A system and method for automatically communicating an identification signal from a vehicle controller of a vehicle that may include receiving trip data from a dispatch controller based on the identification signal that is communicated. Software of the vehicle controller may be updated at a determined time based on the trip data that is received, and movement of the vehicle may be controlled based on movement allowance signals received by the vehicle controller and used by the software of the vehicle controller during the movement of the vehicle to determine whether the vehicle is permitted to enter into segments of one or more routes.

A method and a control device determine a position-related braking ability of a vehicle. A first vehicle determines at least one piece of position-related information of a route section, the at least one piece of position-related information of the route section being information relating to the braking ability on the route section. The first vehicle transmits the at least one piece of position-related information to a receiver, the receiver being at least one second vehicle, whereby the braking curves of at least one rail vehicle are adapted according to the situation, thus allowing safety on the route section and the utilization of the section to be improved.

A locomotive control system includes a locomotive having a traction motor and sensors. The traction motor provides tractive effort for propelling the locomotive and the sensors measure performance conditions of the locomotive. The system also includes a controller having one or more processors communicatively coupled with the traction motor. The controller selects one or more baseline conditions that designate operational conditions under which the locomotive is to operate, and monitors the performance conditions that are generated by the locomotive during operation of the locomotive according to the operational conditions designated by the one or more baseline conditions. The controller identifies a flashover condition or a plugging condition of the locomotive by comparing the performance conditions that are generated by the locomotive during operation of the locomotive with the one or more baseline conditions. The flashover condition or the plugging condition causing degradation of one or more components of the locomotive.

The present invention discloses an iterative learning control (ILC) method for a multi-particle vehicle platoon driving system, and relates to the field of ILC. The method includes: firstly, discretizing a multi-particle train dynamic equation using a finite difference method to obtain a partial recurrence equation, and then transforming the partial recurrence equation into a spatially interconnected system model; secondly, transforming the spatially interconnected system model into an equivalent one-dimensional dynamic model using a lifting technology, and in order to compensate input delay, designing an ILC law based on a state observer, and thirdly, transforming a controlled object into an equivalent discrete repetitive process according to the ILC law, and converting a controller combination problem into a linear matrix inequality based on stability analysis of the repetitive process. The method is simple and easy to implement, considers structure uncertainty of the system, and has a good control performance and robustness.

It makes it possible to track instrument information and to obtain an accurate history of the instrument information, even when a mounting position mounted with a railway vehicle instrument has been changed. Instrument information indicating an operation or a state of a railway vehicle instrument mounted to each mounting position included in a composition is collected. A serial number determined for each mounting position is updated. The instrument information is assigned with a temporary ID including position information specifying the each mounting position and a serial number. A combination of a new temporary ID and an old temporary ID included in the temporary ID group and assigned to new instrument information and old instrument information indicating an operation or a state of a same railway vehicle instrument is estimated.

A train driver assistance method includes: acquiring basic data of a train under a complex and severe condition; determining whether a traction power system is normal according to the basic data; if so, acquiring an energy-efficient optimized speed profile of the train in a normal state according to the basic data of the train in the current state; and if not, acquiring an energy-efficient optimized speed profile of the train in an abnormal state according to the basic data of the train in the current state. In this way, the method can acquire the energy-efficient optimized speed profile of the train in its current state. The comprehensive electric train driver assistance method can enable the train to adapt to the complex and severe environment and realize the energy-efficient operation of the train and self-rescue of the train.

The present disclosure generally relates to systems and methods of increasing functional safety of locomotives. In exemplary embodiments, a locomotive functional safety system is configured to: receive one or more manual control commands from a locomotive control stand and/or a user interface onboard a locomotive; determine whether the one or more manual control commands pass or satisfy one or more predetermined criteria; if the one or more manual control commands pass or satisfy the one or more predetermined criteria, approve the one or more manual control commands and allow the one or more manual control commands to be relayed onward and/or acted upon; and if the one or more manual control commands do not pass or satisfy the one or more predetermined criteria, disapprove the one or more manual control commands and disallow the one or more manual control commands to be relayed onward and/or acted upon.

A train control system includes independent virtual in-train forces modelling engines onboard each of a plurality of locomotives in a train. Each of the plurality of locomotives may also include an analytics engine and a calibration engine configured to assimilate, analyze, and calibrate real time information from other locomotives and from draft gears and couplers interconnecting the locomotives with determinations made by the independent virtual in-train forces modelling engine onboard the respective locomotive, with the plurality of locomotives of the train being configured to operate collectively and coordinate their own acceleration values based on a common goal of minimizing in-train forces without being dependent on a command from a lead locomotive or central command.

A train control method is provided for a vehicle on-board controller (VOBC) configured on one end of a train. The method includes: performing a train awakening process; acquiring a running plan sent by an automatic train supervision (ATS) system after the train is successfully awakened; setting, according to a direction indicated by the running plan, a running direction of the train to be downward or upward; when the running direction is set to downward, using, as a head for train positioning, one end of the train not configured with the VOBC, to acquire positioning information of the train; when the running direction is set to upward, using, as the head for train positioning, one end of the train configured with the VOBC, to acquire the positioning information of the train; and controlling, according to the positioning information of the train, the train to pull out of a parking garage.

An optimal driving profile is generated for vehicles provided with electric propulsion, by providing a sequence of consecutive time intervals until an arrival time and providing a plurality of accelerations that the vehicle can hypothetically take in each of the time intervals; by applying laws of uniformly accelerated motion for each of the accelerations and for each of the time intervals, hypothetical future points along the route are generated, starting from the current position and speed of the vehicle; for each of the time intervals, before analysing the subsequent time interval, it is checked whether the generated points meet all the predefined constraints; if the check has a negative outcome, the point being checked is eliminated from the plausible hypotheses; driving profiles are obtained, each defined by a respective sequence of remaining points; from among said driving profiles, the one requiring a lowest overall energy to be executed is selected.