Wireless Energy Mesh Routing for EV and Building Power Sharing
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Solution Overview
Problem
Electric vehicles and smart buildings lack the ability to wirelessly share energy during battery power shortages or power outages, posing a risk of battery damage and inconvenience.
Innovation Solution
An ad hoc wireless mesh network is formed using machine learning models to identify source and sink nodes, enabling energy transfer between electric vehicles and smart buildings while adhering to IEEE guidelines and managing energy loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If wireless energy transfer is implemented between electric vehicles and smart buildings, then energy sharing capability is improved, but system complexity increases
Solution Approach 1:
The system segments the energy network into discrete nodes (electric vehicles and smart buildings) that can independently participate in energy sharing. Each node is equipped with specific components (transmitters, receivers, controllers) that enable modular energy transfer capabilities without requiring complete system redesign
Solution Approach 2:
The wireless energy transfer system is designed to serve multiple functions: electric vehicles can both consume and supply energy, smart buildings can store and distribute energy, and the same infrastructure supports various transfer scenarios (vehicle-to-vehicle, vehicle-to-building, building-to-building)
2Productivity
If machine learning models are used to form ad hoc wireless mesh networks and identify source and sink nodes, then energy transfer efficiency is improved, but computational requirements and processing time increase
Solution Approach 1:
Machine learning models are trained in advance on historical energy consumption and generation patterns. This preliminary training enables the models to quickly make predictions about optimal energy transfer scenarios without requiring extensive real-time computation when actual energy sharing is needed
Solution Approach 2:
The system continuously monitors energy transfer performance and feeds this information back to the machine learning models. This feedback loop allows the models to refine their predictions and optimize energy transfer efficiency over time based on actual system behavior and changing conditions
3Ease of operation
If wireless energy transfer is implemented without physical connection, then convenience and operational flexibility are improved, but energy loss during transmission increases
Solution Approach 1:
The system merges multiple energy transfer paths through mesh networking, where energy can be routed through intermediate nodes. This combination of paths allows the system to select optimal routes that minimize energy loss while maintaining wireless operational flexibility
Solution Approach 2:
The system dynamically adjusts transmission parameters (power levels, frequency, modulation) based on real-time conditions such as distance between nodes, environmental factors, and energy demands. These parameter changes optimize the balance between maintaining operational flexibility and minimizing energy loss during wireless transmission
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Ensures battery safety and maintains vehicle functionality by predicting energy needs and preventing overheating, allowing vehicles to reach destinations without wired charging and buildings to operate during outages.
Implementation Method 1
The computer of the energy node, utilizing a transceiver, transfers energy wirelessly from the energy node as the source energy node to the sink energy node via the ad hoc wireless mesh network
Implementation Method 2
In near field wireless energy transfer, power is transferred over short distances by the electromagnetic field using inductive coupling between electrical coils of wire
Data Source
AI summary
Intelligent wireless energy sharing is provided. An ad hoc wireless mesh network that includes a plurality of energy nodes is formed using a set of machine learning models. The plurality of energy nodes is comprised of the energy node and a set of other energy nodes. A source energy node and a sink energy node in the plurality of energy nodes is identified using the set of machine learning models in response to forming the ad hoc wireless mesh network. Energy is wirelessly transferred from the energy node as the source energy node to the sink energy node via the ad hoc wireless mesh network utilizing a transceiver.