Prediction, planning, and optimization of trip time, trip cost, and/or pollutant emission for a vehicle using machine learning

A method for controlling operation of a vehicle, comprising:

• generating a predictive model relating at least the vehicle's engine speed, fuel consumption rate, and vehicle speed based, at least in part, on training data including values of the vehicle's fuel consumption rate and vehicle speed that are collected over an engine speed range of at least one engine of the vehicle;
• obtaining at least one constraint on at least one of a trip time, trip fuel consumption, vehicle speed, fuel consumption rate, or pollutant emissions;
• automatically producing, by at least one processor and based, at least in part, on the predictive model and the at least one constraint, an output constraint on vehicle operation;
• and
• determining a failure of the predictive model.

A method for controlling operation of a vehicle, comprising:

• generating one or more predictive models relating vehicle speed, at least one engine or vessel parameter that indicates or influences propulsion, and at least one of directly measured operating condition or indirectly measured operating condition based, at least in part, on (a) training data collected over an engine speed range of at least one engine of the vehicle and (b) at least one of a wind condition or water condition;
• obtaining at least one constraint on at least one of a trip time, trip fuel consumption, vehicle speed, fuel consumption rate, pollutant emissions, or optimization of at least fuel cost and time cost;
• and
• automatically producing, by at least one processor and based, at least in part, on the one or more predictive models and the at least one constraint, an output constraint for the vehicle.

A computer-readable storage medium storing contents that, when executed by one or more processors, cause the one or more processors to perform actions comprising:

• generating a predictive model relating at least a vehicle's engine speed, fuel consumption rate, and vehicle speed based, at least in part, on training data including values of the vehicle's fuel consumption rate and vehicle speed that are collected over an engine speed range of at least one engine of the vehicle;
• obtaining at least one constraint on at least one of a trip time, trip fuel consumption, vehicle speed, fuel consumption rate, or pollutant emissions;
• automatically producing an output constraint on vehicle operation based, at least in part, on the predictive model and the at least one constraint;
• and
• determining a failure of the predictive model.

A computer-readable storage medium storing contents that, when executed by one or more processors, cause the one or more processors to perform actions comprising:

• generating one or more predictive models relating vehicle speed, at least one engine or vessel parameter that indicates or influences propulsion, and at least one of directly measured operating condition or indirectly measured operating condition based, at least in part, on (a) training data collected over an engine speed range of at least one engine of a vehicle and (b) at least one of a wind condition or water condition;
• obtaining at least one constraint on at least one of a trip time, trip fuel consumption, vehicle speed, fuel consumption rate, pollutant emissions, or optimization of at least fuel cost and time cost;
• and
• automatically producing an output constraint for the vehicle based, at least in part, on the one or more predictive models and the at least one constraint.