Vehicle Trip Planning with Dynamic Parameter Estimation
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Solution Overview
Problem
Existing vehicle systems face inaccuracies and inefficiencies in trip planning due to differences between expected and actual vehicle parameters, leading to customer dissatisfaction and potential fuel losses, as calculated plans are often based on incorrect values.
Innovation Solution
An energy management processing unit with a trip planning module, estimator modules, and an arbiter module is implemented onboard the vehicle system to plan and re-plan trip profiles based on actual parameter values, adjusting power settings and throttle levels dynamically to optimize performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If trip planning is performed using expected parameter values, then the planning process is simple and fast, but the accuracy and efficiency of vehicle operations deteriorate due to differences between expected and actual values
Solution Approach 1:
The system performs preliminary estimation of actual parameter values (mass, horsepower, braking capability) before finalizing the trip plan. Estimator modules calculate these values in advance based on available data, allowing the trip planning module to use accurate actual values rather than expected values, thereby improving operational accuracy while maintaining a streamlined planning process.
Solution Approach 2:
The system implements feedback mechanisms where estimator modules continuously monitor and estimate actual vehicle parameters during operation. These estimated values are fed back to the trip planning module, which uses them to generate or adjust trip plans. This feedback loop ensures that planning decisions are based on accurate actual parameter values rather than static expected values.
2Loss of energy
If trip plans are calculated using expected values, then the calculation process is quick, but fuel efficiency deteriorates due to inaccuracy in power settings
Solution Approach 1:
The system performs preliminary estimation of actual vehicle parameters before trip plan calculation. By having estimator modules calculate accurate mass, horsepower, and braking capability values in advance, the trip planning module can generate fuel-optimized plans using real actual values rather than expected values, reducing fuel consumption without significantly extending planning time.
Solution Approach 2:
The system dynamically changes the parameter values used in trip planning from static expected values to dynamic actual values estimated by estimator modules. This parameter change allows the trip plan to be optimized based on the vehicle's true operational characteristics, improving fuel efficiency while the estimation process runs efficiently in the background.
3Productivity
If the system implements dynamic re-planning based on actual parameters, then operational efficiency improves, but system complexity and computational load increase
Solution Approach 1:
The system segments the trip planning functionality into distinct modules: estimator modules for parameter estimation, trip planning module for plan generation, and arbiter module for re-plan decision-making. This segmentation allows each module to perform its specific function efficiently, improving operational effectiveness while distributing computational load and managing complexity through modular architecture.
Solution Approach 2:
The system uses feedback from estimator modules to trigger selective re-planning when actual parameter values differ significantly from expected values. The arbiter module receives feedback about parameter deviations and determines whether re-planning is necessary, enabling dynamic adaptation to improve operational efficiency while avoiding unnecessary re-planning that would increase computational burden.
4Reliability
If the arbiter module evaluates multiple re-plan requests, then the reliability of trip planning improves, but the processing time and computational load increase
Solution Approach 1:
The arbiter module evaluates multiple re-plan requests from different estimator modules but implements a selective approach rather than processing all requests equally. It prioritizes requests based on the significance of parameter deviations and the criticality of affected trip plan aspects, performing partial evaluation to achieve sufficient reliability without the excessive processing time that would result from evaluating every possible re-plan request in detail.
Data Source
AI summary
A system includes an energy management processing unit that includes a trip planning module, a plurality of estimator modules, and an arbiter module. The trip planning module plans a trip profile specifying power settings based on trip data including a plurality of parameters having expected values. Each of the estimator modules generates an estimation re-plan request when the difference between an expected and experienced respective trip planning parameter value exceeds a threshold. The arbiter module receives at least one estimation re-plan request from the plurality of estimator modules, determines whether a re-plan is to be performed based on at least one of a state of the vehicle system or additional estimation re-plan requests, and provides an arbitrated re-plan request to the trip planning module for re-planning the trip profile when it is determined that a re-plan is to be performed.