EV Charging Port Power Distribution for Variable Connection Times
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Solution Overview
Problem
Existing power distribution systems for electric vehicle charging stations struggle to optimize power allocation across multiple ports, leading to inefficient charging, especially in scenarios with varying vehicle connection times and priorities.
Innovation Solution
The implementation of machine learning techniques to dynamically control power distribution among multiple charging station ports, using trained machine learning engines to allocate a fixed power budget based on real-time data and objectives such as maximizing vehicle service, minimizing charge time, and optimizing energy use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If fixed even power distribution is used among multiple charging ports, then power allocation simplicity is maintained, but charging efficiency deteriorates when vehicles have different connection times and priorities
Solution Approach 1:
The patent implements dynamic power distribution that adjusts power allocation in real-time based on vehicle connection time predictions, battery states, and charging priorities. The system transitions from static even distribution to dynamic adaptive distribution, where power levels are continuously optimized based on current system state and predicted future states.
Solution Approach 2:
The system changes the power distribution parameters dynamically by using machine learning models to predict vehicle connection durations and adjusting power allocation accordingly. The patent modifies power distribution parameters based on predicted connection times, battery charge levels, and vehicle priorities, transforming fixed parameters into adaptive variables.
2Productivity
If machine learning techniques are implemented for dynamic power distribution, then charging efficiency is improved, but system complexity increases
Solution Approach 1:
The patent introduces machine learning models as intermediary components that sit between the power distribution system and the charging ports. These models process input data about vehicle states, connection times, and power availability, then translate complex optimization problems into actionable power allocation decisions, simplifying the control architecture while maintaining high efficiency.
Solution Approach 2:
The system employs self-service mechanisms where the machine learning models automatically learn from historical data and continuously improve power distribution decisions without manual intervention. The models autonomously adjust power allocation based on learned patterns, reducing the need for complex manual configuration and ongoing system management.
3Reliability
If power is allocated based on predicted connection time, then vehicles with short connection times are adequately charged, but power distribution complexity increases
Solution Approach 1:
The patent applies preliminary action by using machine learning models to predict vehicle connection times before charging begins. The system proactively allocates power based on these predictions, ensuring that vehicles with short predicted connection times receive sufficient power upfront, while vehicles with longer predicted durations receive appropriate baseline power levels.
Data Source
AI summary
An approach is provided for dynamically controlling power distribution amongst a plurality of charging station ports based on one or more objectives. A method includes obtaining input data, wherein the input data includes at least one of: user data associated with one or more current charging station users, or non-user data that is not associated with the current charging station users. The method includes processing the input data through one or more machine learning engines, wherein the one or more machine learning engines are trained to determine a particular power distribution, among the plurality of charging station ports, that achieves one or more objectives. The method includes configuring the plurality of charging station ports according to the particular power distribution. The particular power distribution specifies a maximum charging rate or a percentage of a power budget for each of the plurality of charging station ports.