Fog-Cloud EV Charging Assignment for Grid-Aware Station Routing
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Solution Overview
Problem
Current electric vehicle charging management systems fail to effectively balance multiple factors such as charging station availability, wait times, energy costs, and grid stability, leading to inefficiencies and increased load on the power grid during peak hours.
Innovation Solution
A fog and cloud-based charging service application utilizing software-defined networking (SDN) to optimize electric vehicle charging station assignments by considering factors like energy requirements, travel distance, wait times, and energy credits, while minimizing total response time through a method that calculates optimal charging and discharging commands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a query-based approach is used to guide users to charging locations, then the system implementation is simple, but the search quality is limited and cannot provide cost savings and timely quality of service
Solution Approach 1:
The system transitions from a simple query-based approach to a multi-parameter optimization system that considers travel time, charging wait time, energy costs, and station availability simultaneously, fundamentally changing the decision parameters from single-factor to multi-factor optimization
Solution Approach 2:
The system implements dynamic real-time optimization by continuously monitoring charging station status, travel conditions, and energy prices to provide up-to-date routing recommendations, making the system adaptive rather than static
2Measurement precision
If all available charging stations are considered along with multiple factors, then the charging decision quality improves, but the computation capacity requirement increases
Solution Approach 1:
The system segments the computation tasks by separating real-time data collection from optimization calculation, and further divides the optimization into hierarchical levels (route planning vs. charging station selection), reducing the computational burden on any single component
Solution Approach 2:
The system introduces an intermediary optimization layer that aggregates data from multiple sources and pre-processes information before presenting options to users, reducing the direct computational complexity required at the user interface level
3Productivity
If charging stations are assigned without coordination, then the charging service availability is high, but the power grid stability deteriorates during peak hours
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring power grid conditions and charging station status, using this information to dynamically adjust charging recommendations and distribute load across different stations and time periods, preventing grid overload while maintaining service availability
Solution Approach 2:
The system performs preliminary optimization calculations to predict future charging demands and grid conditions, proactively assigning charging stations before peak loads occur, thereby preventing grid instability rather than reacting to it after it occurs
Data Source
AI summary
A system, method, and non-transitory computer readable medium that assigns an electric vehicle to a charging station is described. The system includes a software-defined networking (SDN) controller application stored in a cloud-based computing platform, a computing device stored in the cloud platform, and a fog and cloud-based charging service application stored in the computing device. The SDN controller application is linked to a plurality of fog servers and is configured to manage network communications between the fog servers and the cloud-based computing platform, between the fog and a number S of charging stations CSs, where s=1, 2, . . . , S, and between the fog and a number I of electric vehicles EVi, where I=1, 2, . . . , I. The fog and cloud-based charging service application determines an optimal charging station CSopt for each electric vehicle and transmits a route to the optimal charging station to the EVi.