Electric Vehicle Distance to Empty Calculation Using Learned Efficiency
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Solution Overview
Problem
Electric vehicles face significant errors in estimating the distance to empty (DTE) due to varying driving styles, especially during early traveling periods, as there is a lack of accurate data on battery remaining capacity and driving inclination, unlike fuel-powered vehicles.
Innovation Solution
A device and method that calculate accumulated fuel efficiency by combining learned fuel efficiency before charging with real-time traveling fuel efficiency, using a ratio based on the State of Charge (SOC) of the battery, to reduce errors and provide more accurate DTE information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If real-time accumulated fuel efficiency is used for DTE estimation, then the system responds quickly to current driving conditions, but significant errors occur at early traveling periods due to insufficient data accumulation
Solution Approach 1:
The system performs preliminary action by calculating learned fuel efficiency from historical data before real-time estimation begins. This pre-calculated baseline is then combined with real-time accumulated fuel efficiency using SOC-based weighting, allowing the system to provide accurate DTE estimates from the start of travel rather than requiring extensive data accumulation period
Solution Approach 2:
The system dynamically changes the weighting parameters between learned fuel efficiency and real-time accumulated fuel efficiency based on SOC thresholds. When SOC is above the threshold, learned efficiency is weighted more heavily; when SOC drops below the threshold, real-time accumulation is weighted more heavily. This parameter adaptation resolves the contradiction by adjusting the balance between speed and precision based on current battery state
2Measurement precision
If accumulated fuel efficiency is calculated over long periods, then driving inclination and style are accurately accounted for, but the system cannot provide accurate DTE information at early traveling periods
Solution Approach 1:
The system performs preliminary calculation of learned fuel efficiency from historical driving data before the vehicle begins its operational life or before each charging cycle. This pre-computed baseline captures the driver's typical inclination and style without requiring real-time accumulation, enabling accurate DTE estimation from the very beginning of travel
Solution Approach 2:
The system introduces learned fuel efficiency as an intermediary between historical driving patterns and real-time estimation. This intermediary serves as a bridge that transfers the accuracy benefits of long-term accumulation to the early traveling period, while real-time accumulation gradually takes over as it accumulates sufficient data
3Device complexity
If only real-time data is used for DTE calculation, then the system is simple to implement, but it cannot account for driver's driving inclination or style
Solution Approach 1:
The system performs preliminary analysis of historical driving data to extract learned fuel efficiency that encapsulates driver inclination and style. This pre-processed information is stored and then combined with simple real-time measurements, achieving both simplicity in real-time operation and accuracy in driving style consideration
Solution Approach 2:
The system uses the vehicle's existing data infrastructure to automatically generate learned fuel efficiency from historical trip data without requiring external intervention or complex manual calibration. The system serves itself by leveraging its own accumulated data to improve its estimation accuracy
Data Source
AI summary
The prevent invention provides a device and a method for calculating a distance to empty of an electric vehicle which can reduce an initial error in estimating a distance to empty of an electric vehicle. The device and the method for calculating a distance to empty of an electric vehicle can provide more accurate DTE information from the start to the end of traveling by reducing the earlier error, in estimating the DTE from the amount of the presently remaining fuel (the amount of remaining capacity of a battery) of the electric vehicle.