Electric Vehicle Range Estimation with Separate Drive and Auxiliary Consumption Prediction

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Solution Overview

Problem

Existing methods for determining the remaining range of electric vehicles do not sufficiently account for secondary consumers, leading to unreliable estimates when an electric motor is in operation.

Innovation Solution

The method involves separately considering the consumption of the electric drive and auxiliary consumers by determining distinct prediction values using different calculation methods and filters, which are tailored to each component and take into account historical consumption patterns, with weighting based on residual energy to provide a more precise and differentiated calculation of the remaining range.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If existing range determination methods are used, then the calculation is simple, but the reliability is insufficient when an electric motor is in operation due to inadequate consideration of secondary consumers

Engineering Contradiction:
Improvereliability of range estimationVSAvoidcomplexity of calculation method
Core Design Contradiction:
ReliabilityVSDevice complexity

Solution Approach 1:

The patent segments the energy consumption calculation by separating auxiliary consumer consumption from drive consumption. Two distinct prediction values are determined: one for auxiliary consumers and one for the drive, each calculated using appropriate methods. This segmentation allows for more reliable range estimation by accurately accounting for secondary consumers while maintaining manageable calculation complexity through modular processing.

Inventive Principle:
Principle #1Segmentation

2Measurement precision

If separate prediction values for drive and auxiliary consumers are determined using different calculation methods, then the measurement precision is improved, but the device complexity increases

Engineering Contradiction:
Improveprecision of consumption predictionVSAvoidcomplexity of calculation system
Core Design Contradiction:
Measurement precisionVSDevice complexity

Solution Approach 1:

The patent applies local quality by using different calculation methods tailored to specific components: auxiliary consumer prediction uses one approach while drive consumption prediction uses another. This allows each prediction to be optimized for its specific characteristics, improving overall measurement precision. The control unit implements this by processing different data types and calculation logic for different consumption sources, with the complexity managed through structured modular architecture.

Inventive Principle:
Principle #3Local quality

Data Source

PatentEP2731822B1Method for determining the remaining range of a motor vehicle, and motor vehicle
Publication Date: 2019.06.12 AUDI AG
  • EP2731822B1 patent drawingFigure 1~2
  • EP2731822B1 patent drawingFigure 3

AI summary

Method for determining the remaining range (39) of a motor vehicle (1) which has an energy store (5) for a drive which acts on at least one wheel of the motor vehicle (1) and has an electric motor (2), as a function of residual energy (34) in the energy store (5), wherein consumption values (17, 18, 19) which describe the current consumption of the drive and of at least one secondary consumer are determined using at least one sensor, a drive prediction value (20a, 20b, 20c) which is assigned to the drive and describes the consumption over a predetermined distance is determined from the consumption values (17) of the drive, and at least one secondary consumption prediction value (21, 22) which is assigned to the secondary consumers and describes the consumption over a predetermined distance is determined separately from the consumption values (18, 19) of the secondary consumers, and the remaining range (39) is determined for at least one distance which is to be travelled by the motor vehicle (1) and is described by the route data, by taking into account the drive prediction value (20a, 20b, 20c) and the secondary consumption prediction value (21, 22).