EV Charging Signal Disaggregation from Whole-House Consumption
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Solution Overview
Problem
Electric vehicle owners face increased electricity bills due to frequent charging, and existing systems lack an effective method to accurately detect and disaggregate EV charging signals from whole-house consumption profiles, making it difficult to understand and manage energy costs.
Innovation Solution
The method involves an electronic processor that identifies potential EV charging intervals using long and decreasing patterns, evaluates candidates with parametric models, determines initial charging points through signal processing, and accounts for user feedback to accurately detect and disaggregate EV charging signals from whole-house profiles, employing techniques like sliding windows, dynamic programming, and alpha-beta pruning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If EV charging signals are detected from whole-house consumption profiles, then users can understand energy costs and optimize charging times, but the complexity of signal processing and disaggregation increases significantly
Solution Approach 1:
The patent segments the whole-house consumption signal into individual appliance-level signals by identifying characteristic patterns. The system divides the complex aggregate signal into separable components representing different devices, including the EV charger, by analyzing temporal patterns, power levels, and usage behaviors of individual appliances within the household.
Solution Approach 2:
The patent introduces an intermediary processing layer that translates raw consumption data into meaningful energy cost information. This intermediary system uses pattern recognition algorithms and appliance profiles to bridge the gap between raw electrical signals and user-understandable energy consumption data, enabling cost calculation without requiring direct access to utility billing systems.
2Measurement precision
If advanced signal processing techniques are used to accurately detect EV charging intervals, then detection precision improves, but computational time and processing resources increase
Solution Approach 1:
The patent applies preliminary action by pre-processing the consumption signal to identify and remove obvious non-charging patterns before performing detailed EV charging detection. The system performs initial filtering, baseline estimation, and obvious pattern recognition beforehand, which reduces the computational burden and processing time required for the subsequent precise charging interval detection algorithms.
Data Source
AI summary
The present invention is directed to systems and methods of disaggregating and detecting energy usage associated with electric vehicle charging from a whole-house consumption signal. In general, methods of the present invention may include: identifying by an electronic processor potential interval candidates of electric vehicle charging, based at least in part upon long and decreasing patterns; determining by the electronic processor intervals associated with the charging of an electric vehicle, based at least in part on evaluating each potential interval candidate; determining by the electronic processor an initial point of charging for each interval associated with the charging of an electric vehicle; and accounting by the electronic processor for feedback of any incorrectly detected signals.