An Optimization Algorithm for the Design of Battery Electric Fleets in Underground Mines

Abstract

Diesel-based vehicles are prevalent in underground mines all over the world; however, an electric revolution is beginning to unfold in the mining industry. Mines operating with diesel fleets require extensive ventilation due to toxic gas and heat emissions from diesel engines, and these ventilation costs increase as mines continue to dig deeper for depleting resources. Electric mining vehicles have zero emissions and emit less heat, meaning ventilation costs decrease. Furthermore, unlike diesel trucks, electric trucks can capture regenerative braking energy on downhill segments. Recent battery advancements have allowed electric mining trucks to become cost-competitive with their diesel counterparts when all system-level advantages are considered. However, there is presently a lack of knowledge in designing an optimal electric mining fleet and charging infrastructure, compared to the many decades of "best practices" available for diesel fleets. To fill this gap, this paper proposes an optimization algorithm for battery electric fleets in underground mines which provides insights into the design of electric fleets. This work identifies critical input design parameters, presents a battery optimization algorithm, and uses detailed vehicle models to simulate mining productivity and truck energy use. The main algorithm considers ventilation costs, charger infrastructure costs, fleet labor costs, truck energy costs, and truck capital costs (including battery lifetime) to obtain an overall cost-per-tonne output metric. The algorithm is tested on a variety of scenarios, and some key findings are that fast-charging with optimal battery sizes results in the lowest $/tonne and this result decreases as fast-charging levels increase to 450 kW.