EV Charging Station Battery Scheduling for Peak Grid Support
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Solution Overview
Problem
Conventional electric vehicle charging stations face challenges in managing peak recharge times, leading to increased charging times for EVs and potential strain on the power grid, which can result in inefficient energy use and battery degradation.
Innovation Solution
A system that employs machine learning to predict peak recharge time intervals for EVs within a threshold distance, allowing charging stations to pre-charge their batteries before peak demand, thereby supporting the power grid during peak usage and extending battery life by discharging during low usage periods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If charging stations charge batteries at peak demand times, then EVs can be recharged quickly, but the power grid experiences excessive strain and energy efficiency decreases
Solution Approach 1:
The system performs preliminary charging of station batteries during off-peak hours when grid demand is low. The power needs engine predicts future peak demand intervals, and the charge control module schedules charging to occur before these peaks, allowing the station to serve EVs during peak times without drawing excessive power from the grid.
2Reliability
If charging stations have larger battery capacity to support peak demand, then power grid support is improved, but device complexity and cost increase
Solution Approach 1:
The system dynamically adjusts the charging and discharging of station batteries based on real-time grid conditions and predicted EV charging demands. The power needs engine continuously monitors and predicts demand patterns, allowing the station to optimize its battery utilization without requiring excessive battery capacity, thus maintaining reliability while controlling complexity.
3Loss of time
If charging stations operate without predictive scheduling, then system simplicity is maintained, but charging time for EVs increases during peak periods
Solution Approach 1:
The system implements a feedback loop where the power needs engine continuously monitors EV SoC data, charging patterns, and grid conditions to predict future peak demand. This predictive feedback enables the charge control module to proactively schedule charging operations, reducing EV charging times during peak periods while maintaining manageable automation through rule-based control logic.
Data Source
AI summary
A power needs engine predicts a peak recharge time interval for a charging points of charging stations based on state of charge (SoC) data for electric vehicles (EVs) that are within a threshold distance. The SoC data characterizes an SoC of batteries of the EVs. A charge control module creates and/or updates charging schedules for the charging points of the charging stations based on the charge time and the peak recharge time interval for the charging points of the charging stations. The charge control module also provides the charging schedules to computing platforms of the charging points of the charging stations. The computing platforms cause the batteries of the charging points to charge and discharge according to a corresponding charging schedule of the charging schedules.