System and method for modeling in-train forces with multiple locomotives interspersed evenly along train

A method of using independent virtual in-train forces modeling engines disposed onboard each of a plurality of locomotives that are spaced at approximately regular intervals along an entire length of a train in order to model segments of the train, each segment including one of the plurality of locomotives and associated non-powered rail cars connected to the one locomotive, and to coordinate accelerations and decelerations of each of the plurality of locomotives determined independently onboard each of the plurality of locomotives, the method comprising:

• assimilating, analyzing, and calibrating real-time information from:
• one or more of the plurality of locomotives,
• sensors acquiring real-time data on forces and displacements at draft gears and couplers interconnecting the plurality of locomotives with non-powered rail cars in between each of the plurality of locomotives, and
• determinations made by the independent virtual in-train forces modeling engines,
• operating the plurality of locomotives of the train collectively to minimize in-train forces without being dependent on command and control signals from a lead locomotive or central command,
• modeling a segment of the train including one of the plurality of locomotives and the non-powered rail cars connected to the one locomotive with each of the independent virtual in-train forces modeling engines using acquired real-time and historical operational and structural data as training data comprising characteristics of the one locomotive and using machine learning performed by a machine learning engine for controlling changes in speed of the one locomotive on which it is disposed,
• simulating in-train forces and train operational characteristics with each of the independent virtual in-train forces modeling engines using:
• physics-based equations,
• kinematic or dynamic modeling of behavior of the train or components of the train during operation when at least one of the plurality of locomotives is changing speed, and
• inputs derived from stored historical contextual data comprising characteristics of the components of the train and the track over which the train is traveling, and
• using an energy management system associated with each of the plurality of locomotives of the train, adjusting one or more of throttle requests, dynamic braking requests, and pneumatic braking requests for each of the plurality of locomotives based at least in part on real-time information from one or more of the plurality of locomotives, from the draft gears and couplers, and from determinations made by the independent virtual in-train forces modeling engine disposed onboard each of the plurality of locomotives.

A locomotive control system, comprising:

• a learning system configured to:
• receive real-time and historical operational and structural data for use as training data by one or more independent virtual in-train forces modeling engines from one or more systems or components of a train at a data acquisition hub communicatively connected to one or more of sensors and databases associated with one or more locomotives or other components of a train including a plurality of locomotives that are spaced at approximately regular intervals along an entire length of the train in order to model segments of the train, each segment including one of the plurality of locomotives and non-powered rail cars connected to the one locomotive, and coordinate accelerations and decelerations of each of the plurality of locomotives determined independently onboard each of the plurality of locomotives using at least one of the independent virtual in-train forces modeling engines disposed onboard each of the plurality of locomotives to minimize in-train forces without being dependent on command and control signals from a lead locomotive or central command;
• simulate, using each of the independent virtual in-train forces modeling engines, in-train forces and train operational characteristics using:
• physics-based equations,
• kinematic or dynamic modeling of behavior of the train or components of the train during operation when at least one of the plurality of locomotives is changing speed, and
• inputs derived from stored historical contextual data comprising characteristics of the components of the train and the track over which the train is traveling;
• store one or more independent virtual in-train forces models simulated by the independent virtual in-train forces modeling engines in a model database, wherein each of the one or more independent virtual in-train forces models includes a mapping between different combinations of the stored historical contextual data and corresponding simulated in-train forces and train operational characteristics that occur when the at least one of the plurality of locomotives in the train is changing speed;
• calculate, using a machine learning engine, relative weights to assign to each of different types of the stored historical contextual data used by each of the independent virtual in-train forces modeling engines and assign the relative weights to the stored historical contextual data;
• train the learning system with the machine learning engine using the weighted stored historical contextual data and the training data to determine a probability of each of the independent virtual in-train forces models providing an accurate representation of actual in-train forces and train operational characteristics that occur when one or more of the plurality of locomotives changes speed, and using a learning function including at least one learning parameter, wherein training the learning system includes:
• providing the weighted stored historical contextual data as an input to the learning function, the learning function being configured to use the at least one learning parameter to generate an output based on the input;
• causing the learning function to generate the output based on the input;
• comparing the output to the training data, wherein the training data includes data produced by sensors having captured actual information on in-train forces and train operational characteristics during the changes of speed of the one or more locomotives of the plurality of locomotives;
• comparing the determined probabilities of each of the virtual in-train forces models to a predetermined threshold probability level;
• and
• initiating adjustments to one or more of the relative weights assigned to each of the different types of the stored historical contextual data of each of the independent virtual in-train forces models to improve the determined probabilities of each of the independent virtual in-train forces models based on actual information on in-train forces and train operational characteristics acquired from a plurality of different trains operating under different conditions;
• and
• adjust one or more of throttle requests, dynamic braking requests, and pneumatic braking requests for each of the one or more of the plurality of locomotives of the train that is changing speed using an energy management system associated with the one or more locomotives of the train based at least in part on one of the virtual in-train forces models with the highest probability of providing an accurate representation of actual in-train forces and train operational characteristics and with one or more of the simulated in-train forces and train operational characteristics falling within a predetermined acceptable range of values.