EV Range Prediction Using Historical Energy Data and Trailer Detection
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Solution Overview
Problem
The unpredictable range of electric vehicles causes 'range anxiety' among users, leading to reluctance in adopting them over conventional ICE vehicles, due to the fear of insufficient range to reach destinations.
Innovation Solution
A method for predicting the range of electric vehicles by using a combination of range models, including a dynamic vehicle model, energy model, and state of charge model, which consider current and historical energy consumption data, along with parameters like vehicle speed, battery state, and trailer attachment, to provide accurate and reliable range metrics to the user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a simple range model based only on current operating cycle data is used, then the device complexity is reduced, but the measurement precision of range prediction deteriorates
Solution Approach 1:
The system performs preliminary actions by recording and storing energy consumption rate data from previous vehicle operating cycles before the current cycle begins. This historical data is then utilized in the first range model to improve prediction accuracy from the outset of the current operating cycle, rather than waiting to accumulate current cycle data.
Solution Approach 2:
The system implements feedback by continuously recording energy consumption rate values from previous operating cycles and feeding this information back into the range prediction model. The first range model uses both historical recorded data and current operating cycle data to dynamically adjust and improve range predictions throughout the vehicle's operation.
2Device complexity
If range prediction is provided only at the end of a vehicle operating cycle, then the device complexity is reduced, but the loss of time for obtaining accurate range information increases
Solution Approach 1:
The system performs preliminary range predictions during the current vehicle operating cycle by utilizing recorded energy consumption rate data from previous cycles. This allows the user to obtain range information early in the operating cycle rather than waiting until the end, while keeping the system relatively simple by building upon previously collected data.
3Measurement precision
If historical energy consumption data from previous operating cycles is incorporated into range prediction, then the measurement precision of range prediction is improved, but the device complexity increases
Solution Approach 1:
The system implements feedback by continuously recording energy consumption rate data from previous operating cycles and feeding this information back into the range prediction model. The first range model uses both historical recorded data and current operating cycle data to dynamically adjust and improve range predictions throughout the vehicle's operation.
Solution Approach 2:
The system performs preliminary actions by recording and storing energy consumption rate data from previous vehicle operating cycles before the current cycle begins. This historical data is then utilized in the first range model to improve prediction accuracy from the outset of the current operating cycle.
Data Source
AI summary
A first method of predicting the range of an electric vehicle comprises, determining a range value during a current vehicle operating cycle using a first range model, wherein the first range model is dependent on an energy consumption rate value recorded during a previous vehicle operating cycle. A second method of predicting the range of an electric vehicle comprises, monitoring a trailer detecting means of the vehicle; and determining a first range value if the trailer detecting means detects that a trailer is attached to the vehicle.