Adaptive Transmission Shifting Profiles via Fleet Data Aggregation
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Solution Overview
Problem
Adaptive transmission systems in vehicles require significant initial adjustments and settling periods to optimize shifting profiles, which can be time-consuming and affect powertrain performance, especially in newly manufactured vehicles.
Innovation Solution
A backend computer system collects and analyzes data from a fleet of vehicles to determine updated initial shifting profiles based on clutch pressure and torque output data, environmental conditions, and user driving styles, and transmits these profiles to newly manufactured vehicles, allowing for quicker adaptation and improved shifting performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If adaptive transmission systems use initial shifting profiles with learn mode algorithms, then the transmission can adapt to driving behavior, but the settling period becomes time-consuming and powertrain performance is delayed
Solution Approach 1:
The patent applies preliminary action by collecting and analyzing clutch data from multiple vehicles during their settling periods, then using this aggregated data to create updated initial shifting profiles. This allows new vehicles to start with pre-optimized profiles that require shorter settling periods, effectively performing the adaptation work in advance across the fleet and applying the results to individual vehicles before they begin their own learning processes.
2Manufacturing precision
If clutch data is collected and analyzed from multiple vehicles, then updated initial shifting profiles can be determined, but data processing complexity increases
Solution Approach 1:
The patent merges clutch data from multiple vehicles into a single aggregated dataset for analysis. By combining data across the fleet and identifying common patterns and trends, the system determines updated initial shifting profiles that represent optimized performance across the vehicle population, reducing the need for complex individual vehicle processing while achieving high precision results.
Solution Approach 2:
The patent creates updated initial shifting profiles as optimized copies based on aggregated fleet data. These updated profiles are then distributed to vehicles, allowing each vehicle to benefit from the collective learning experience of the entire fleet without requiring each individual vehicle to independently process and analyze all the raw clutch data.
3Productivity
If updated initial shifting profiles are provided to new vehicles, then settling period is reduced, but data transmission and profile updating infrastructure is required
Solution Approach 1:
The patent introduces a backend server as an intermediary that collects clutch data from vehicles, processes the aggregated data to determine updated initial shifting profiles, and then distributes these updated profiles back to vehicles. This intermediary infrastructure enables the flow of optimization information across the fleet, reducing settling periods for individual vehicles while managing the complexity of data collection and distribution centrally.
Data Source
AI summary
A computer having a processor and memory that stores instructions executable by the processor, wherein the computer is programmed to: receive adaptive transmission clutch data from a plurality of first vehicles, the data from each first vehicle including a modified shifting profile; determine, using the received data, an updated initial shifting profile; and provide the updated initial profile to a plurality of second vehicles.