Tonly hybrid 91 t truck fleet in Brazil: haulage performance insights for mine engineers
Reviewed by Tom Sullivan
First reported on International Mining – News
30 Second Briefing
Tonly Heavy Industries is deploying a fleet of hybrid 91 t class wide-body mining trucks to a contractor’s R&D operations in Brazil, extending its off-highway portfolio that already exceeds 70,000 units in service worldwide. The company reports more than 8,000 units delivered to overseas markets and claims that roughly half of all new-energy mining transport units globally are its vehicles. For mine operators, the Brazilian deployment will provide real-world data on fuel–electric performance, haul profiles and maintenance behaviour in a high-production environment.
Technical Brief
- Hybrid trucks are 91 t class wide-body dumpers, targeting high-volume, short-haul mine profiles.
- Fleet is being deployed to a contractor’s dedicated R&D operation rather than a production client site.
- Brazilian testwork will focus on fuel–electric interaction under local ramp gradients and ambient temperatures.
- Data collection will include duty cycles, regenerative braking utilisation and battery–engine load sharing behaviour.
- Maintenance records from the R&D fleet are expected to benchmark component life versus conventional mechanical trucks.
- Results will inform haul road design optimisation, including curvature, gradients and surface condition for hybrids.
- Contractor R&D setting allows controlled A/B comparison of hybrid versus diesel fleets on identical haul routes.
- Outcomes are likely to influence future truck selection criteria for mining projects in similar climates.
Our Take
Tonly Heavy Industries’ deployment of a 91 t hybrid fleet in Brazil extends the company’s recent push from China into export markets, following its framework agreement to supply 800 autonomous trucks to Xinjiang Hanxiang, and signals that its ‘new-energy’ haulage platform is now being proven in multiple regulatory and operating environments.
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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