A hierarchical optimization model for off-peak battery swapping scheduling of electric trucks in open-pit mines

This study addresses the queuing inefficiencies caused by synchronized battery-swapping demands for electric trucks in open-pit mines. Through Discrete Event Simulation (DES), we identified systemic bottlenecks stemming from this synchronization. To mitigate this, we propose a hierarchical off-peak battery-swapping scheduling framework comprising an inner-layer Mixed-Integer Linear Programming (MILP) and an outer-layer Bayesian Optimization (BO) mechanism. Validated through three large-scale case studies, the model achieved 65% and 80% reductions in queuing times for single and dual loading platform scenarios, respectively, with 5.2%–5.7% improvements in transport throughput. Notably, expanding battery-swapping stations to four achieved equivalent efficiency gains (667 trips) as the optimization strategy (665 trips), highlighting the cost-effectiveness of intelligent scheduling over infrastructure scaling. Furthermore, in the third case study, by increasing loading platforms to alleviate constraints from upstream processes, the optimized model boosts transportation trips by up to 10%, demonstrating its capability to eliminate battery-swapping bottlenecks and fully unlock the potential of energy replenishment workflows.