Bidirectional EV Charging Schedules for Lower-Carbon Grid Intervals
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Solution Overview
Problem
Electric vehicles (EVs) contribute to greenhouse gas emissions due to their reliance on electricity from fossil fuel sources, as existing technologies lack a direct influence on energy demand and supply to optimize carbon footprint reduction.
Innovation Solution
A carbon footprint optimization system that uses prediction models and bidirectional energy transfer capabilities to schedule charging and discharging events based on carbon intensity, leveraging AI and ML to minimize emissions by charging EVs during low-carbon intervals and using EVs to supply energy during high-emission periods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If EVs charge from grid electricity generated from fossil fuels, then electricity demand is satisfied, but greenhouse gas emissions increase
Solution Approach 1:
The system performs preliminary actions by scheduling EV charging during periods when renewable energy generation is predicted to be high and fossil fuel generation is low. The optimization system forecasts carbon intensity in advance and pre-schedules charging events to coincide with low-carbon energy availability, thereby reducing emissions before they occur.
Solution Approach 2:
The system dynamically adjusts charging schedules based on real-time and forecasted carbon intensity data from the grid. Rather than using fixed charging times, the optimization algorithm continuously adapts charging events to match varying carbon intensity conditions, enabling EVs to charge when the grid is cleanest and discharge when carbon intensity peaks.
2Loss of energy
If AI/ML techniques optimize fuel economy in HEVs, then fuel consumption is reduced, but the system only influences limited parameters without direct control on energy demand and supply
Solution Approach 1:
The system implements feedback loops by continuously monitoring actual carbon intensity data from the grid and comparing it against predictions. The optimization algorithm uses this feedback to refine future charging and discharging schedules, creating a closed-loop system that adapts to actual grid conditions rather than relying solely on forecasts.
Solution Approach 2:
The optimization system enables EVs to serve themselves by autonomously determining optimal charging and discharging times based on carbon intensity forecasts and user-defined preferences. The system self-adjusts scheduling parameters without requiring manual intervention, and EVs effectively serve the dual purpose of meeting their own energy needs while contributing to grid stabilization during high-carbon periods.
3Productivity
If EVs charge during periods of high fossil fuel generation, then electricity demand is met, but carbon footprint increases
Solution Approach 1:
The system changes the temporal parameter of energy consumption by shifting charging events from periods of high fossil fuel generation to periods of low carbon intensity. The optimization algorithm transforms the when and how of energy uptake, converting fixed or arbitrary charging schedules into dynamically adjusted events that align with renewable energy availability and low-carbon grid conditions.
Data Source
AI summary
Approaches, techniques, and mechanisms are disclosed for improving carbon footprint of electric vehicles and/or homes. A time window when an electric vehicle connects with a charging station is determined. The charging station is connected with a grid from which the charging station is configured to draw electricity to charge the electric vehicle. An electricity demand of the electric vehicle is predicted based on a current state of charge (SoC) of batteries of the electric vehicle. Costs for drawing electricity from the grid during time intervals are computed. The time window is partitioned into a plurality of time intervals including the time intervals. An optimized schedule for performing operations with the batteries is generated based on the costs. The operations include those used to charge the electric vehicle to satisfy the predicted electricity demand of the electric vehicle.