Fuel consumption system for locomotive
A method for predicting changes in fuel consumption due to alterations in train operations is disclosed. The method comprises: collecting a baseline train data of a first train, the baseline train data including first train parameters, first operational parameters, and a first fuel consumption of the first train; inputting the baseline train data into an artificial intelligence (AI) model; training an AI model with a second train data, the second train data includes second operational parameters and a second fuel consumption of the first train, the AI model is trained until a baseline operation is predictable; implementing operational changes from the baseline operation to a field train operation for a third train, the field train operation includes changes to the first train parameters and changes to the first operational parameters implemented in the third train; and predicting, utilizing the AI model, the fuel consumption of the third train.
1 . A method for predicting changes in fuel consumption due to alterations in train operations, the method comprising:
collecting a baseline train data of a train, the baseline train data including first train parameters, first operational parameters, and a first fuel consumption of a historical train;
inputting the baseline train data into an artificial intelligence (AI) model;
training the AI model with a second train data, the second train data including second operational parameters and a second fuel consumption of the train, wherein the AI model is trained until a baseline operation is predictable;
implementing operational changes from the baseline operation to a field train operation of the train, wherein the field train operation includes changes to the first train parameters and changes to the first operational parameters; and
predicting, utilizing the AI model, the fuel consumption of the field train operation.
2 . The method of claim 1 , wherein:
the baseline train data includes baseline train run data and simulation data;
the first train parameters include train tonnage, consist tonnage, a propulsion systems, a train system change, and consist changes from the baseline operation; and
the first operational parameters includes a route dataset, total trip time, consist tonnage, horsepower, train performance, tractive effort, braking efficiency, track information, and historical fuel consumption metrics.
3 . The method of claim 1 , further comprising:
applying a simulation of proposed operational changes to the AI model, wherein the proposed changes include at least one of the following: speed alteration, load variation, route modification, or operational strategy adjustment.
4 . The method of claim 1 , wherein the AI model utilizes a Regularization Technique.
5 . The method of claim 1 , further comprising:
comparing a predicted fuel consumption with the baseline train data to quantify an impact of proposed operational changes on the fuel consumption.
6 . The method of claim 1 , wherein the AI model employs one chosen from the group consisting of Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), Gated Recurrent Units (GRUs), Transformer Models, Feedforward Neural Networks, Autoencoders, Hybrid Models, Regression Models, Linear Regression, Logistic Regression, Polynomial Regression, Ridge Regression, Lasso Regression, Quantile Regression, Bayesian Linear Regression, Principal Components Regression, Partial Least Squares Regression, Elastic Net Regression, Step wise Regression, Support Vector Machine Regression, and Decision Tree Regression.
7 . The method of claim 2 , wherein the route dataset includes characteristics of the train, a route of the train, and a topography of the route.
8 . A train comprising:
a frame;
ground engaging elements supporting the frame;
a prime mover for powering propulsion of the ground engaging elements, the prime mover mounted in the frame;
a plurality of consists; and
a controller including an artificial intelligence (AI) model for predicting fuel consumption of a field operation, the AI model configured to:
collect a baseline train data of the train, the baseline train data including first train parameters, first operational parameters, and a first fuel consumption of a historical train,
train the AI model with a second train data until a baseline operation is predictable, the second train data includes second operational parameters and a second fuel consumption of the train,
analyze implemented operational changes from the baseline operation to the train including changes to the first train parameters, and changes to the first operational parameters, and
predict the fuel consumption of the field operation.
9 . The train of claim 8 , wherein the train is connected to the plurality of consists, each consist having second ground engaging elements and a second controller.
10 . The train of claim 9 , further comprising:
a GPS device in communication with the controller, the GPS device providing real-time location of the train,
wherein the controller is further configured to consider topography of a route and weather conditions along the route in predicting the fuel consumption.
11 . The train of claim 9 , wherein:
the baseline train data includes baseline train run data, simulation data, train tonnage, consist tonnage, a propulsion system performance data, a train system change, and consist changes from the baseline operation, and
the first operational parameters include a total trip time, consist tonnage, horsepower, train performance, tractive effort, braking efficiency, track information, and historical fuel consumption metrics.
12 . The train of claim 9 , wherein the AI model is further configured to apply a simulation of proposed operational changes to the AI model, wherein the proposed changes include at least one chosen from the group consisting of: a speed alteration, a load variation, a route modification, and a train system change.
13 . The train of claim 9 , wherein the AI model utilizes a Regularization Technique.
14 . A system for predicting fuel consumption in a train, the system comprising:
a controller including a processor, the controller comprising:
a data collection module configured to collect baseline train data for the train, the baseline train data including initial train parameters, initial operational parameters, and an initial fuel consumption based on historical data of the train,
an artificial intelligence (AI) model operatively connected to the data collection module, configured to:
receive and process the baseline train data, and
iteratively train with new train data of the train to establish a predictive baseline operation model for the train, the new train data including new operational parameters and corresponding fuel consumption data of the train,
an operational change module configured to track changes in train operations, including modifications to the train, adjustments to the initial train parameters, and alterations to the initial operational parameters, and
a fuel prediction unit configured to:
receive data regarding operational changes from the operational change module, and
predict the fuel consumption for the train reflecting the changes in train operations.
15 . The system of claim 14 , wherein:
the baseline train data includes baseline train run data, simulation data, train tonnage, consist tonnage, propulsion system performance data, a train system change, and consist changes from the baseline operation, and
the initial operational parameters include a total trip time, consist tonnage, horsepower, train performance, tractive effort, braking efficiency, track information, and historical fuel consumption metrics,
wherein the fuel prediction unit is further configured to calculate fuel consumption savings from the changes in train operations.
16 . The system of claim 15 , wherein the AI model is further configured to apply a simulation of proposed operational changes to the AI model, wherein the proposed changes include at least one chosen from the group consisting of: a speed alteration, a load variation, a route modification, and a train system change.
17 . The system of claim 15 , wherein the AI model utilizes a Regularization Technique.
18 . The system of claim 15 , wherein the AI model is further configured to compare a predicted fuel consumption with the baseline train data to quantify an impact of the operational changes on the fuel consumption of the train.
19 . The system of claim 15 , wherein the AI model employs one chosen from the group consisting of Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), Gated Recurrent Units (GRUs), Transformer Models, Feedforward Neural Networks, Autoencoders, Hybrid Models, Regression Models, Linear Regression, Logistic Regression, Polynomial Regression, Ridge Regression, Lasso Regression, Quantile Regression, Bayesian Linear Regression, Principal Components Regression, Partial Least Squares Regression, Elastic Net Regression, Step wise Regression, Support Vector Machine Regression, and Decision Tree Regression.
20 . The system of claim 15 , wherein the initial operational parameters further include a route dataset, the route dataset including characteristics of the train, a route of the train, and a topography of the route.