EV Charger Power Adjustment for Uncertain Target SoC Demand
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Solution Overview
Problem
Existing charging stations face challenges in accurately predicting power demand due to uncertainties in operational factors such as arrival and departure times of electric vehicles, initial state of charge, weather conditions, and driver behavior, leading to potential undercharging and inefficiencies.
Innovation Solution
The method involves obtaining vehicle data and station data, determining if electric vehicles have achieved a pre-defined target state of charge, computing a target power to achieve this state, adjusting the maximum power of each charger, and generating notifications for deploying the charging station based on these adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If power demand is estimated without considering uncertainties in operational factors, then ordering process is simple, but electric vehicles may be undercharged
Solution Approach 1:
The system performs preliminary analysis of operational factors and power demand before the actual charging operation. By predicting power demand in advance considering various uncertainties, the system prepares appropriate charging strategies beforehand, ensuring reliable charging without last-minute adjustments.
Solution Approach 2:
The system incorporates feedback loops that continuously monitor actual operational factors (arrival times, departure times, initial SoC) and compare them with predicted values. This feedback mechanism allows the system to adjust power demand predictions and charging strategies in real-time, improving charging reliability while managing complexity through adaptive control.
2Reliability
If maximum power of chargers is increased to meet target SoC, then charging reliability improves, but cost and device complexity increase
Solution Approach 1:
The system dynamically adjusts the maximum power of chargers based on real-time operational conditions and predicted power demand. Instead of using fixed high-power chargers, the system optimizes power allocation dynamically, allowing standard chargers to meet target SoC requirements through intelligent control, thereby reducing deployment costs while maintaining reliability.
Solution Approach 2:
The system changes operational parameters (charging power levels, time slots, charger assignments) to optimize charging outcomes. By adjusting these parameters based on predicted power demand and actual operational factors, the system achieves reliable charging without requiring expensive high-power charger infrastructure.
3Measurement precision
If power demand is predicted without considering operational uncertainties, then prediction process is simple, but power demand estimation accuracy deteriorates
Solution Approach 1:
The system performs preliminary analysis of operational factors and their uncertainties before finalizing power demand predictions. By pre-processing data and identifying key uncertainty sources in advance, the system builds accurate prediction models without excessive complexity during real-time operation.
Solution Approach 2:
The system uses historical operational data and patterns to create simplified models that replicate complex real-world behaviors. By copying past operational scenarios and their outcomes, the system achieves accurate power demand predictions using computationally efficient models rather than complex real-time simulations.
Data Source
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AI summary
The present invention provides a method, a computing system, and a computer-program product for robust optimization of charging of a plurality of electric vehicles (EVs) (108) scheduled for charging at a charging station (102). The charging station (102) comprises a plurality of chargers (106). In one embodiment, the method comprises obtaining vehicle data and station data (130). Based on the vehicle data and the station data (130), it is determined if a set of EVs from amongst the plurality of EVs has failed to achieve a pre-defined target SoC, after a pre-defined charging duration. If the set of EVs has failed to achieve the pre-defined target SoC, a target power is computed. Further, the method includes adjusting at least one of the maximum power of each charger associated with charging of each EV from the set of EVs and the maximum power of the set of EVs as per the target power, to reach the target SoC of the set of EVs.