engleski

Adaptive State of Charge Control for a Battery Hybrid Locomotive Based on Track Condition Provided by a Remote Wireless Sensor Network

Weather conditions have a significant impact on railroad traffic operation and maintenance, especially under icy conditions, and in the cases of substantial snowfall, rain, wind, fog, and temperature extremes. These can affect the train dynamics by increasing movement resistances while also lowering track to wheels adhesion, which is necessary for favorable traction. In the former case, fuel consumption may significantly increase, whereas in the latter case the risk of train derailment increases. The fifth generation wireless networks, which have been introduced lately in mobile broadband market, can also support communication for different systems, such as industrial automation, autonomous vehicles or Internet of Things (IoT). Therefore, these networks can be used together with distributed sensors arrays and appropriate databases in order to provide more accurate estimation of track conditions. Having this in mind, this paper proposes the use of track condition data derived from the remote wireless sensor network for the purpose of adaptive battery state of charge control within a hybrid locomotive, and the proposed concept has been simulated over a mountain railway route driving scenario. The obtained simulation results are used to gain insights about the possible benefits of usage of the proposed remote wireless sensor network in terms of finding optimal control strategy for the freight train, while simultaneously increasing the transportation safety and predicting possible delays due to changed conditions at the route.

Weather conditions ; wireless networks ; hybrid electric locomotive ; mountain rail route ; modeling ; emissions ; advanced transportation technologies

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