Vehicle Configuration Selection Based on Driving Profile Emissions
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Solution Overview
Problem
Consumers face difficulty in selecting an optimal vehicle configuration that aligns with their driving behavior, and existing systems lack the ability to provide personalized advice on energy consumption, fuel consumption, and CO2 emissions based on individual driving habits.
Innovation Solution
A computer-implemented method determines a vehicle configuration tailored to a user's driving profile using sensors and machine learning algorithms to calculate energy consumption and CO2 emissions, allowing users to select the most suitable drive train configuration through a configurator application.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a person attempts to decide on a vehicle configuration manually, then they can select from available options, but they are overwhelmed and cannot determine the optimal configuration for their driving behavior
Solution Approach 1:
The system automatically collects driving behavior data from sensors in the user's existing vehicle and uses machine learning algorithms to generate personalized vehicle configuration recommendations, eliminating the need for manual research and expert consultation while providing tailored advice based on the user's actual driving patterns
Solution Approach 2:
The patent replaces manual decision-making processes with an automated computer-based system that collects data via sensors, processes information through machine learning algorithms, and generates configuration recommendations, substituting human cognitive effort with automated computational analysis
2Ease of operation
If sellers provide advice on vehicle configuration, then they can guide customers, but they cannot provide individual advice and may not be objective or independent
Solution Approach 1:
The system empowers users to obtain objective, independent advice through an automated algorithm that processes their own driving data without human intervention, eliminating biases and conflicts of interest inherent in seller-provided recommendations while maintaining personalized, reliable guidance
Solution Approach 2:
The patent introduces an independent computational intermediary (the machine learning system) that mediates between the user's driving behavior data and vehicle configuration options, providing unbiased recommendations free from commercial interests while maintaining objectivity through algorithmic processing
3Loss of energy
If the vehicle configuration is optimized for economical operation, then energy consumption and CO2 emissions are reduced, but the vehicle must be precisely matched to individual driving behavior patterns
Solution Approach 1:
The system optimizes energy efficiency by dynamically adjusting vehicle configuration parameters (such as drive train selection, transmission settings, and auxiliary system activation) based on analyzed driving behavior patterns, enabling the vehicle to operate in the most economical manner for each specific user's driving habits
Solution Approach 2:
The patent replaces complex manual optimization processes with automated machine learning algorithms that analyze driving data and determine optimal configuration settings, reducing the perceived complexity for users while achieving precise energy efficiency optimization through computational methods
Data Source
AI summary
A method for determining a vehicle configuration (24) adapted to driving behavior of a person includes: determining a driving profile (20) of the person, wherein the driving profile (20) includes a speed profile and/or an acceleration profile of a vehicle (10) controlled by the person; determining category values (28) for the driving profile (20) for a plurality of vehicle configurations (24), wherein the category values (28) include one or both of an energy consumption and CO2 emissions of the particular vehicle configuration (24), wherein the energy consumption and/or the CO2 emissions are/is calculated from the speed profile and/or the acceleration profile; and determining an optimal vehicle configuration (24) by ascertaining vehicle configuration values (30) for each vehicle configuration (24) by weighting the category values (28) for each vehicle configuration (24) and selecting an optimal vehicle configuration value (30).