CiDi–Zoomlion hybrid autonomous trucks in Xinjiang: deployment lessons for mine planners
Reviewed by Tom Sullivan

First reported on International Mining – News
30 Second Briefing
A fleet of 40 Zoomlion ZT160HEV hybrid haul trucks fitted with CiDi’s intelligent autonomous haulage system has been delivered to a large coal mine in Xinjiang, taking the site’s total unmanned truck count to more than 120 units. The ZT160HEV platform combines diesel-electric hybrid power with drive‑by‑wire controls, enabling CiDi’s system to manage autonomous haul cycles, navigation and collision avoidance in complex pit conditions. For mine operators, the scale of deployment signals growing confidence in mixed hybrid–autonomous fleets for high‑volume coal haulage in remote regions.
Technical Brief
- Hybrid ZT160HEV trucks use diesel-electric powertrain integrated with full drive-by-wire control architecture.
- CiDi’s intelligent driving stack manages unmanned haulage using on-board perception, planning and control modules.
- Large-batch delivery of 40 units indicates production-level standardisation of sensors, control units and interfaces.
- For other high-volume mining operations, the project evidences maturing Chinese OEM–autonomy supplier collaboration models.
Our Take
CiDi’s move from deploying over 120 unmanned trucks at a coal operation in Xinjiang to pursuing a Hong Kong IPO suggests it is using large reference sites to underpin a capital-raising story focused on exportable autonomous haulage technology.
Within our Mining coverage, coal is more often associated with conventional fleet upgrades than with autonomy, so this Xinjiang deployment signals that Chinese coal operations are becoming a key proving ground for hybrid and driverless haulage at scale.
For Zoomlion, fielding a 40-unit hybrid fleet in Changsha-linked projects positions its ZT-series trucks as a domestic alternative to Western OEM autonomous-ready haulers, which could matter if Chinese operators or regulators later favour local supply chains.
Prepared by collating external sources, AI-assisted tools, and Geomechanics.io’s proprietary mining database, then reviewed for technical accuracy & edited by our geotechnical team.
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