Energy Management System Optimizing PGV Selection for Fuel and Emissions
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Solution Overview
Problem
Existing transportation networks face inefficiencies in vehicle yard operations, particularly in selecting propulsion-generating vehicles (PGV) to match cargo-carrying vehicles (CCV) based on energy efficiency and emission standards, leading to suboptimal fuel consumption and emission levels.
Innovation Solution
A method and system that utilize processors to calculate a tractive effort threshold, select PGV from a larger group based on energy efficiency and emission criteria, and optimize vehicle configurations within the yard, ensuring that the selected PGV consume less fuel and generate fewer emissions while meeting the required tractive effort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional vehicle pairing methods are used in vehicle yards, then vehicles can be assigned to payloads based on power and ability, but fuel consumption and emissions are not optimized
Solution Approach 1:
The system changes the selection parameters from traditional power-only criteria to a multi-parameter optimization including fuel consumption and emissions. The energy management system evaluates multiple PGV candidates based on their energy efficiency characteristics and selects the optimal combination that minimizes fuel consumption while meeting the tractive effort requirement.
Solution Approach 2:
The system implements feedback loops where the energy management system continuously monitors fuel consumption and emissions data from PGV operations. This feedback is used to refine future vehicle pairing decisions, creating a closed-loop optimization system that improves energy efficiency over time while maintaining reliable vehicle assignment.
2Reliability
If traditional vehicle pairing methods are used in vehicle yards, then vehicles can be assigned to payloads based on power and ability, but emissions are not optimized
Solution Approach 1:
The system expands selection criteria beyond traditional power and ability parameters to include emissions characteristics. The energy management system evaluates PGV candidates based on their emissions profiles and selects combinations that minimize harmful emissions while maintaining the required tractive effort for reliable payload delivery.
Solution Approach 2:
The system incorporates emissions data feedback into the vehicle pairing optimization process. By monitoring emissions from PGV operations and feeding this information back into the selection algorithm, the system continuously improves its ability to assign vehicles in a way that reduces harmful emissions while maintaining assignment reliability.
3Use of energy by moving object
If the energy management system integrates with the yard planner system, then fuel consumption and emissions are reduced, but system complexity increases
Solution Approach 1:
The system merges the energy management system with the existing yard planner system, integrating energy optimization functions into the established vehicle assignment infrastructure. This integration allows the system to leverage existing hardware and software platforms, reducing the overall complexity increase despite adding sophisticated energy optimization capabilities.
Solution Approach 2:
The integrated system performs multiple functions including traditional vehicle assignment based on power and ability, energy optimization to minimize fuel consumption, and emissions reduction. By making the system multi-functional, it consolidates multiple operations into a unified platform, managing complexity through functional integration rather than separate systems.
4Object-generated harmful factors
If the energy management system integrates with the yard planner system, then emissions are reduced, but system complexity increases
Solution Approach 1:
The system integrates emissions reduction capabilities into the existing yard planner infrastructure, combining environmental optimization with operational planning. This merging approach allows emissions management to be handled within the established system framework, minimizing the complexity increase associated with adding new environmental control functions.
Solution Approach 2:
The integrated system provides universal functionality that simultaneously addresses traditional vehicle assignment requirements, fuel consumption optimization, and emissions reduction. By designing the system to handle multiple objectives within a single unified platform, it manages complexity through multi-functionality rather than requiring separate specialized systems.
5Use of energy by moving object
If PGV selection is optimized for energy efficiency, then fuel consumption is reduced, but the availability of suitable PGV may decrease
Solution Approach 1:
The system performs preliminary evaluation and pre-positioning of PGV based on energy efficiency criteria. By proactively identifying and preparing energy-efficient PGV combinations in advance, the system ensures their availability when needed while optimizing for fuel consumption. This preliminary action allows the system to balance energy efficiency with operational availability requirements.
Solution Approach 2:
The PGV selection system dynamically adjusts its optimization criteria based on real-time operational conditions and PGV availability. When energy-efficient PGV are available, the system prioritizes fuel consumption reduction; when availability is constrained, it flexibly adjusts to maintain operational versatility. This dynamic adaptation resolves the contradiction between energy optimization and PGV availability.
6Object-generated harmful factors
If PGV selection is optimized for energy efficiency, then emissions are reduced, but the availability of suitable PGV may decrease
Solution Approach 1:
The system performs preliminary identification and preparation of low-emission PGV combinations, ensuring their availability for upcoming assignments. By proactively managing the pool of environmentally friendly vehicles, the system maintains both emissions reduction goals and adequate PGV availability for diverse operational requirements.
Solution Approach 2:
The system dynamically balances emissions optimization with PGV availability constraints. It adjusts its selection criteria in real-time based on the availability of environmentally friendly vehicles, maintaining versatility in PGV deployment while prioritizing emissions reduction when suitable vehicles are available. This dynamic approach resolves the trade-off between environmental performance and operational adaptability.
Data Source
AI summary
A method and system for identifying cargo-carrying vehicles (CCV) to be included in a vehicle system that are non-propulsion-generating vehicles scheduled to depart from a vehicle yard and travel to one or more destination locations at a predetermined departure time. Further includes, calculating a minimum tractive effort threshold required by one or more propulsion-generating vehicles (PGV) to propel the vehicle system to the one or more destination locations along a route within a predetermined time period. And selecting a set of one or more PGV from a larger group of PGV within the vehicle yard to be included in the vehicle system. The set of one or more PGV produce a tractive effort of at least the minimum tractive effort threshold, and having at least one of a lower fuel consumption or lower emissions than selecting one or more of the remaining PGV from the larger group of PGV, respectively.