EV Charging Optimization via AI Carbon Forecasting
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Solution Overview
Problem
Electric vehicles (EVs) often charge using electricity generated from fossil fuel sources due to a lack of direct influence from EVs on electricity grids, leading to increased carbon footprints, as existing systems operate independently and lack feedback loops to optimize energy usage and minimize greenhouse gas emissions.
Innovation Solution
Implementing AI/ML prediction models that connect EVs with electricity grids to forecast energy demands and emissions, optimizing charging schedules to align with lower carbon intensity periods, thereby influencing energy consumption behaviors and reducing overall carbon footprints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If EVs charge using traditional electricity grids without optimization, then charging convenience is maintained, but carbon footprint increases due to fossil fuel dependency
Solution Approach 1:
The system performs preliminary actions by predicting future electricity generation mix and carbon intensity levels before charging occurs. The optimization module pre-calculates charging schedules that align with periods of lower carbon intensity, allowing EVs to charge during optimal times without requiring user intervention or compromising convenience.
Solution Approach 2:
The system implements feedback loops by continuously monitoring actual electricity generation data from renewable and fossil fuel sources, comparing it against predicted values, and using this information to refine future charging schedule predictions. This feedback mechanism enables the system to adapt to changing grid conditions and improve carbon footprint reduction over time while maintaining charging convenience.
2Object-affected harmful factors
If AI/ML prediction models are implemented to optimize charging schedules, then carbon emissions are reduced, but system complexity increases
Solution Approach 1:
The patent introduces an optimization module as an intermediary layer between the EV charging system and the electricity grid. This module contains the AI/ML prediction models and processes, acting as a mediator that translates complex predictions into simple charging schedule recommendations. The intermediary structure isolates the complexity within a dedicated component, making the overall system more manageable and maintainable while achieving emission reduction goals.
3Object-affected harmful factors
If charging schedules are optimized to align with lower carbon intensity periods, then emissions are decreased, but charging time flexibility is reduced
Solution Approach 1:
The system implements dynamic charging schedules that adapt to real-time and predicted future grid conditions. Rather than fixed schedules, the optimization module continuously adjusts charging timing based on forecasted carbon intensity levels, renewable energy availability, and EV-specific parameters such as driver behavior patterns and vehicle usage schedules. This dynamic approach maintains flexibility while achieving emission reduction targets.
Solution Approach 2:
The system changes multiple parameters simultaneously to optimize charging schedules, including timing, duration, and power level. By adjusting these parameters based on predicted carbon intensity and renewable energy availability, the system can shift charging to optimal periods without significantly impacting user convenience. The multi-parameter optimization allows for flexible scheduling that accommodates various driver needs while minimizing emissions.
Data Source
AI summary
Approaches, techniques, and mechanisms are disclosed for improving end-to-end carbon footprint of electric vehicles. It is determined that an electric vehicle is requesting battery charging by a charging station. A current state of charge for the electric vehicle is used to estimate a charging time duration for the electric vehicle to reach a target state of charge. Multiple candidate time durations are generated within an available time period between a start time and an end time by a demand and footprint optimization system, each candidate time duration in the multiple candidate time durations within the available time period being no shorter than the charging time duration used to charge the electric vehicle from the current state of charge to the target state of charge. The charging time duration for battery charging is scheduled within a specific candidate time duration that is selected from among the multiple candidate time durations.