Progress Rail Services Corporation

Sammy Akif

John William Brand

Sidarta Fornari Beltramin

Marcos Blanco Fernandes

Evan Paul Sevel

Joseph Maverick White

Konan Thomason

Frederick Gary Silcox, Jr

Colby Smith Bradley

Methods and systems implement an output interface of a display computing system to display a visualization of predictive output of an onboard computing system of a train. A display computing system obtains, from an onboard predictive control system of a train traveling along a rail from a departure to a destination, predicted speeds which the predictive control system uses to preemptively adjust control signals sent by an onboard train control system of the train. The display computing system provides a visualization of the predicted speeds obtained by the predictive control system.

Predictive control system visualization for automatic train operation

A system includes one or more processors and memory storing processor-executable instructions that cause the one or more processors to perform operations. The operations include generating a driving strategy for a traveling route of a train based on saved data in the system, the train comprising at least one diesel-electric locomotive (DEL) and at least one battery-electric locomotive (BEL); operating the train according to the driving strategy; receiving update data; revising the driving strategy based on the saved data and the update data including: determining an amount of energy for the train to traverse a segment of the traveling route based on the driving strategy and the update data, and determining a distribution of the amount of energy between the at least one DEL and the at least one BEL based on the driving strategy and the update data; and operating the train according to the revised driving strategy.

Optimizing a driving strategy for a hybrid diesel-electric locomotive and battery-electric locomotive consist

A system includes one or more processors and memory storing processor-executable instructions that cause the one or more processors to perform operations. The operations include generating a driving strategy for a traveling route of a train comprising at least one battery-electric locomotive (BEL) based on saved data in the system for optimizing one or more aspects for the train over the traveling route; operating the train according to the driving strategy; receiving update data associated with a current location of the train; revising the driving strategy based on the saved data and the update data for optimizing energy consumption efficiency for a segment of the traveling route by identifying active powered components of the train that are non-essential for traversing the segment and turning off power to one or more of the identified active powered components for a duration of traversing the segment.

Managing vehicle energy use based on future energy demand

A train control system uses sensory inputs related to operational parameters of a train for automatically scoring or classifying particular train driving strategies implemented by a machine learning model for a particular train operating on a predefined route or route segment. The train control system includes one or more predefined rules related to one or more of a first set of the operational parameters, wherein each of the rules defines a Boolean, true or false classification based on whether a particular train driving strategy results in one or more of the first set of operational parameters complying with the rule. One or more comparative key performance indicators are related to one or more of a second set of operational parameters, and are used to rank the particular train driving strategy for the predefined route or route segment relative to a different train driving strategy for the same or comparable route or route segment.

System and method for scoring train runs

A train control system uses artificial intelligence for maintaining synchronization between centralized and distributed train control models. A machine learning engine receives training data from a data acquisition hub, a first set of output control commands from a centralized virtual system modeling engine, and a second set of output control commands from a distributed virtual system modeling engine. The machine learning engine compares the first set of output control commands and the second set of output control commands, and trains a learning system using the training data to enable the machine learning engine to safely mitigate any difference between the first and second sets of output control commands using a learning function including at least one learning parameter.

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Northlake, TX, United States of America

Sammy Akif