EV Energy Transactions Using AI Demand Prediction Modes
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Solution Overview
Problem
Existing vehicle systems lack efficient methods for optimizing energy transactions based on energy demand and environmental impact, particularly in electric vehicles, which can enhance energy distribution and storage strategies.
Innovation Solution
Electric vehicles are equipped with AI models that predict future energy demand, allowing them to operate in cost-optimization or environmental-optimization modes, and execute energy transactions such as distributing to the grid, transferring to other EVs, or storing renewable energy, based on these predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If electric vehicles store and distribute energy based on predicted demand, then energy management efficiency is improved, but system complexity increases due to AI models and multiple operational modes
Solution Approach 1:
The AI model predicts future energy demand in advance, allowing the EV to proactively determine operational modes and execute energy transactions before actual demand occurs. This preliminary action enables optimized energy management by preparing energy distribution strategies ahead of time based on forecasted conditions.
Solution Approach 2:
The system dynamically switches between cost-optimization mode and environmental-optimization mode based on real-time energy demand conditions and AI predictions. This dynamic adaptability allows the EV to optimize energy transactions according to varying operational requirements, balancing complexity with improved energy management efficiency.
2Loss of energy
If electric vehicles execute energy transactions based on AI predictions, then cost optimization is improved, but measurement precision requirements increase for energy demand prediction
Solution Approach 1:
The system uses AI models to continuously predict energy demand and receives feedback on actual energy demand conditions. This feedback loop allows the EV to refine its predictions and adjust operational modes to achieve better cost optimization while managing the precision requirements through iterative improvement.
Solution Approach 2:
The AI model analyzes multiple parameters including energy demand patterns, cost factors, and environmental conditions to make prediction decisions. By changing and optimizing these parameters, the system achieves cost optimization while managing the complexity of measurement precision requirements through multi-parameter analysis.
3Object-affected harmful factors
If electric vehicles operate in environmental-optimization mode, then environmental impact is reduced, but energy transaction flexibility decreases
Solution Approach 1:
The system dynamically switches between cost-optimization mode and environmental-optimization mode based on real-time conditions and AI predictions. This dynamic capability allows the EV to reduce environmental impact when operating in environmental-optimization mode while maintaining overall transaction flexibility through mode switching, resolving the contradiction between environmental benefits and operational versatility.
Data Source
AI summary
An example operation includes one or more of receiving, by an electric vehicle (EV), an energy demand of an electrical grid, wherein the EV is configured to store energy and to distribute the energy, determining, by the EV, an operational mode based on the energy demand, wherein the operational mode is at least one of a cost-optimization mode or an environmental-optimization mode, and executing, by the EV, an energy transaction based on the operational mode, wherein the executing comprises at least one of: distributing the energy from the EV to the electrical grid, transferring the energy from the EV to another EV, or storing by the EV additional energy from a renewable energy source, wherein a timing of the energy transaction is based on an execution of an artificial intelligence (AI) model that predicts a future energy demand of the electrical grid.