Grid Control Module for EV Charging Load Redistribution
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Solution Overview
Problem
The electrical grid system faces strain during peak demand hours due to the simultaneous recharging of battery electric vehicles, and existing systems lack predictive capabilities to manage charging demand effectively, especially for mass transit vehicles with unpredictable stop locations and varying power needs.
Innovation Solution
A control module within the electrical grid system collects usage data from battery electric vehicles, including charge levels, locations, and itineraries, to dynamically redistribute electrical supply and suggest optimal recharging locations, anticipating load demands and adjusting power output based on factors like vehicle density, price, and environmental impact.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If battery electric vehicles are recharged simultaneously at peak electricity demand hours, then vehicle operators can maintain adequate charge levels, but the electrical grid system becomes strained and overloaded
Solution Approach 1:
The control module receives usage data including planned itineraries from vehicles and determines anticipated electrical loads in advance. Power is redistributed to recharging stations before the actual charging demand occurs, preventing grid overload while ensuring vehicles are charged when needed.
Solution Approach 2:
The system dynamically redistributes electrical supply on the grid based on real-time usage data and anticipated loads. The control module continuously adjusts power allocation to different sectors and recharging stations, transforming the static grid into a dynamic system that adapts to changing demand patterns.
2Power
If utilities offer discounted rates to encourage off-peak charging, then grid strain during peak hours is reduced, but vehicle operators may not have adequate charge levels when needed
Solution Approach 1:
The control module determines anticipated electrical loads based on received usage data including planned itineraries. By predicting when and where vehicles will need charging, the system proactively redistributes power to ensure charge availability without requiring vehicles to charge during off-peak discounted hours.
Solution Approach 2:
The system receives usage data from vehicles including current charge levels, locations, and planned itineraries. This feedback loop enables the control module to make informed decisions about power redistribution, balancing grid load management with ensuring vehicles have adequate charge for their specific needs.
3Productivity
If the electrical grid system redistributes power dynamically based on usage data, then load management is optimized and peak strain is reduced, but system complexity increases
Solution Approach 1:
The control module performs multiple functions: receiving usage data from vehicles, determining anticipated electrical loads in different sectors, redistributing electrical supply, and managing recharging stations. This multi-functional approach consolidates complexity into a single control entity rather than requiring separate systems for each function.
Data Source
AI summary
A method for recharging an electric vehicle includes receiving, by a control module and from the vehicle in transit along a route having a number of stops for passenger pickup, a current charge level and a current location. The stops include recharging stations for recharging the vehicle. The method includes receiving from a next stop, an anticipated stop time at the next stop for the vehicle. The method includes determining a power output to be supplied to the vehicle by a recharging station at the next stop based on the current charge level. The power output comprises an amount of power to be supplied at the next stop, the amount of power for satisfying a minimum amount of charge to enable the vehicle to arrive at a subsequent stop after the next stop. The method includes transmitting, to the recharging station at the next stop, the power output.