EV Computing Sharing During Charging With Secure Task Isolation
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Solution Overview
Problem
Electric vehicles equipped with advanced computing resources for autonomous driving often have these resources idle during charging, posing challenges in balancing power usage, ensuring security, protecting privacy, and maintaining safety while utilizing their computing capabilities effectively.
Innovation Solution
A system that allocates computing tasks to electric vehicles based on their battery status and charging duration, using machine learning to predict parking times and balance power consumption, while ensuring security through isolation and validation of results, and providing incentives to owners for participating in computing resource sharing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If computing resources are utilized during charging, then productivity is improved, but reliability deteriorates due to power balance issues
Solution Approach 1:
The system predicts charging duration before allocating computing tasks. By using machine learning models to forecast how long the vehicle will remain connected to the charging station, the system can pre-select appropriate tasks that will complete within the predicted time window, ensuring tasks are allocated before the charging session ends and maintaining power balance.
Solution Approach 2:
The task allocation system dynamically adjusts task selection based on real-time charging status and predicted duration. As the charging session progresses and the vehicle's needs change, the system can modify or reassign tasks to maintain optimal utilization without compromising the vehicle's power requirements.
2Productivity
If computing tasks are allocated during charging, then productivity is improved, but security worsens due to system vulnerability
Solution Approach 1:
The system isolates computing tasks into separate containers or virtual environments that are sandboxed from the vehicle's critical systems. This segmentation allows external computing tasks to run simultaneously with vehicle operations without direct access to sensitive components, maintaining security while enabling productivity.
Solution Approach 2:
A task allocation server acts as an intermediary between external computing requests and the vehicle's computing resources. This intermediary layer validates, monitors, and manages task execution, providing a security buffer that protects the vehicle system while enabling productive use of computing resources.
3Productivity
If computing resources are shared, then productivity is improved, but loss of information increases due to privacy concerns
Solution Approach 1:
The system segments data access permissions so that computing tasks can utilize processing power without accessing sensitive vehicle or user information. Data is divided into public, protected, and private categories, with computing tasks only receiving access to necessary non-sensitive data, thereby maintaining privacy while enabling productivity.
4Productivity
If more computing resources are equipped, then productivity is improved, but device complexity increases
Solution Approach 1:
The task allocation server provides a universal management platform that can handle multiple types of computing tasks across different vehicles with varying hardware configurations. This multi-functional system simplifies the complexity by providing a standardized interface and automated task selection, allowing diverse computing resources to be managed through a single unified system.
Data Source
AI summary
An electric vehicle computing sharing system (100) is adapted to receive a signal indicating the electric vehicle (110, 120, 130) is connected to a charging station (115, 125, 135). The computing sharing system (100) may be further adapted to receive information about the electric vehicle (110, 120, 130). The computing sharing system (100) may be further adapted to determine a predicted charging duration (535) for the electric vehicle (110, 120, 130). The computing sharing system (100) may be further adapted to identify a task for execution by a computing resource of the electric vehicle (110, 120, 130) based on the predicted charging duration (535). The computing sharing system (100) may be further adapted to transmit the task to the electric vehicle (110, 120, 130). The computing sharing system (100) may be further adapted to receive a result for the task from the electric vehicle (110, 120, 130).