Electric Vehicle Mileage Prediction Using Dynamic Power Consumption
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Solution Overview
Problem
Existing methods for predicting the remaining driving mileage of electric vehicles are inaccurate, leading to unexpected power exhaustion and breakdowns due to insufficient consideration of real-time power consumption and varying driving environments.
Innovation Solution
A method and device that acquire in-transit data and driving environment data to calculate power consumption per mileage using a power consumption rate data model, which is updated and refined through historical data analysis to provide a more accurate prediction of remaining driving mileage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the remaining driving mileage is estimated using average energy consumption level based on SOC evaluation, then the calculation process is simple, but the prediction accuracy is insufficient leading to unexpected power exhaustion
Solution Approach 1:
The patent applies dynamics by transitioning from static average energy consumption values to dynamic real-time power consumption calculation. The system continuously updates power consumption based on current driving conditions, vehicle state, and environmental factors, making the prediction model adaptive to changing conditions rather than relying on fixed historical averages.
Solution Approach 2:
The patent implements feedback mechanisms by using actual power consumption data from vehicle sensors to continuously refine and update the power consumption rate model. The system compares predicted versus actual consumption, learns from discrepancies, and adjusts future predictions accordingly, creating a closed-loop system that improves accuracy over time.
2Measurement precision
If real-time power consumption calculation with multiple parameters is used, then the prediction accuracy is improved, but the calculation complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-processing and storing historical power consumption data, vehicle parameters, and environmental factors before they are needed for prediction. The system pre-calculates and stores correlation relationships between various parameters and power consumption, so that during real-time operation, it can quickly retrieve and combine pre-processed data rather than performing complex calculations from scratch.
3Measurement precision
If the power consumption rate model is updated continuously with historical data, then the model accuracy is improved, but the data processing complexity increases
Solution Approach 1:
The patent applies the extraction principle by selectively identifying and extracting only the most relevant parameters and features from the extensive historical data set. Rather than processing all available data, the system extracts key influential factors (such as driving conditions, vehicle state, environmental factors) that have the strongest correlation with power consumption, reducing the dimensionality and complexity of data processing while maintaining prediction accuracy.
Data Source
AI summary
The present invention relates to a method and device for on-line prediction of remaining driving mileage of an electric vehicle. The method comprises: acquiring in-transit data and driving environment data of the electric vehicle which is driving; calculating the power consumption per mileage of the electric vehicle in the current case by using the in-transit data and the driving environment data in combination with a power consumption rate data model; predicting the remaining driving mileage of the electric vehicle based on the power consumption per mileage. The device provided by the present invention is implemented on the basis of the method above. The prediction result of the present invention is more accurate, to avoid the problem that the power is exhausted due to exceeding the mileage expected by a user so that the electric vehicle cannot continue to drive, thereby improving the driving experience of the user.