Quanergy Systems Inc. has announced that it has raised $90 million in Series B funding at a valuation of more than $1 billion. Sensata Technologies, Delphi Automotive, Samsung Ventures, Motus Ventures and GP Capital participated in the round. The investment brings the company’s total funds raised to approximately $150 million.

“Innovation in LiDAR technology represents one of the largest opportunities unfolding around the globe, and this infusion of funding will enable us to accelerate development, scale faster and expand our world-class engineering team,” says Louay Eldada, Quanergy CEO. “We are grateful for the strong support from so many investors who share our vision of creating intelligent sensing solutions that permeate through multiple industries, significantly improving safety and efficiency.”

Quanergy intends to use the investment and leverage its intellectual property to work with its partners in ramping up the production of its solid state LiDAR sensors. The sensors use standard semiconductor manufacturing processes and have no moving parts on a macro or micro scale, offering lower cost, higher reliability, smaller size and lower weight.

Quanergy will continue the global expansion of the company and scale its operations and infrastructure to meet the growing demand for autonomy in vehicles and other systems that can benefit from increased levels of automation to save lives, space, time, energy and costs.

The company is aggressively working to commercialize the sensors critical for advanced driver assistance systems (ADAS) and autonomous driving applications, and currently has pre-production contracts with multiple global customers for the solid state sensors.

About Quanergy Systems

Quanergy Systems Inc. was founded in 2012 and builds on decades of experience of its team in the areas of optics, photonics, optoelectronics, artificial intelligence software and control systems. Headquartered in Sunnyvale, Calif., in the heart of Silicon Valley, Quanergy offers the LiDAR sensors and software for real-time capture and processing of 3D mapping data and object detection, tracking, and classification. In transportation, the data is utilized to greatly improve the accuracy and reliability of on-board driver safety systems and enhance them with perception, scenario analysis and decision making capability for active control, as well as enable autonomous driving in the future.