HYBRID CONSIST TRACTIVE EFFORT MANAGEMENT
    221.
    发明公开

    公开(公告)号:US20240199099A1

    公开(公告)日:2024-06-20

    申请号:US18081003

    申请日:2022-12-14

    Abstract: A train control system minimizes in-train forces in a train with a hybrid consist including a diesel-electric locomotive and a battery electric locomotive. The train control system includes a virtual in-train forces modeling engine configured to simulate 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 when the train is accelerating, and inputs derived from stored historical contextual data characteristic of the train, and a virtual in-train forces model database configured to store in-train forces models. Each of the in-train forces models includes a mapping between combinations of the stored historical contextual data and corresponding simulated in-train forces and train operational characteristics that occur when the consist is changing speed. An energy management system determines an easing function of tractive effort vs. time that will minimize the in-train forces created by changes in tractive effort responsive to power notch changes in a diesel-electric locomotive, and commands execution of the easing function by a battery electric locomotive based at least in part on an in-train forces model with simulated in-train forces and train operational characteristics that fall within a predetermined acceptable range of values.

    Method for monitoring the position of a parked rail vehicle, and computer program, in particular for a train safety system

    公开(公告)号:US11866075B2

    公开(公告)日:2024-01-09

    申请号:US17916192

    申请日:2021-03-01

    CPC classification number: B61L25/025 B61L27/04 B61L27/70 B61L2201/00

    Abstract: The position of a rail vehicle that is parked on a track is monitored in a cold movement detection. A vehicle-side device of an automatic train safety system is deactivated when the vehicle is parked. Prior to the deactivation, a first positional value is determined by the automatic train safety system, and independently, a second positional value is determined by another localization system. With the vehicle-side device deactivated, the actual position of the vehicle is monitored by the other localization system. The additional positional values and/or a deviation of the actual position from a target position is transmitted to a track-side device of the train safety system. When the vehicle-side device is activated, the track-side device transmits the actual position of the vehicle to the vehicle-side device. If the vehicle has not moved, the automatic train guidance system can immediately assume the monitoring process starting from the first position.

    CABLE CAR AND CABLE CAR NETWORK WITH SEVERAL CABLE CARS

    公开(公告)号:US20230322281A1

    公开(公告)日:2023-10-12

    申请号:US18299326

    申请日:2023-04-12

    Inventor: Clemens Mohr

    CPC classification number: B61L23/002 B61L2201/00 B61B12/002

    Abstract: A cable car and a cable car network with cable car stations and cable car vehicles movable with a haulage rope between the cable car stations includes a cable car control unit for controlling the cable car, wherein a maximum electrical energy consumption of the cable car is predetermined; an energy detection unit configured for determining an electrical energy consumption of the cable car; and wherein the cable car control unit is configured to control or regulate an electrical energy consumption of at least one electrical consumer of the cable car based at least in part on the determined electrical energy consumption of the cable car such that the maximum electrical energy consumption predetermined for the cable car is not exceeded. Associated methods of operating a cable car or cable car network are also disclosed.

    TRAIN OPERATION CONTROL SYSTEM AND METHOD BASED ON TRAIN-GROUND COORDINATION

    公开(公告)号:US20230257010A1

    公开(公告)日:2023-08-17

    申请号:US18013265

    申请日:2021-11-04

    Abstract: A train operation control system and method based on train-ground coordination are provided. The system includes a dispatching center server, a resource management unit (RMU) for ground train control equipment, and on-board train control equipment (CC), wherein the dispatching center server is connected via communication to the on-board CC, and the on-board CC is connected via communication to the RMU for the ground train control equipment; and the RMU for the ground train control equipment and the on-board CC coordinatively complete resource management and implement train operation control, wherein the resource management is divided into two levels, at a first level, the RMU is responsible for performing the resource management in the unit of section, and at a second level, a preceding train and a succeeding train interact with each other via direct train-to-train communication, such that finer resource sharing in a section is achieved between the trains.

    VEHICLE SPEED MANAGEMENT SYSTEMS AND METHODS
    228.
    发明公开

    公开(公告)号:US20230227082A1

    公开(公告)日:2023-07-20

    申请号:US18123082

    申请日:2023-03-17

    CPC classification number: B61L25/021 G06V10/82 G06N3/08 B61L2201/00

    Abstract: A method is provided that may include obtaining image data from vision sensors disposed onboard a vehicle. The method may include determining a stopping distance of the vehicle based at least in part on the image data using an artificial intelligence (AI) neural network having artificial neurons arranged in layers and connected with each other by connections. A moving speed and a speed limit of the vehicle may be determined using the AI neural network. The method may control movement of the vehicle using the AI neural network by enforcing movement authorities preventing unwarranted movement of the vehicle based on a difference between the moving speed and the speed limit. The method may include receiving feedback regarding the stopping distance and the speed limit calculated by the artificial neurons and training the AI neural network by changing connections between the artificial neurons based on the feedback received.

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