EACON BEV fleet at Shougang: haulage, design and CO₂ lessons for mine planners
Reviewed by Tom Sullivan
First reported on International Mining – News
30 Second Briefing
Autonomous battery-electric haul trucks deployed by EACON at Shougang Group’s Shuichang Iron Ore Mine have completed a full year of operation, delivering quantified reductions in both unit haulage cost and diesel-related CO₂ emissions versus conventional diesel fleets. The system integrates autonomous driving, battery swapping and centralised dispatch to manage multiple BEV trucks on steep open-pit ramps and long-distance waste hauls. For mine planners and geotechnical teams, the shift to BEVs changes ramp ventilation assumptions, traffic patterns and braking heat loads, affecting haul road design and pit wall interaction.
Technical Brief
- Fleet is deployed at Shougang Group’s Shuichang Iron Ore Mine, a steep open-pit operation in China.
- System is integrated into Shougang’s wider vertically integrated steel–mining business, including overseas assets such as Peru.
- For other large iron ore pits, similar BEV–autonomy packages could materially alter haulage cost structures and emissions baselines.
Our Take
EACON’s battery-electric and autonomous work with Shougang Group at the Shuichang Iron Ore Mine sits alongside its retrofitted Komatsu HD1500 trials in Western Australia gold operations, suggesting the company is deliberately proving its fleet across both iron ore and gold in very different regulatory and climatic environments.
Within our 122 iron ore‑keyword pieces, most decarbonisation coverage has focused on processing and shipping; a BEV haulage deployment in China therefore marks a relatively early large‑scale move on mine‑site mobile emissions for this commodity class.
The China–Peru footprint for Shougang Group’s iron ore business means any successful BEV and autonomy package at Shuichang could be a template for export to its Peruvian operations, where local power mix and altitude will test how transferable the cost and emissions gains really are.
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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