Motor Vehicle Learning Function Driver Guidance
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Solution Overview
Problem
Existing learning functions in motor vehicles require a long time to activate and continuously recalibrate, necessitating multiple kilometers of driving to ascertain characteristics and provide correction values for nominal value deviations, leading to suboptimal performance and emission levels.
Innovation Solution
Incorporating the driver into the learning process by prompting them to operate the vehicle in specific defined states, such as overrun phases, to accelerate the learning of correction values, utilizing the engine control unit and user information systems to guide the driver through optimal operating conditions and routes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If learning functions continuously ascertain characteristics in multiple defined operating states to provide correction values for nominal value deviations, then manufacturing precision and reliability are improved, but time required for learning activation increases significantly
Solution Approach 1:
The system performs preliminary characterization of the vehicle's actual state during manufacturing or initial setup, storing this information for later use. This allows the learning function to skip the lengthy characterization phase and directly provide correction values, reducing activation time from thousands of kilometers to minimal driving cycles while maintaining accuracy.
Solution Approach 2:
The patent applies preliminary action by pre-determining vehicle characteristics during manufacturing or initial setup, storing this data in the control unit. This preliminary characterization eliminates the need for lengthy runtime learning, allowing correction values to be generated almost immediately while maintaining the accuracy that would otherwise require thousands of kilometers of driving.
2Reliability
If learning functions continuously recalibrate over the entire service life to correct drifts, then reliability is improved, but productivity and time efficiency deteriorate
Solution Approach 1:
Instead of continuous recalibration, the system implements periodic learning cycles at predetermined intervals or under specific conditions. The control unit determines when learning is necessary based on stored vehicle data and operational states, triggering recalibration only when needed rather than continuously, thus maintaining reliability while improving productivity.
Solution Approach 2:
The learning function transitions from a static continuous operation to a dynamic adaptive process. The system monitors vehicle state and automatically activates learning only when characteristics change or drift is detected, adjusting the recalibration frequency based on actual needs rather than following a fixed continuous schedule.
3Productivity
If the system incorporates driver guidance to operate in specific states for learning, then learning speed and productivity are improved, but device complexity increases
Solution Approach 1:
The system provides feedback to the driver through the user information system, guiding them to operate the vehicle in states that facilitate learning. This feedback mechanism accelerates the learning process by ensuring optimal operating conditions are achieved, while the complexity is managed through integration with existing vehicle information systems.
Solution Approach 2:
The user information system acts as an intermediary between the learning function and the driver. It translates complex learning requirements into simple driver actions or information display, enabling accelerated learning without significantly increasing perceived complexity for the end user.
Data Source
AI summary
A method for carrying out a learning function is described, which is used to provide at least one correction value to compensate for at least one nominal value deviation of at least one component of a motor vehicle. At least one characteristic is ascertained in the case of at least one defined operating state of the motor vehicle with the aid of the learning function and used to determine the at least one correction value. The method includes prompting a driver of the motor vehicle to operate the motor vehicle in the at least one defined operating state. A system for implementing a corresponding method is also described.