EV Charging Plan Creation for Peak Demand Leveling
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Solution Overview
Problem
Existing charging management systems for electric vehicles struggle to level the charging demand across facilities, leading to inefficiencies and increased costs due to variations in demand, which can result in the need for more expensive power sources during peak times.
Innovation Solution
A charging management method that predicts and adjusts the charging demand by obtaining location, battery, and history information to create a charging plan that keeps the demand within predetermined limits, allowing for optimal use of charging facilities and power sources, including autonomous travel and scheduling adjustments to avoid peak times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If charging is performed without demand prediction and leveling, then charging operations are simple, but charging demand varies significantly causing inefficiency and increased costs
Solution Approach 1:
The system performs preliminary actions by predicting future charging demands before they occur, creating charging plans in advance that level the demand. The prediction unit forecasts charging demands based on historical data and patterns, allowing the system to prepare optimal charging schedules that prevent peak demand situations before they happen.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual charging demands against predicted demands, and using this information to refine future predictions and adjust charging plans. The history information storage and prediction unit create a closed-loop system where past performance informs future decisions, improving efficiency while maintaining manageable complexity.
2Loss of energy
If charging demand is leveled to avoid peak times, then power source costs are reduced, but charging plan creation becomes more complex
Solution Approach 1:
The system creates charging plans in advance that specifically target off-peak charging times, performing the leveling action before the actual charging occurs. By predicting future demands and scheduling charges during low-cost periods, the system reduces power source costs while automating the complex planning process to manage the complexity burden.
Solution Approach 2:
The system changes the temporal parameters of charging operations, shifting charging schedules from peak demand periods to off-peak periods based on predicted patterns. This parameter adjustment (timing) directly reduces power source costs, while the automated prediction and planning algorithms manage the complexity of coordinating these changes across multiple facilities.
3Measurement precision
If real-time information processing is performed for all charging facilities, then charging demand prediction accuracy is improved, but system operational complexity increases
Solution Approach 1:
The system segments the information processing by dividing it into distinct functional units: acquisition of location/battery/history information, storage of history data, prediction of charging demands, and creation of charging plans. This segmentation allows real-time processing of critical data while distributing the complexity across modular components that can be managed independently.
Solution Approach 2:
The history information storage acts as an intermediary between raw data acquisition and prediction operations. By storing and structuring historical charging data, location data, and battery information, this intermediary component enables accurate real-time prediction without requiring all processing components to directly access and analyze raw data simultaneously, thus reducing system complexity while maintaining prediction accuracy.
Data Source
AI summary
A charging management method includes obtaining, predicting, and creating. In the obtaining, location information, battery information, and history information are obtained, the location information being information on the location of a traveling object powered by electricity, the battery information being information on the remaining capacity of a storage battery mounted on the traveling object, the history information being information on the charging history of the storage battery. In the predicting, the charging demand of at least one charging facility is predicted on the basis of the location information, the battery information, and the history information obtained in the obtaining. In the creating, a charging plan for charging the storage battery to keep the charging demand less than or equal to a predetermined value is created on the basis of the charging demand predicted in the predicting.