Regional Electrified Freight Architecture for Energy Coordination
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Solution Overview
Problem
The transition to electrified commercial vehicles presents challenges due to the disparity between vehicle powertrains and charging infrastructure, leading to increased asset costs, downtime, and carbon emissions, necessitating a unified systems architecture that integrates vehicle powertrain architectures, operations logistics, and energy pathways to optimize electrified transport.
Innovation Solution
A method for generating an optimized regional architecture that integrates vehicle powertrain architectures, operations logistics, and energy pathways, using iterative modeling to refine freight transport, vehicle energy, and energy infrastructure models, ensuring harmonious operation and meeting performance targets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If electrified commercial vehicles are deployed without unified systems architecture, then vehicle electrification can proceed, but asset costs, downtime, and carbon emissions increase
Solution Approach 1:
The system segments the electrified transport ecosystem into distinct but interconnected components: vehicle powertrain architectures, operations logistics, and energy pathways (charging infrastructure and grid). Each segment is modeled and optimized independently through specialized sub-models, then integrated through co-optimization to achieve system-wide efficiency while reducing asset costs and downtime
Solution Approach 2:
The methodology performs preliminary actions by conducting comprehensive systems architecture planning and coordination before vehicle deployment. Through iterative modeling and co-optimization, the system determines optimal charging infrastructure locations, quantities, and power ratings in advance, preventing reactive solutions that would otherwise increase costs and emissions
2Productivity
If reactive solutions are implemented for electrified transport, then deployment can occur quickly, but net carbon emissions and regional power loss increase
Solution Approach 1:
The system performs preliminary planning and coordination through iterative modeling to determine optimal charging infrastructure deployment strategies before implementation. This advance planning identifies the most effective phase-in locations, quantities, and power ratings, enabling quick deployment without reactive measures that would increase carbon emissions and power loss
Solution Approach 2:
The methodology incorporates feedback mechanisms through iterative modeling and co-optimization, where results from each modeling iteration inform adjustments to the next. This continuous feedback loop ensures that deployment strategies are refined to minimize carbon emissions and power loss while maintaining deployment momentum
3Reliability
If behind-the-fence energy management is used, then energy needs can be met, but asset cost and net carbon emissions increase
Solution Approach 1:
The system merges behind-the-fence energy management with public access charging infrastructure through unified systems architecture. By coordinating private and public charging resources through integrated modeling and co-optimization, the system reduces reliance on costly standalone private infrastructure while minimizing carbon emissions through optimized energy pathway selection
4Ease of manufacture
If charging infrastructure is deployed without unified planning, then infrastructure can be established, but effective phase-in determination becomes challenging
Solution Approach 1:
The system segments the complex infrastructure deployment planning into manageable components through specialized sub-models for vehicle powertrain architectures, operations logistics, and energy pathways. This segmentation simplifies the planning process while the co-optimization framework integrates the components to determine optimal phase-in strategies for locations, quantities, and power ratings
5Adaptability or versatility
If iterative co-optimization modeling is performed, then optimal regional architecture is achieved, but computational time and complexity increase
Solution Approach 1:
The system segments the comprehensive systems modeling into three interconnected but independently executable sub-models: freight transport modeling, vehicle energy modeling, and energy infrastructure modeling. This segmentation allows parallel computation of each sub-model while maintaining system-wide optimization through iterative co-optimization, reducing overall computational time compared to monolithic modeling approaches
Data Source
AI summary
A method for generating an optimized regional architecture for efficient national transport is provided. The method integrates vehicle powertrain architectures, operations logistics, and energy pathways to optimize both behind-the-fence and public access energy dispensing solutions supported by local distributed energy resource equipment and centralized fuel sourcing. More specifically, the method provides an optimal regional architecture for electrified national transport modeling. The method assimilates critical data for seasonal operating scenarios to provide a regional specific constrained-optimal infrastructure deployment solution. As discussed herein, the present invention provides local government agencies, industry end users, energy suppliers, and equipment providers with a flexible planning tool to navigate the deployment of electrified freight transportation systems in view of localized constraints.