It’s known that the use of the automobile vehicles in a quarry for transporting the rock mass is characterized by a cyclic nature and the ability to overcome the slopes of up to 8-12% along the road as well as curves with the radii of at least 20-24 m. However, the disadvantage of this way of transportation of quarry loads is relatively high costs [1]. One of the ways to reduce costs is to implement the software-based operation of the mining dump truck while ensuring energy efficiency and safety. This way implies following the route “mine face – concentrator” when driving with the rock mass and the route “concentrator – mine face” when driving without a cargo along the specified trajectory and at a given driving speed using the information management systems in a quarry and on the board of the mining dump truck controlled by the driver as well as in an unmanned version in case of autonomous driving. The article describes the method to optimize the function “way – speed” and as a result the way to optimize the control of the traction electric motors in a mining dump truck during cyclic movement along the specified trajectory with a fixed time to pass the route in order to ensure energy efficiency and safety when using the method of dynamic programming developed by Richard Bellman.
The article deals with the problem of the characteristics selection for the electric motors of the drivetrains of the high-speed tracked vehicles with individually driven sprockets. The existing method of the tracked vehicle cornering dynamics analysis is not suitable for the analysis of the motion at the speeds near to the critical speeds in the skidding mode. The authors offer a method for improving the accuracy of the existing analytical dependencies by the use of neural network. The above-mentioned improvements provide estimation of the limit values of the required traction forces on the sides of the vehicle but they do not allow gathering statistical data on the electric motor operating conditions of short and long duration during the vehicle operation. The authors offer to solve this problem by means of a driving simulation complex. The complex provides simulation of the tracked vehicles driving along stochastic routes in real time mode and collection of the stochastic data on the electric motor loads.
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