Abstract
In order to recover and utilize the potential energy of mining trucks efficiently, this paper proposes a nested optimization method of a novel energy storage system. By analyzing the multi-objective optimization problem of the oil-circulating hydro-pneumatic energy storage system, a nested optimization method based on the advanced adaptive Metamodel-based global optimization algorithm is carried out. Research shows that this method only requires a short time to solve the complex nonlinear hybrid optimization problem and achieves better results. The optimized energy storage system has higher system efficiency, energy density, and volume utilization rate, thus obtaining a smaller system volume and weight. Verified by the bench experiment of its powertrain, the hydro-pneumatic hybrid mining truck with the optimized energy storage system significantly reduces its fuel consumption and CO2 emission. Thus, it lays the foundation for the practical application of hydro-pneumatic hybrid mining trucks.
Highlights
The fuel consumption of hybrid mining truck Umd is the optimization objective, which can be expressed in Equation (6); the required operation conditions of the powertrain are taken as constraints, namely the flow limit of lifting pump and hydraulic motor qmax, the pressure limit of the accumulators and nitrogen tanks pmax, the temperature limit of the hydraulic system Tmax, and the power limit of the cooling system Pratecool, as shown in Equation (7); By superimposing the fuel consumption of each time step, the total fuel consumption in the whole working cycle period t is obtained [31], in which the scheme with the minimum value is taken as the optimal energy management strategy (EMS)
Optimization Results To solve the complex black-box problem of the nested optimization of oil-circulating hydro-pneumatic energy storage system (OHESS), this section adopts Genetic algorithm (GA) and adaptive Metamodel-based global optimization (AMGO) algorithms to search for the global optimal solution
In order to recover and utilize the potential energy of mining trucks efficiently, this paper proposes a nested optimization method of its novel energy storage system
Summary
A nested optimization method of OHESS based on the advanced AMGO algorithm is presented for the potential energy recovery of hybrid mining trucks. The powertrain configuration of the hydro-pneumatic hybrid electric-drive mining truck with OHESS is carried out and analyzed, as a way to recover the potential energy of mining trucks when going downhill; 2. Compared with the multi-objective comprehensive scheme, better optimization results of OHESS are obtained by the nested method, which could significantly reduce the fuel consumption and the corresponding CO2 emission of mining trucks. The engine provides the main required traction power, and OHESS supplies the rest
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