EV Charging Prediction Controller Using Routine Analysis
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Solution Overview
Problem
Electric vehicle users experience range anxiety due to unreliable range indicators and limited charging infrastructure, leading to inconvenient charging times and locations.
Innovation Solution
A method that determines a vehicle's charging requirement by predicting the state of charge based on the user's routine, providing timely and location-specific charging reminders, and recommending suitable charging locations, using a controller with input, processing, and output means to inform the user of charging needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a state of charge indicator is provided to show remaining range, then users can estimate vehicle range, but the range displayed may not be reliable especially when state of charge is low
Solution Approach 1:
The system performs preliminary actions by determining the user's routine of use of charge before providing range information. It predicts future state of charge based on established usage patterns, allowing the system to proactively inform users about charging requirements before the vehicle actually runs out of charge, thereby improving reliability of range information.
Solution Approach 2:
The system implements feedback by continuously monitoring actual usage patterns and comparing them with predicted consumption. The controller adjusts predictions based on routine determination, providing progressively more accurate range information as it learns the user's specific charging behavior and vehicle usage patterns.
2Adaptability or versatility
If charging infrastructure is limited, then charging stations are scarce in many areas, but users need reliable charging information to plan journeys
Solution Approach 1:
The system performs preliminary charging planning by determining routine usage patterns and predicting future state of charge. It proactively provides charging recommendations and timing information before the user needs to charge, making charging planning more convenient even when infrastructure is limited.
Solution Approach 2:
The system enables self-service by automatically learning and adapting to the user's charging routine without requiring manual input. The controller autonomously determines usage patterns, predicts charging needs, and provides personalized recommendations, reducing the complexity of charging planning for the user.
3Ease of operation
If users only charge at home or work, then charging is convenient, but users may run out of charge during journeys between charging locations
Solution Approach 1:
The system performs preliminary assessment of charging requirements by predicting future state of charge based on determined routines. It proactively informs users of upcoming charging needs before the vehicle reaches charge-depleted state, allowing users to plan intermediate charging stops during journeys while maintaining convenience of home/work charging.
Solution Approach 2:
The system provides beforehand cushioning by giving advance warning of charging requirements. It buffers against range anxiety by predicting when charge will be depleted and informing users in advance, allowing them to take preventive action before running out of charge during journeys between home and work or other regular destinations.
Data Source
AI summary
A method for determining a charging requirement for an energy storage means of a vehicle can include: determining a routine of use of charge of the energy storage means; predicting a future use of charge of the energy storage means based on the routine; determining a charging requirement for the energy storage means based on the prediction; and providing an output to a user of the vehicle indicative of a time at which an increase in the state of charge of the energy storage means will be required based on the determined charging requirement.