Vehicle Charging Site Planning Using Fleet Dwell and Energy Simulation
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Solution Overview
Problem
Current systems lack an efficient method to determine the energy supply requirements for multiple battery-powered vehicles at candidate charge sites, as they fail to accurately identify dwell periods, chargeable dwell periods, and energy consumption patterns, leading to inadequate infrastructure planning.
Innovation Solution
A method and system that identify dwell periods, chargeable dwell periods, and energy consumption of battery-powered vehicles by analyzing location, acceleration, and motion data, and determine energy replenishable and supply requirements for candidate charge sites, including required power supply and peak demand, using telematics data and simulation of vehicle operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional infrastructure planning methods are used, then infrastructure development is simple and quick, but energy supply requirements cannot be accurately determined leading to inadequate charging infrastructure
Solution Approach 1:
The system segments the infrastructure planning process into distinct analytical components: identifying dwell periods, determining chargeable dwell periods, calculating energy consumption, and assessing supply requirements. This segmentation allows each aspect to be analyzed separately with appropriate data and methods, improving overall accuracy while maintaining manageable complexity through modular processing.
Solution Approach 2:
The system performs preliminary analysis by identifying dwell periods and chargeable dwell periods before calculating energy requirements. This preliminary action establishes the foundation for accurate energy supply determination by pre-processing vehicle operational data, location data, and charge site information to identify relevant charging opportunities and consumption patterns.
2Measurement precision
If detailed vehicle operational analysis is performed to accurately determine energy consumption patterns, then energy supply planning accuracy improves, but data processing time and computational resources increase
Solution Approach 1:
The system extracts only the essential data elements needed for energy consumption determination from vehicle operational data, location data, and charge site information. By focusing on critical parameters such as dwell period timing, location coordinates, and energy usage metrics, the system achieves accurate energy supply planning without processing unnecessary data, thus reducing computational overhead and processing time.
Solution Approach 2:
The system performs analysis at the appropriate level of detail by identifying chargeable dwell periods and calculating energy consumption only for relevant time periods and locations. Rather than analyzing all vehicle operational data continuously, the system focuses on partial but sufficient information - specifically dwell periods at charge sites - achieving accurate energy determination without excessive data processing.
3Productivity
If the system identifies all dwell periods and chargeable dwell periods for multiple vehicles, then charging infrastructure optimization improves, but system complexity and data processing requirements increase
Solution Approach 1:
The system merges the analysis of multiple vehicles by aggregating dwell period data, location data, and energy consumption patterns across the fleet. This combining approach identifies common charging needs and optimal charge site utilization strategies, improving overall infrastructure efficiency while reducing redundant analysis through consolidated data processing.
Solution Approach 2:
The system develops a universal analysis framework that handles multiple vehicles simultaneously using the same methodology for identifying dwell periods, determining chargeable periods, and calculating energy requirements. This multi-functional approach optimizes charging infrastructure for entire fleets rather than individual vehicles, improving productivity through standardized processes that scale across multiple vehicles without proportionally increasing complexity.
Data Source
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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.