Fleet Charging Site Planning for Peak Power and Dwell Periods
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems lack an efficient method to determine and meet the energy supply requirements for multiple battery-powered vehicles at candidate charge sites, as they fail to accurately identify dwell periods, energy consumption, and energy replenishment, leading to inadequate charging infrastructure planning.
Innovation Solution
A method and system that identify dwell periods, chargeable dwell periods, and energy consumption of vehicles, determining energy replenishable and required power supply at candidate charge sites by analyzing vehicle data, including location, acceleration, and motion data, to calculate supply requirements for peak demand and infrastructure needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If charging infrastructure is planned without accurate identification of dwell periods and energy consumption, then infrastructure planning is simplified, but energy supply requirements cannot be met and charging reliability deteriorates
Solution Approach 1:
The system performs preliminary analysis of vehicle dwell periods, locations, and energy consumption patterns before charging infrastructure is deployed. By pre-identifying chargeable dwell periods and calculating energy requirements, the system ensures that infrastructure planning is based on accurate data, thereby guaranteeing charging reliability without adding operational complexity.
Solution Approach 2:
The method segments vehicle operation into distinct dwell periods and active periods, further dividing dwell periods into chargeable and non-chargeable segments. This segmentation allows for precise calculation of energy consumption and supply requirements during chargeable periods, ensuring reliable charging planning while maintaining manageable system complexity through structured data organization.
2Quantity of substance
If energy supply requirements are not accurately determined for peak demand, then infrastructure cost is reduced, but energy supply sufficiency during peak demand deteriorates
Solution Approach 1:
The system pre-calculates energy supply requirements by analyzing historical vehicle data to identify peak demand periods and chargeable dwell periods. This preliminary calculation ensures that sufficient energy supply capacity is planned for peak demand scenarios before they occur, while the automated calculation process prevents excessive complexity in the planning stage.
Solution Approach 2:
The method uses vehicle data feedback loops to continuously monitor and refine energy consumption patterns and dwell period identification. This feedback mechanism ensures accurate determination of peak demand requirements and energy supply sufficiency, while the iterative refinement process automates complexity management through data-driven adjustments.
3Adaptability or versatility
If the number of simultaneously charging vehicles is not accommodated, then infrastructure capacity is reduced, but charging availability during high-demand periods deteriorates
Solution Approach 1:
The system segments the fleet into individual vehicle units with distinct dwell period patterns, allowing independent analysis of each vehicle's charging requirements. This segmentation enables the infrastructure to adaptively plan capacity for multiple simultaneous chargers based on actual vehicle behavior data, ensuring charging availability during high-demand periods while managing coordination complexity through modular vehicle-level tracking.
Solution Approach 2:
The method dynamically adjusts charging capacity planning based on real-time identification of chargeable dwell periods and vehicle locations. By continuously updating the understanding of how many vehicles may simultaneously require charging, the system adapts infrastructure capacity requirements to actual usage patterns, ensuring availability during peak periods while avoiding over-provisioning that would increase complexity.
Data Source
AI summary
Systems and methods for determining supply requirements for charging a plurality of vehicles are described. Based on real-world data for a plurality of vehicles, operation of comparable battery-powered vehicles is simulated. Energy consumption by the vehicles and energy replenishable to the vehicles is simulated, to determine power supply requirements or charge station requirements for candidate charge sites. Feasibility of implementing battery powered vehicles is assessed.