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This paper presents a supervisory control strategy to intelligently split power within a hybrid energy storage system (HESS) of battery and supercapacitor (SC) in electric vehicles (EVs). The intelligent energy management strategy (IEMS) is based on linear parameter-varying model predictive control (LPV-MPC) with the main aim of mitigating battery degradation. The battery RC model is considered in the control-oriented model, where the increment of the battery current is selected as the control variable, resulting in a linear state-space model dependent on the HESS parameters, namely, the battery state-of-charge (SoC) and SC state-of-voltage (SoV). The cost function minimizes the power loss in the battery and squared error of the SoV across the prediction horizon. A new SoV control strategy is proposed based on the upcoming acceleration, providing opportunities for the efficient utilization of the SC and extending the battery lifespan. Constrained optimization is transformed into a quadratic programming problem, which can be easily solved in real time. The superiority of the proposed method in assessing battery degradation was verified by comparing different strategies using five evaluation factors under two drive cycles. Compared with the LPV-MPC, where SoV remained fixed, the proposed method demonstrates reductions of up to 18.82% in the battery current root-mean-square, 30.26% and 25.85% in the discharge and charge peak current, 9.71% in the ampere-hour throughput, 4.78% in the capacity loss, and 29.06% in the energy loss. To validate the real time performance of the proposed method, it is implemented in the real-time digital simulator (RTDS).

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.