EV Charging Schedule Optimization for Lower-Carbon Home Energy Use
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Solution Overview
Problem
Existing electric vehicle charging systems do not effectively optimize energy usage to minimize carbon footprint, as they lack integration with electricity grids and do not utilize bidirectional energy transfer capabilities, leading to inefficient use of renewable energy sources and increased greenhouse gas emissions.
Innovation Solution
A system that simulates and optimizes electric vehicle charging schedules using genetic algorithms and AI/ML models, integrating grid, vehicle, and home data to predict and influence energy demands and supplies, enabling bidirectional energy transfer to achieve a 100% renewable energy portfolio.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If electric vehicle charging systems operate independently without grid integration, then system simplicity is maintained, but carbon footprint reduction capability is limited
Solution Approach 1:
The patent merges the EV charging system with the electricity grid and home energy management system, creating an integrated bidirectional energy transfer platform. This combination enables the system to access real-time grid carbon intensity data and coordinate charging with renewable energy availability, thereby reducing carbon footprint while maintaining manageable complexity through unified control architecture
Solution Approach 2:
The charging system is designed to perform multiple functions: it can charge EVs, discharge to home, interact with grid, and optimize based on carbon intensity. This multi-functionality allows the same infrastructure to serve various purposes (charging, energy storage, grid support) and achieve carbon reduction without requiring separate dedicated systems
2Object-generated harmful factors
If charging systems utilize bidirectional energy transfer and AI optimization, then carbon footprint is reduced, but system complexity increases
Solution Approach 1:
The system employs AI/ML models that automatically analyze grid data, predict carbon intensity patterns, and optimize charging schedules without requiring complex manual configuration or user intervention. The self-learning algorithms adapt to changing conditions and automatically adjust operations, reducing the need for complex control logic while achieving effective carbon footprint reduction
Solution Approach 2:
The system performs preliminary analysis of grid carbon intensity data and predicts future renewable energy availability before making charging decisions. By pre-processing data and anticipating optimal charging windows, the system simplifies real-time control complexity while maintaining effective carbon footprint reduction through proactive scheduling
3Object-generated harmful factors
If EV charging is optimized for carbon reduction, then renewable energy utilization increases, but charging flexibility decreases
Solution Approach 1:
The charging schedule is dynamically adjusted based on real-time grid conditions, carbon intensity variations, and user needs. The system can flexibly modify charging rates, timing, and discharge operations in response to changing renewable energy availability and user requirements, maintaining charging flexibility while maximizing renewable energy utilization through adaptive control
Data Source
AI summary
Total charging time to charge an EV from a starting SOC to a target SOC is determined based on vehicle related data. Home energy usage of a home in an EV availability time window is also determined based on home related data. EV idle power consumptions for an EV charging mode, an EV discharging mode, and an EV idle mode with neither charging nor discharging are further determined. A genetic algorithm is set up with cost and penalty functions. These functions are built, for each candidate schedule in a solution space, based on the starting state of charge, the home energy usage and the EV idle power consumptions. The genetic algorithm is run to generate an optimized schedule that includes schedule values for the time slots in the EV availability time window to control whether the EV is to charge, discharge or idle in each of these time slots.