The paper proposes to apply an algorithm for predicting the minimum level of the state of charge (SoC) of stationary supercapacitor energy storage system operating in a DC traction substation, and for changing it over time. This is done to insure maximum energy recovery for trains while braking. The model of a supercapacitor energy storage system, its algorithms of operation and prediction of the minimum state of charge are described in detail; the main formulae, graphs and results of simulation are also provided. It is proposed to divide the SoC curve into equal periods of time during which the minimum states of charge remain constant. To predict the SoC level for the subsequent period, the learning algorithm based on the neural network could be used. Then, the minimum SoC could be based on two basic types of data: the first one is the time profile of the energy storage load during the previous period with the constant minimum SoC retained, while the second one relies on the trains’ locations and speed values in the previous period. It is proved that the use of variable minimum SoC ensures an increase of the energy volume recovered by approximately 10%. Optimum architecture and activation function of the neural network are also found.

In this paper, the influence of Mo addition on the structure and mechanical properties of the NiCoMnIn alloys have been studied. Series of polycrystalline NiCoMnIn alloys containing from 0 to 5 mas.% of Mo were produced by the arc melting technique. For the alloys containing Mo, two-phase microstructure was observed. Mo-rich precipitates were distributed randomly in the matrix. The relative volume fraction of the precipitates depends on the Mo content. The numbers of the Mo rich precipitates increases with the Mo contents. The structures of the phases were determined by the TEM. The mechanical properties of the alloys are strongly affected by Mo addition contents. Brittleness of the alloys increases with the Mo contents.