EV Charging Station Sizing Using Fuel-to-Charging Demand Conversion
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Solution Overview
Problem
The challenge of sizing electric vehicle charging stations to meet future demand while ensuring efficient use of electrical network capacity remains unresolved, particularly in transitioning from fuel stations to charging infrastructure.
Innovation Solution
A method and device for sizing a charging station by converting historical fuel consumption data into equivalent electric charging sessions, determining average charging power based on vehicle categories, and calculating the required number of charging points and compensation battery sizing to manage future electrical load demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the number of charging points is increased to meet future electric vehicle demand, then the charging station can serve more vehicles and reduce waiting times, but the complexity of the charging station and the load on the electrical network increases
Solution Approach 1:
The patent applies preliminary action by converting historical fuel consumption data into equivalent electric charging sessions before the charging station is operational. This pre-processing of data allows for accurate prediction of future charging demand patterns, enabling proper sizing of charging infrastructure before deployment, thus avoiding the need for later expansions and reductions in complexity
Solution Approach 2:
The patent utilizes parameter changes by transforming fuel consumption parameters (volume, frequency) into electric charging parameters (power, duration, energy). This parameter transformation enables the application of historical data from fuel stations to predict electric vehicle charging patterns, allowing for optimized charging point configuration that balances capacity needs with infrastructure complexity
2Loss of time
If the charging station is sized to meet peak future demand, then user waiting times are minimized, but the electrical network capacity utilization becomes inefficient during low-demand periods
Solution Approach 1:
The patent applies dynamics by using probabilistic models to represent the dynamic and uncertain nature of future electric vehicle charging demand. Instead of static sizing based on deterministic assumptions, the system models demand as probability distributions that can be updated and refined, enabling flexible charging station configuration that adapts to varying demand patterns while minimizing both waiting times and wasted capacity
Solution Approach 2:
The patent incorporates feedback mechanisms by using historical fuel station operational data to continuously refine predictions of future electric vehicle charging patterns. This feedback loop allows the system to learn from actual usage patterns and adjust charging station sizing recommendations to achieve optimal balance between service level (minimizing waiting time) and resource utilization (electrical network capacity)
3Measurement precision
If fuel station operational data is converted to equivalent electric charging sessions, then accurate predictions of future charging demand can be made, but the complexity of data processing and conversion increases
Solution Approach 1:
The patent uses an intermediary approach by introducing equivalent electric charging sessions as a mediating concept between historical fuel consumption data and future charging demand predictions. Rather than directly mapping fuel data to electric vehicle parameters, the system creates intermediate equivalent charging sessions that preserve the statistical patterns of fuel usage while translating them into electric vehicle charging context, simplifying the overall conversion process while maintaining prediction accuracy
Data Source
Figure 1
Figure 2a~2b
Figure 2c~2d
AI summary
The present disclosure relates to a method for sizing a charging station for electric vehicles, in view of operating the charging station at a future period of time T, the charging station being installed at the location of a fuel station operated over a past or current period of time N, said charging station comprising at least one electric charging point and being connected to an electrical network, the method being implemented by a device comprising a circuit and a memory, the memory comprises consumption data specific to the previously operated fuel station for the period of time N comprising a set of fuel consumption sessions, and at least one charging power probability distribution as a function of vehicle categories; the method comprises: /a/ converting fuel consumption sessions into a set of equivalent electric charging sessions; /b/ for each equivalent electric charging session, determining an average charging power based on the charging power probability distribution and the equivalent electric charging session; /c/ determining a number of electric charging point(s) of the charging station from the average charging powers of the equivalent electric charging sessions determined for the period T.