EV Charger Reservation Control Using State-of-Charge Prediction
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Solution Overview
Problem
Current charging systems lack an efficient reservation system, making it difficult for drivers to locate available charging stations when the vehicle's charge level drops below a threshold, potentially leaving them stranded.
Innovation Solution
A system that includes a sensor module and control module to determine the vehicle's state-of-charge, geographical ranges of charging stations, and reserve charging sessions based on consumption factors, enabling automatic reservation and navigation to available stations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a driver manually searches for charging stations when charge level drops below threshold, then the driver can locate available chargers, but the system complexity and time required increase significantly
Solution Approach 1:
The system enables self-service by automatically monitoring battery charge levels, calculating geographical ranges, identifying available charging stations, and reserving charging sessions without driver intervention. The control module continuously performs these functions based on sensor data and consumption factors.
Solution Approach 2:
The system performs preliminary actions by proactively locating and reserving charging stations before the battery is fully depleted. It calculates future geographical ranges based on consumption factors and secures charging sessions in advance, preventing the driver from being stranded.
2Reliability
If the system continuously monitors charge levels and automatically reserves charging stations, then charging availability is ensured, but energy consumption and system complexity increase
Solution Approach 1:
The system implements periodic monitoring of charge levels rather than continuous monitoring, triggering automatic location and reservation processes only when the charge level drops below a predetermined threshold. This reduces energy consumption while maintaining reliable charging availability.
Solution Approach 2:
The system dynamically adjusts the threshold for triggering automatic reservation based on consumption factors such as weather conditions, traffic patterns, and battery state. By changing this parameter adaptively, the system optimizes between reliability and energy consumption.
3Productivity
If the system calculates multiple geographical ranges and factors to estimate charge consumption, then charging station availability is optimized, but the time required for processing increases
Solution Approach 1:
The system pre-calculates geographical ranges and consumption factors when charge levels are still adequate, preparing multiple potential charging station options in advance. This preliminary processing reduces the time required when actual reservation is needed, as the system already has pre-analyzed data ready.
Data Source
AI summary
A system is described. The system comprises: a sensor module and a control module. The control module comprises a processor and a memory. The memory comprising a sensor control module that when executed by the processor causes the processor to: determine a first state-of-charge of one or more batteries of a vehicle; determine whether the first state-of-charge is below a threshold charge level; determine one or more first geographical ranges comprising one or more first charging stations based on the first state-of-charge; determine charge consumption factors that influence charge consumption in the batteries; estimate the charge consumption based on the charge consumption factors; determine a second state-of-charge based on the charge consumption; determine one or more second geographical ranges comprising one or more second charging stations based on the second state-of-charge; and reserve one or more charging sessions with the one or more second charging stations for charging the batteries.