Adaptive EV Charging Using Aggregate Power Signature Detection
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Solution Overview
Problem
The complexity of charging electric vehicles (EVs) is increased by the intermittent nature of renewable energy sources and fluctuations in energy demand, leading to inefficiencies and higher costs in energy transfer, especially when charging fleets of EVs or managing energy demands alongside other loads.
Innovation Solution
Adaptive charging controllers (ACCs) and adaptive charging managers (ACMs) are used to sense and analyze aggregate power waveforms, identifying contributing power sources and making decisions on when and how much to charge EVs based on available renewable and non-renewable energy sources, optimizing energy use and cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If electric vehicles are charged during periods of high energy demand, then charging infrastructure utilization is improved, but energy costs increase and grid stability deteriorates
Solution Approach 1:
The system dynamically adjusts charging rates based on real-time aggregate power waveform analysis, transitioning from static charging schedules to adaptive charging that responds to changing grid conditions and renewable energy availability
Solution Approach 2:
The system uses feedback from analyzing aggregate power waveforms to identify contributing power sources and adjust charging decisions, creating a closed-loop control system that optimizes charging based on actual grid conditions
2Loss of energy
If electric vehicles are charged when renewable energy sources are available, then energy costs are reduced, but charging reliability deteriorates due to intermittent energy supply
Solution Approach 1:
The system performs preliminary analysis of aggregate power waveforms to predict when renewable energy sources will be available, allowing advance scheduling of charging to maximize use of low-cost renewable energy while ensuring reliability
Solution Approach 2:
The system changes charging parameters (rate, timing, duration) based on identified power source characteristics, adapting charging behavior to match the intermittent nature of renewable sources while maintaining overall charging goals
3Productivity
If adaptive charging controllers analyze aggregate power waveforms to identify power sources, then charging efficiency is improved, but system complexity increases
Solution Approach 1:
The adaptive charging controller performs multiple functions including waveform analysis, power source identification, charging decision-making, and real-time control adjustment, consolidating these capabilities into a single multi-functional device rather than separate systems
Data Source
AI summary
The present disclosure describes a device for providing adaptive charging of an electric vehicle (EV). The device includes a memory and at least one processor configured for receiving a set of parameters of an aggregate power waveform carried on a power line from a set of sensors; receiving a set of relationships between a set of power signatures and a set of power sources; determining, using the set of parameters and the set of power signatures, a subset of the set of power signatures represented in the set of parameters; identifying, using the subset of the set of power signatures and the set of relationships between the set of power signatures and the set of power sources, a subset of the set of power sources contributing to the aggregate power waveform; and controlling, responsive to identifying identified subset of the set of power sources, a charging circuit.