Adaptive EV Charging Algorithms for Infrastructure Bottlenecks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The existing electrical infrastructure in parking garages and office parks is inadequate to support a large number of electric vehicle charging stations, leading to a bottleneck in the distribution of electricity, as it was originally designed for air circulation and lighting loads, making it difficult to install a meaningful number of EV chargers without exceeding power capacity or incurring high costs.
Innovation Solution
Implementing an adaptive charging system that uses centralized computing systems and electric vehicle node controllers to dynamically adjust charging rates, optimizing power distribution by calculating and allocating charging rates based on energy demand, departure times, and cost functions, ensuring fair allocation and maximizing asset utilization while staying within the power capacity of the infrastructure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a large number of EV chargers are installed in existing parking structures, then charging capacity and EV adoption are improved, but the existing electrical infrastructure cannot support the additional power load
Solution Approach 1:
The system dynamically adjusts charging rates in real-time based on current power availability, EV charging needs, and grid conditions. Each charger can operate at variable power levels rather than fixed maximum capacity, allowing the system to accommodate more chargers by flexibly distributing available power across multiple vehicles simultaneously.
Solution Approach 2:
The invention changes the operating parameters of the charging system by transitioning from static, high-power charging to dynamic, adaptive power distribution. The system monitors and adjusts charging parameters (power rate, timing, duration) based on real-time conditions, enabling existing infrastructure to support higher numbers of chargers through optimized parameter management.
2Quantity of substance
If the power capacity of the parking structure is increased to support more EV chargers, then charging capacity is improved, but the cost of infrastructure upgrades becomes extremely expensive
Solution Approach 1:
The system enables self-service charging where EVs are charged during periods of low demand or excess supply without requiring infrastructure upgrades. The adaptive algorithm automatically utilizes available power capacity during off-peak hours or when renewable generation is high, allowing the system to serve more vehicles using existing infrastructure resources.
Solution Approach 2:
The system performs preliminary charging actions during periods when power is abundant and inexpensive, preparing vehicles for later use. By charging EVs in advance during off-peak hours or when renewable energy is available, the system reduces the need for peak capacity expansion and avoids expensive infrastructure upgrades.
3Speed
If charging rates are increased to meet EV charging deadlines, then charging speed is improved, but the power capacity of the existing infrastructure is exceeded
Solution Approach 1:
The system dynamically adjusts charging rates based on real-time power availability and EV needs. When power capacity is available, charging rates are increased to meet deadlines; when capacity is constrained, rates are adjusted downward. This dynamic adaptation allows the system to maximize charging speed within the limits of existing infrastructure.
Solution Approach 2:
The system employs periodic charging cycles, alternating between high-rate charging when power is available and lower-rate charging or waiting periods when capacity is constrained. This periodic action allows the system to make progress on charging multiple vehicles over time without consistently exceeding power capacity limits.
Data Source
AI summary
Electric vehicle node controllers in accordance with embodiments of the invention enable adaptive charging. One embodiment includes one or more centralized computing systems; a communications network; a plurality of electric vehicle node controllers, where each electric vehicle node controller in the plurality of node controllers contains: a network interface; a processor; a memory containing: an adaptive charging application; a plurality of electric vehicle node parameters describing charging parameters of an electric vehicle node in the electric vehicle charging network; where the processor is configured by the adaptive charging application to: send electric vehicle node parameters to the one or more centralized computing systems; and charge the electric vehicle node using a charging rate received from the one or more centralized computing systems; where the one or more centralized computing systems is configured to: receive the electric vehicle node parameters from the plurality of electric vehicle node controllers; calculate a plurality of charging rates for the plurality of electric vehicle node controllers using the electric vehicle node parameters, a plurality of adaptive charging parameters, and a cost function; and send the charging rates to the plurality of electric vehicle node controllers.