Vehicle Charging Intent Prediction System
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Solution Overview
Problem
Electric vehicle users face the challenge of manually tracking their vehicle's battery State of Charge (SOC) level and locating charging stations, which can lead to inefficient and inconvenient charging processes.
Innovation Solution
A vehicle charging management system that predicts user intent to charge based on operational status and historical charging patterns, automatically reminding users when charging is needed and recommending suitable charging stations, thereby eliminating the need for manual tracking and station location searches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual tracking of battery SOC level and charging station location is implemented, then users can monitor charging status, but user effort and time consumption increase
Solution Approach 1:
The system enables self-service by automatically monitoring battery SOC levels and identifying charging stations without user intervention. The processor continuously tracks charging status and autonomously provides recommendations, eliminating manual tracking effort while maintaining reliable monitoring.
Solution Approach 2:
The system implements feedback by continuously monitoring battery SOC levels and providing real-time charging recommendations to users. The processor analyzes current status and historical data to generate timely notifications and station recommendations, ensuring reliable monitoring with minimal user effort.
2Ease of operation
If automated charging prediction system is implemented, then user convenience and charging timeliness improve, but system complexity increases
Solution Approach 1:
The system performs preliminary action by predicting future charging needs based on historical patterns and current status. The processor analyzes past charging behavior to anticipate when charging will be required, enabling users to plan ahead and charge at optimal times without complex real-time decision-making.
Solution Approach 2:
The automated prediction system performs self-service by autonomously analyzing data patterns and generating charging recommendations without requiring complex user configuration. The system independently manages the complexity of pattern recognition and recommendation generation, providing simple user-facing interactions.
3Productivity
If charging recommendations are provided based on historical patterns, then charging efficiency improves, but data processing requirements increase
Solution Approach 1:
The system applies partial action by focusing data processing on the most relevant features for charging prediction, such as SOC level trends and historical charging times. Rather than processing all possible vehicle data, the processor selectively analyzes key patterns that directly impact charging efficiency, reducing computational energy consumption while maintaining high productivity.
Data Source
AI summary
A vehicle charging management method is disclosed. The method may include obtaining a first input associated with a vehicle. The first input may include a state of charge (SOC) level of a vehicle battery, a distance travelled by the vehicle since last charge, and time since the last charge. The method may further include obtaining a second input associated with historical vehicle battery charging information. Responsive to obtaining the first input and the second input, the method may include determining a vehicle user intent to charge the vehicle battery. The method may further include comparing the vehicle user intent with a threshold value, and transmitting a notification to a communication device when the vehicle user intent is greater than the threshold value.