EV Charging Scheduling With Abnormality Detection and Route Adjustment
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Solution Overview
Problem
Existing electric vehicle charging management systems face challenges in efficiently managing charging schedules and routes, particularly when abnormal charging operations occur, leading to delays, route adjustments, and increased operational complexity.
Innovation Solution
A network-connected electric vehicle charging management system that includes a server capable of scheduling charging operations, monitoring charging information, detecting abnormal conditions, generating warnings, and adjusting charging schedules and routes to mitigate disruptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time monitoring and automatic adjustment systems are implemented, then operational reliability and flexibility are improved, but device complexity increases
Solution Approach 1:
The system continuously monitors charging operation states and compares them against expected parameters. When abnormalities are detected (such as charging interruptions or deviations from scheduled times), the system automatically generates warnings and adjusts subsequent charging schedules. This closed-loop feedback mechanism ensures reliable operation while maintaining manageable system complexity through rule-based automatic responses.
Solution Approach 2:
The charging management system performs self-monitoring and self-adjustment without requiring constant human intervention. It autonomously detects charging abnormalities, sends warnings to relevant personnel, and automatically modifies charging schedules based on detected issues. This self-service capability improves reliability while keeping the system architecture relatively simple by eliminating the need for complex manual control interfaces.
2Loss of time
If manual monitoring and adjustment of charging operations is performed, then system complexity is reduced, but loss of time increases due to delayed detection and response to abnormal charging conditions
Solution Approach 1:
The system pre-establishes charging schedules with specific time points and parameters before charging operations begin. It proactively monitors whether actual charging operations deviate from these pre-planned parameters. By having predetermined expectations for normal operation, the system can immediately detect abnormalities and respond without waiting for manual assessment, thus reducing response time while maintaining a relatively simple monitoring framework.
Solution Approach 2:
The system continuously compares real-time charging status against expected parameters and provides immediate feedback when deviations are detected. This automatic feedback mechanism eliminates the time delay associated with manual monitoring and response, while the rule-based nature of the feedback logic keeps the system complexity manageable.
3Productivity
If charging operations are scheduled without flexibility for abnormalities, then scheduling simplicity is maintained, but productivity decreases due to route delays and vehicle underutilization
Solution Approach 1:
The charging schedule is designed as a dynamic rather than static plan. When the system detects charging abnormalities (such as interruptions or timing deviations), it automatically adjusts subsequent charging time points and parameters. This dynamic adaptation allows the system to respond to real-world conditions, maintaining high vehicle utilization rates without requiring overly complex manual rescheduling procedures. The scheduling complexity remains manageable through algorithmic automatic adjustment rather than manual intervention.
Solution Approach 2:
The system modifies charging schedule parameters (such as charging start times, duration, and sequence) based on detected operational abnormalities. By automatically changing these parameters in response to real-time conditions, the system maintains optimal vehicle utilization without requiring complex manual rescheduling. The parameter adjustment logic keeps scheduling system complexity at acceptable levels while significantly improving productivity.
Data Source
AI summary
Electric vehicle charging management methods and systems are provided. A server performs a charging scheduling operation for each electric vehicle charging station to determine a specific time point for each electric vehicle charging station to perform a charging operation in which the charging operation is to charge an electric vehicle coupled with the electric vehicle charging station. When the charging operation corresponding to each electric vehicle charging station is being performed, each electric vehicle charging station transmits charging information corresponding to the charging operation to the server through a network. The server determines whether the charging operation of specific electric vehicle charging station among the electric vehicle charging stations is abnormal based on the charging information received from each electric vehicle charging station, and generates a warning notification and sends the warning notification through the network when the charging operation of the specific electric vehicle charging station is abnormal.