Trip Optimizer for Railroads image

Trip Optimizer for Railroads

Trip Optimizer for Railroads

On-time arrival with the least fuel expenditure is a key priority for freight railroads worldwide. GE’s Trip Optimizer is an easy-to-use control system that allows the crew or dispatcher to achieve on-time arrival while minimizing possible fuel use.

Optimal driving solutions are computed on-board and executed in closed loop using GPS-based navigation. Train and track parameters are adapted online to reduce model errors. Computing the driving plan requires solving an optimization program with thousands of variables in seconds.

A speed regulator design relies on a loop-shaping algorithm to maintain stable operation and manage variations in intercar forces. Location estimation provides precise coordinate tracking via Kalman-filter-based compensation for GPS dropouts. Model-based methods adaptively track key train parameters using GPS and other locomotive data.

Finally, innovative displays bring intuitive mode awareness and ease of use to the underlying optimal control strategy.

Project Impact

Fuel savings of 3% to 17% are realized. Putting that in perspective, for each Evolution locomotive on which it is used, Trip Optimizer can reduce fuel consumption by 32,000 gallons, cut CO2 emissions by more than 365 tons, and cut NOx emissions by 3.7 tons per locomotive per year. If Trip Optimizer is deployed on the approximately 10,000 similar locomotives in service in North America, these savings equate to taking a million passenger cars off the road for a year.

  • Our Expertise

    Capabilities utilized for Trip Optimizer for Railroads project

  • High Assurance Systems

    Enabling the design, development, and verification of safety critical infrastructure

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  • Embedded Computing

    Integrating computation with physical processes to create the joint optimization of algorithms, software and hardware

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  • Autonomy

    Developing intelligent robotic systems by integrating robot perception, learning, and motion planning

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  • Real-Time Optimization

    Combining Digital Twins, market data, and optimization algorithms to create supervisory control algorithms that will maximize business outcomes

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  • Model-Based Controls

    Developing advanced multi-variable model-based control algorithms that leverage online models to provide stability and improve transient performance and operability

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  • Estimation & Modeling

    Developing novel models for real-time use in controls, estimation and optimization

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