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  • Greater China

Hidden Hill leads $137m round for China's CS Smart

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  • Tim Burroughs
  • 31 October 2023
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Hidden Hill Capital, a private equity firm backed by warehouse operator turned logistics and infrastructure investment manager GLP, has led a CNY 1bn (USD 137m) Series B round for China-based logistics vehicle manufacturer CS Smart.

SZ Capital, Ganfeng Lithium, and Zhongxiang Jusheng also took part in the round, according to a statement. The proceeds will go towards product R&D, market development, and supply chain upgrades aimed at producing globally competitive pure electric smart logistics vehicles.

Founded in 2012, CS Smart is a new energy vehicle (NEV) technology specialist. It started collaborating with FAW Jiefang in 2019 – coinciding with the launch of its second-generation vehicles – and raised Series A funding later the same year.

The company has released a range of small to mid-size delivery vehicles that are used in more than 170 cities across China and shipped to over 10 countries. They are widely used in express delivery, supermarket and fresh food distribution, healthcare, and municipal services. CS Smart claims to integrate data, computing power, and algorithms into specific logistics application scenarios.

In late 2021, the company released its first autonomous “robot vehicle.” The vehicle – which features level-four self-driving technology, where it is capable of full autonomy in certain environments, but the option of human override remains – has been used in pilot programmes by ZTO Express.

Zhonglang Dong, a managing partner at Hidden Hill, noted that as commercial NEV penetration rises, there is evidence of homogenous competition in China. He believes that vehicles focused on specific usage scenarios have the greatest growth potential.

“As express delivery volume continues to rise and traffic safety supervision is further improved, the last-mile distribution market will become one of the important application scenarios for new energy logistics vehicles,” Dong said, adding that operational and cost efficiency, as well as small and micro models, are likely to win out.

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