systems that can monitor both external environments and internal battery conditions, potentially reducing system complexity and cost while improving safety.
The work, published in Light: Science & Applications (DOI: 10.37188/lam.2026.102), was led by Professor Yongkang Dong from the National Key Laboratory of Laser Spatial Information, Harbin Institute of Technology, China, and his team. Traditional FMCW LiDAR provides accurate 3D imaging but lacks the ability to detect crucial parameters such as battery temperature, electrolyte density, or gas concentrations—factors essential for preventing thermal runaway in EV batteries. Currently, these functions require separate systems, leading to higher costs and integration challenges. The new multifunctional LiDAR overcomes this limitation by detecting echo signals from both free space and optical fiber, enabling 3D imaging and sensing of environmental temperature, gas concentrations, and liquid density simultaneously.
In their proof-of-concept experiments, the team successfully imaged a target at 30 meters with adjustable resolution ranging from 0.3 cm to 1.2 cm. They also measured the electrolyte density and temperature of a battery with accuracies of 3×10⁻⁵ g/mL and 0.5 °C, respectively. Furthermore, they detected concentrations of gases critical for monitoring thermal runaway—C2H2, CO2, and CH4—with detection limits of 0.07 ppm, 48 ppm, and 0.56 ppm, respectively.
The system operates by using the FMCW LiDAR principle for free-space imaging, while the optical fiber component functions as an optical frequency domain reflectometry (OFDR) sensor. This dual-mode operation allows the same demodulator to process both imaging and sensing data. The researchers explain that the distance to a target is calculated from the optical path difference between the collimator reflection peak and the target reflection peak. Meanwhile, reflection spectra from fiber Bragg gratings (FBG), Fabry-Perot (FP) cavities, and multi-pass cells (MPC) can be demodulated from their reflection peaks in the spatial domain via inverse Fourier transform.
This integrated approach could revolutionize EV design by merging the perception system and battery management system into a single unit, reducing complexity and cost. The ability to monitor multiple parameters in real-time offers a proactive safety mechanism against thermal runaway, a major concern for EV adoption. The technology also holds promise for aerospace applications, where compact and multifunctional sensing is vital.
The research was supported by the National Key Research and Development Program of China, the National Natural Science Foundation of China, and other funding bodies. As the demand for safer and more efficient autonomous vehicles grows, this multifunctional LiDAR represents a significant step toward comprehensive environmental and internal monitoring in a single device.
