Vehicle Calibration Map Modeling for Faster OTA Updates
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Solution Overview
Problem
Over-the-air (OTA) flash updating of vehicle controllers is inefficient due to the large size of calibration data maps, which results in significant downtime and bandwidth costs, especially when updating multiple vehicles.
Innovation Solution
The use of data regression techniques to model calibration data, allowing vehicles to download reduced mathematical representations of data maps instead of entire tables, and reproduce them on demand based on current environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If entire calibration data maps are transferred during OTA updates, then complete calibration data is available for all vehicle conditions, but data transfer time and bandwidth usage increase significantly
Solution Approach 1:
The calibration data map is segmented into multiple portions based on vehicle conditions (e.g., temperature ranges, altitude ranges). Instead of transferring the entire data map, only the relevant portion corresponding to current vehicle conditions is transferred. This segmentation allows the system to maintain complete calibration data availability for all conditions while significantly reducing the amount of data transferred during each OTA update.
Solution Approach 2:
The system applies local quality by providing different data portions to different vehicles based on their specific operating conditions. Each vehicle receives calibration data tailored to its local environment (temperature, altitude, etc.), ensuring that the calibration data is optimized for the specific conditions each vehicle encounters rather than providing universal data to all vehicles.
2Reliability
If entire calibration data maps are transferred during OTA updates, then complete calibration data is available for all vehicle conditions, but bandwidth consumption increases significantly
Solution Approach 1:
The calibration data map is divided into condition-specific portions (e.g., cold temperature data, hot temperature data, high altitude data). By segmenting the data this way, the system transfers only the necessary portion for each vehicle's current conditions, dramatically reducing bandwidth consumption while ensuring that complete calibration data remains available in the system for all possible conditions.
Solution Approach 2:
The system extracts and transfers only the relevant calibration data portion needed for current vehicle conditions, separating it from the complete calibration data set. This extraction approach removes unnecessary data from the transfer process, reducing bandwidth usage while maintaining the availability of complete calibration data for all conditions through selective retrieval.
3Loss of time
If reduced data portions are transferred based on current conditions, then data transfer time is reduced, but the system must accurately identify and select the appropriate data portion
Solution Approach 1:
The system performs preliminary actions by pre-organizing the calibration data into condition-specific portions and establishing clear identification criteria for each portion. This preliminary structuring of data makes it easier to quickly identify and select the appropriate data portion during OTA updates, reducing the complexity of real-time data selection while minimizing update downtime.
Data Source
AI summary
A data map is modeled to generate a modeled data map, the data map including a calibration to control vehicle operation over various weather conditions encountered by a vehicle, the modeled data map characterizing data elements of the data map according to a mathematical model. The modeled data map is sent to the vehicle to expand into at least a portion of the data map to control the vehicle operation.