EV Charging Control Using Grid Capacity and Demand Priorities
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Solution Overview
Problem
Charging electric vehicles poses challenges due to varying energy requirements and power sources, leading to inefficient charging times and increased demand charges during peak hours, which can overload the power grid and result in higher electricity costs for consumers.
Innovation Solution
An automated power charging system that calculates the power grid capacity and demand level of electric vehicles, allowing for the selection of charging modes such as an override mode or internal combustion engine mode to optimize charging based on grid capacity and redistribute load increments according to predetermined priorities, thereby managing peak demand and reducing overload conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If electric vehicles are charged during peak demand periods, then charging speed and user convenience are improved, but power grid overload and demand charges increase
Solution Approach 1:
The system performs preliminary actions by pre-charging electric vehicles during off-peak hours when grid capacity is available, before the actual need for power arises. The charge controller monitors grid capacity predictions and schedules charging operations in advance to avoid peak demand periods, thus preventing grid overload while ensuring vehicles are ready when needed.
Solution Approach 2:
The charging system dynamically adjusts its operation based on real-time grid capacity conditions and predictions. The charge controller continuously monitors actual and predicted grid capacity, modifying charging rates and schedules adaptively. This dynamic adjustment allows the system to optimize between charging speed and grid load management, preventing overload while maintaining operational convenience.
2Productivity
If multiple electric vehicles are charged simultaneously, then overall energy distribution efficiency is improved, but peak demand increases causing higher electricity costs
Solution Approach 1:
The system schedules charging operations in advance during off-peak periods when electricity rates are lower and grid capacity is available. By performing preliminary charging before peak demand periods, the system distributes energy efficiently across multiple vehicles without incurring high peak demand charges, thus reducing overall electricity costs while maintaining productivity.
Solution Approach 2:
The charge controller implements load management by selectively charging subsets of vehicles based on priority levels and grid capacity availability. Rather than charging all vehicles simultaneously at full power, the system applies partial charging actions to multiple vehicles in a coordinated manner, distributing the load to avoid peak demand charges while still meeting overall energy distribution goals.
3Loss of time
If charging rate is increased to reduce charging time, then user convenience is improved, but demand charge and grid stress increase
Solution Approach 1:
The system performs charging operations in advance during off-peak periods when grid capacity is abundant and electricity rates are lower. By scheduling charging before peak demand periods, the system can use higher charging rates without incurring peak demand charges, thus reducing charging time while avoiding increased electricity costs.
Solution Approach 2:
The charge controller dynamically adjusts charging rates based on real-time and predicted grid capacity conditions. When grid capacity is available, the system increases charging rates to reduce charging time. When grid capacity is constrained or rates are high, the system modulates charging rates accordingly. This dynamic control optimizes the balance between charging speed and demand charge management.
Data Source
AI summary
A system is provided for performing an automated power charging process for one or more electric vehicles using a processor. Included in the processor is a charge controller that calculates a capacity of a power grid system by communicating with the power grid system via a network, and a power demand level of the one or more electric vehicles to satisfy one or more mission requirements of each electric vehicle. The power demand level of the one or more electric vehicles is compared with the capacity of the power grid system. In response to the comparison, at least one charging mode is selected from an override mode and an internal combustion engine mode for performing the automated power charging process. The charge controller automatically charges the one or more electric vehicles based on the selected at least one charging mode.