EV Charging Control Algorithm Selection Under Infrastructure Constraints
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Solution Overview
Problem
Existing electric vehicle charging infrastructures face challenges in determining the best control algorithm to provide optimal quality of service due to varying user needs and technical constraints, including infrastructure capabilities and user behavior, which can lead to inefficiencies and service interruptions.
Innovation Solution
A method for selecting a control algorithm by simulating different algorithms under predefined scenarios, calculating performance indicators, and choosing the algorithm that best meets the quality of service criteria, considering factors like power delivery, vehicle state, and infrastructure constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single control algorithm is used for managing charging infrastructure, then the system is simple to operate, but it cannot adapt to varying user needs and technical constraints, leading to reduced quality of service
Solution Approach 1:
The system dynamically selects control algorithms based on real-time conditions including user needs, vehicle characteristics, and infrastructure constraints. Instead of using a fixed algorithm, the system adapts its control strategy by evaluating multiple algorithms and selecting the most appropriate one for each charging scenario, thereby achieving adaptability without permanent system complexity.
Solution Approach 2:
The system changes the parameter of algorithm selection based on varying conditions. By monitoring parameters such as user preferences, vehicle battery state, charging speed requirements, and infrastructure capacity, the system adjusts which control algorithm is deployed, allowing the same infrastructure to serve diverse needs through parameter-driven algorithm selection.
2Reliability
If multiple control algorithms are evaluated and selected based on performance indicators, then the quality of service is optimized, but the computational complexity and time required for algorithm selection increases
Solution Approach 1:
The system performs preliminary evaluation of control algorithms by simulating their performance under predefined scenarios before actual deployment. By pre-assessing algorithms against various charging scenarios and establishing performance indicators in advance, the system reduces the time required for real-time decision-making, as the selection process relies on pre-computed performance data rather than exhaustive real-time analysis.
Solution Approach 2:
The system uses simplified performance indicator calculations that require minimal computational resources. Rather than implementing complex, time-consuming simulations for each algorithm selection, the system employs lightweight evaluation metrics that can be quickly computed, sacrificing some depth of analysis for the benefit of rapid algorithm selection in real-time operations.
3Productivity
If control algorithms are selected without considering infrastructure constraints, then the selection process is simpler, but service interruptions occur due to power delivery limitations
Solution Approach 1:
The system incorporates feedback from infrastructure constraints into the algorithm selection process. By continuously monitoring actual power delivery capacity, vehicle requirements, and charging progress, the system adjusts its algorithm selection to ensure that chosen algorithms are compatible with current infrastructure capabilities, preventing service interruptions caused by mismatched control strategies.
Solution Approach 2:
The system takes preliminary actions to prevent service interruptions by evaluating infrastructure constraints before selecting control algorithms. By assessing power delivery limitations, connector types, and charging station capabilities in advance, the system chooses algorithms that are pre-suitable for the given infrastructure, thereby avoiding situations where algorithm execution would fail or cause service disruptions.
Data Source
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AI summary
The invention relates to a method for selecting a control algorithm for an electric vehicle charging infrastructure, from among a plurality of predefined control algorithms, comprising steps of: a - simulating control of the charging infrastructure according to each of the control algorithms by following the same predefined activity scenario of the charging infrastructure over a given period of time, the predefined activity scenario comprising the charging of one or more electric vehicles, and for each electric vehicle a time slot for connecting the electric vehicle to one of the terminals of the charging infrastructure and a value of an electrical energy requirement of the electric vehicle, b - for each control algorithm, calculating a performance indicator of the control algorithm, c - selecting one of the control algorithms from among the plurality of predefined control algorithms,based on the performance indicators calculated for the different steering algorithms.,