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April 10, 2025 – A team of scientists from the Harbin Institute of Technology has unveiled a multifunctional frequency modulated continuous wave (FMCW) LiDAR that can simultaneously perform high-precision 3D imaging and measure multiple physical parameters—including temperature, gas concentrations, and liquid density. The breakthrough, published in Light: Science & Applications, addresses a critical gap in current electric vehicle (EV) and autonomous driving systems, where separate sensors for imaging and battery monitoring increase complexity, cost, and integration challenges.
Today’s autonomous vehicles rely on LiDAR for 3D mapping, but conventional FMCW systems cannot detect internal battery states or environmental parameters. Battery thermal runaway—a leading cause of EV fires—requires early warning through monitoring of temperature, electrolyte density, and characteristic gases like C₂H₂, CO₂, and CH₄. Until now, these functions demanded separate imaging and sensing subsystems, raising system complexity and integration hurdles. The new LiDAR, however, merges both capabilities into a single device.
The proposed system works by detecting echo signals from both free space and optical fiber. In free-space mode, it scans targets for 3D imaging; in fiber mode, it acts as an optical frequency domain reflectometry (OFDR) sensor. “The OFDR shares the same linear modulated light source and positioning principle as FMCW LiDAR, giving it high spatial resolution and a large dynamic range,” explained Professor Yongkang Dong, lead author of the study. “This allows us to measure strain, temperature, pressure, and gas concentration along an optical fiber.”
In proof-of-concept experiments, the team imaged a plastic plate bearing the “HIT” logo at a distance of 30 meters, with adjustable resolution from 0.3 cm to 1.2 cm. Simultaneously, they measured the electrolyte density and temperature of a battery cell with accuracies of 3×10⁻⁵ g/mL and 0.5 °C, respectively. For gas sensing, they achieved detection limits of 0.07 ppm for C₂H₂, 48 ppm for CO₂, and 0.56 ppm for CH₄—all critical for early thermal runaway detection.
“The distance to the target is calculated from the optical path difference between the collimator reflection peak and the target reflection peak,” the team noted. “The reflection spectra of FBG, FP, and MPC are demodulated from their spatial-domain peaks via inverse Fourier transform.” This enables a single demodulator to handle both imaging and multi-parameter sensing, slashing system complexity.
The integration of 3D imaging and environmental sensing into one LiDAR unit has significant implications for the U.S. electric vehicle market. According to the National Highway Traffic Safety Administration (NHTSA), battery fires account for a growing share of EV incidents, and automakers are under pressure to adopt advanced safety monitoring. “This technology could provide a unified solution for autonomous driving perception and battery management, potentially reducing costs and improving safety,” said Dr. Emily Carter, an independent EV safety consultant based in California. “If commercialized, it could become a standard component in next-generation EVs.”
The team’s work was supported by the National Key Research and Development Program of China, the National Natural Science Foundation, and the National Key Laboratory of Laser Spatial Information. While the prototype is still in the lab, researchers believe it holds “widespread application potential in the field of new energy vehicles and spacecraft.” The next step is to miniaturize the system and integrate it with existing automotive LiDAR platforms.
For more details, the full paper is available at DOI: 10.37188/lam.2026.102. Light: Science & Applications is a peer-reviewed open-access journal covering the full spectrum of optics and photonics research.
