EV Charging Station Power Reallocation for Missed Target SoC
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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, and external factors like weather and traffic, 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 SoC, and adjusting the maximum power of each charger to match the target power, thereby ensuring accurate power demand prediction and prevention of undercharging.
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 actions by obtaining and analyzing operational factor data (arrival times, departure times, initial SoC) before the charging operation begins. This advance preparation allows the system to predict power demand more accurately and generate appropriate ordering plans, ensuring charging reliability without compromising system complexity
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual operational factors against predicted values. This feedback loop enables the system to adjust predictions and ordering plans in real-time, maintaining reliable charging service while managing system complexity through adaptive control
2Reliability
If maximum power of chargers is increased to meet target SoC, then charging reliability improves, but deployment cost increases
Solution Approach 1:
The system applies dynamics by making charger power allocation flexible and adaptive rather than fixed. Based on predicted power demand and operational factors, the system dynamically adjusts the maximum power of chargers to match actual needs, ensuring reliable charging while optimizing infrastructure capacity utilization and reducing unnecessary deployment costs
Solution Approach 2:
The system changes parameters by adjusting charger power settings based on predicted demand patterns and operational factors. Instead of deploying fixed high-capacity infrastructure, the system modifies power allocation parameters in response to varying charging needs, achieving reliable service with optimized infrastructure capacity
3Measurement precision
If power demand prediction considers multiple operational factors, then prediction accuracy improves, but computational complexity increases
Solution Approach 1:
The system segments the complex prediction task into distinct components: obtaining operational factor data, predicting power demand based on these factors, and generating ordering plans. This segmentation allows each component to be processed independently and efficiently, improving prediction accuracy while managing computational complexity through modular processing
Solution Approach 2:
The system performs preliminary data collection and analysis of operational factors before the main prediction and ordering process. By preparing and validating input data in advance, the system reduces computational complexity during the actual prediction phase while maintaining high prediction accuracy through comprehensive factor consideration
Data Source
AI summary
A method, computing system, and computer-program product for robust optimization of charging of a plurality of electric vehicles scheduled for charging at a charging station is provided. The charging station includes a plurality of chargers. In an embodiment, the method includes obtaining vehicle data and station data. Based thereon, 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.