Edge EV Charging Session Control During Cloud Connectivity Loss
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Solution Overview
Problem
Existing EV charging infrastructure relies heavily on centralized cloud systems, leading to inefficiencies and degraded performance in areas with unreliable internet connectivity, resulting in poor driver experience and inefficient site utilization.
Innovation Solution
Implementing an edge computing architecture at each charging location to manage charging sessions locally, allowing stations to operate independently during network outages by storing session data and utilizing predefined policies for optimized charging parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If centralized cloud-based management is used for EV charging sessions, then system-wide optimization and advanced features are enabled, but charging services degrade when internet connectivity is lost
Solution Approach 1:
An edge computing device is introduced as an intermediary between the charging station and the cloud system. This edge device maintains local charging session management capabilities, allowing charging to continue uninterrupted during cloud connectivity loss while still enabling cloud-based optimization when connected.
Solution Approach 2:
The charging management system is segmented into distributed edge computing devices at each charging location rather than relying on a single centralized cloud system. Each edge device independently manages local charging sessions, providing resilience against cloud connectivity failures.
2Productivity
If centralized cloud systems are used for charging session management, then system-wide optimization is achieved, but data transmission overheads increase
Solution Approach 1:
The edge computing device performs local optimization of charging sessions using locally stored charging session data, eliminating the need for continuous cloud communication for routine optimization decisions. Only when connected does the system synchronize with the cloud for broader system-wide optimization.
3Adaptability or versatility
If centralized cloud-based management is implemented, then advanced centralized load management is enabled, but site outages have cascading effects on fleet-wide charging sites
Solution Approach 1:
The fleet-wide charging system is segmented into independent edge computing devices at each site, each capable of autonomous operation. This segmentation prevents cascading failures while maintaining the ability to coordinate load management across the fleet when cloud connectivity is available.
Solution Approach 2:
The edge computing device continuously monitors local charging conditions and cloud connectivity status, adjusting its operation mode accordingly. When connected, it receives system-wide load management instructions from the cloud; when disconnected, it autonomously manages local load based on stored policies and real-time conditions.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for managing electric vehicle charging sessions. In examples, techniques may include receiving, at an edge computing device associated with a charging site, a connection status of an electric vehicle from a charging station, wherein the edge computing device is located on-site at the charging site; initiating, by the edge computing device, a charging session based on the connection status by creating a charging session record to be stored at the edge computing device; determining optimized charging parameters for the electric vehicle; sending the optimized charging parameters from the edge computing device to the charging station; logging usage information locally at the edge computing device for the charging session; and maintaining operation of the charging station during an interruption in network connectivity between the edge computing device and a cloud computing system by utilizing data stored locally at the edge computing device.