Adaptive EV Charging Network Optimizes Grid Load
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Solution Overview
Problem
The increasing adoption of electric vehicles (EVs) poses a significant stress load on the smart grid due to their high power demand, which existing charging infrastructure is unable to manage efficiently, leading to potential grid overload and inefficiencies in energy distribution.
Innovation Solution
An adaptive charging network utilizing centralized computing systems and node controllers that employ quadratic programming to optimize charging rates for EVs based on energy demands, departure times, and available resources, ensuring efficient energy distribution and minimizing grid stress.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If EV charging infrastructure is expanded to meet increasing EV adoption, then EV charging capability is improved, but grid stress and power demand load increase
Solution Approach 1:
The charging network dynamically adjusts charging rates based on real-time grid conditions, EV battery states, and predicted arrivals. The system transitions from static charging schedules to dynamic optimization, allowing charging rates to adapt continuously to changing conditions, thereby meeting EV charging demands while preventing grid overload
Solution Approach 2:
The system performs preliminary optimization calculations before peak demand periods by predicting EV arrivals and pre-scheduling charging sessions. The centralized computing system calculates optimal charging rates in advance based on forecasted grid conditions and EV battery states, enabling proactive load management rather than reactive responses to grid stress
2Speed
If charging rates are increased to meet EV energy demands, then charging speed is improved, but infrastructure wear and tear increases
Solution Approach 1:
The system optimizes charging parameters including voltage, current, and power delivery rates based on EV battery characteristics, state of charge, and temperature conditions. By dynamically adjusting these parameters, the system achieves fast charging when appropriate while using gentler charging profiles when infrastructure stress or battery conditions warrant more conservative rates, balancing speed with durability
3Productivity
If centralized control system is implemented to optimize charging, then energy distribution efficiency is improved, but system complexity increases
Solution Approach 1:
The patent introduces a centralized computing system that acts as an intermediary between the power grid and distributed EV charging infrastructure. This intermediary coordinates charging sessions, optimizes power distribution, and manages communication between grid operators and charging stations, thereby achieving efficient centralized control while maintaining modular, standardized interfaces that limit operational complexity
Solution Approach 2:
The system implements continuous feedback loops where charging station data on power delivery, EV battery states, and grid conditions are transmitted to the centralized computing system. The system processes this feedback and adjusts charging rates in real-time, creating a self-regulating control mechanism that improves efficiency while using standardized feedback protocols to manage system complexity
Data Source
AI summary
Adaptive charging networks in accordance with embodiments of the invention enable the optimization of electric design of charging networks for electric vehicles. One embodiment includes an electric vehicle charging network, including one or more centralized computing systems, a communications network, several, electric vehicle node controllers for charging several electric vehicles (EVs), where the one or more centralized computing systems is configured to: receive the electric vehicle node parameters from several electric vehicle node controllers, calculate a charging rates for the electric vehicle node controllers using quadratic programming (QP), where the quadratic programming computes the charging rates based on the electric vehicle node parameters, adaptive charging parameters and a quadratic cost function, and distributes the charging rates to the electric vehicle node controllers.