EV Charging Device Power Regulation via Forecast Profiles
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional electric vehicle charging devices are inflexible and computationally intensive, struggling to effectively regulate power delivery in real-time due to limitations in monitoring and processing electrical consumption data from other equipment connected to the same electrical delivery point, leading to inefficiencies and potential overloading of the electrical network.
Innovation Solution
A charging device equipped with an optimization module to construct a charging profile based on electrical consumption forecasts and a regulation module that switches between two operating modes to adjust power delivery according to measured consumption data, optimizing power output to minimize network load and ensure efficient energy distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time monitoring and processing of electrical consumption data is implemented to regulate power delivery, then power regulation accuracy is improved, but device complexity and computing requirements increase
Solution Approach 1:
The optimization module constructs a charging profile in advance based on forecasted electrical consumption data before the actual charging process begins. This preliminary action allows the system to pre-determine the optimal power delivery schedule, reducing the need for complex real-time processing while maintaining regulation accuracy.
Solution Approach 2:
The regulation module dynamically switches between two operating modes: using the pre-constructed charging profile during normal operation, and transitioning to real-time data-based regulation when discrepancies are detected. This dynamic approach balances computational efficiency with regulation accuracy adaptively.
2Power
If conventional real-time regulation based on measured consumption data is used, then power delivery is controlled, but the approach is rigid and computationally intensive
Solution Approach 1:
The system constructs a charging profile in advance that incorporates forecasted consumption patterns, allowing flexible adaptation to expected conditions without requiring complex real-time calculations. This preliminary planning enables the system to be both computationally efficient and adaptable to varying conditions.
Solution Approach 2:
The regulation module continuously monitors actual consumption data and compares it with forecasted values. When significant deviations are detected, the system provides feedback to switch operating modes, thereby adapting to actual conditions while maintaining overall regulatory control and flexibility.
3Ease of operation
If charging devices operate independently without considering other equipment consumption, then device operation is simple, but electrical network overload occurs
Solution Approach 1:
The optimization module constructs a charging profile that incorporates forecasted consumption data from other equipment connected to the same delivery point. By doing this in advance, the system accounts for network constraints without requiring complex real-time coordination, thus maintaining operational simplicity while preventing network overload.
Data Source
Figure 1
Figure 2A~2B
Figure 3
AI summary
The invention relates to a charging device which comprises an optimisation module (OPT) configured to build a charging profile representing a first electric charging power, and a regulation module (REG) having a first operating mode in which said module regulates the output electric power supplied in order to match said power with the first electric power, and a second operating mode in which said module regulates the output electric power supplied in order to match said power with a second electric charging power, the regulation module (REG) being configured to switch between the first mode and the second mode in response to the verification of at least one condition defined as a function of the forecast electricity consumption of the other devices and data of the measured electricity consumption of said other devices.