Adaptive Torque Converter Modeling for Hybrid Powertrain Torque Accuracy
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Solution Overview
Problem
Existing torque converter modeling strategies in hybrid electric vehicles are calibration intensive and prone to inaccurate estimations of impeller and turbine torque due to parameters such as engine torque accuracy, transmission oil temperature, and hardware variation, affecting torque delivery accuracy and efficiency.
Innovation Solution
A system and method that utilizes a polynomial relationship between turbine torque and slip speed, enhanced by online parameter estimation with a Kalman Filter, to adapt the torque converter model to varying transient operating conditions, ensuring accurate impeller and lock-up clutch torque calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional torque converter modeling strategies are used, then the system can transfer rotating power from prime mover to transmission, but the modeling becomes calibration intensive and prone to inaccurate estimations of impeller and turbine torque
Solution Approach 1:
The patent implements dynamic adaptation of torque converter model parameters (K-factor and impeller torque curve) based on real-time operating conditions including transmission fluid temperature and vehicle speed. The model transitions from static calibration to dynamic adaptation, allowing parameters to change continuously with operating conditions, thereby maintaining accuracy without increasing calibration complexity
Solution Approach 2:
The system uses feedback from actual torque converter performance measurements to continuously refine and update the K-factor and impeller torque curve parameters. By comparing predicted versus actual torque converter behavior and adjusting parameters accordingly, the system achieves high measurement precision while avoiding intensive manual calibration through automated adaptive learning
2Adaptability or versatility
If torque converter model parameters are fixed through calibration, then the model structure remains simple, but the model becomes inaccurate under varying transient operating conditions such as temperature changes and hardware variation
Solution Approach 1:
The patent changes the parameters (K-factor and impeller torque curve) of the torque converter model based on operating conditions such as transmission fluid temperature and vehicle speed. This allows the model to adapt to transient conditions while maintaining reliable torque estimation, resolving the contradiction between adaptability and reliability
Data Source
AI summary
An electrified powertrain that generates and transfers drive torque to a driveline of a hybrid electric vehicle is provided. The powertrain includes an engine, an electric motor, a clutch, a torque converter and a controller. The clutch selectively disengages an engine output from a remainder of the electrified powertrain. The torque converter transfers rotating power from at least one of the engine and the electric motor to a transmission. The controller is configured to: receive an impeller speed and a turbine speed of the torque converter; determine a target turbine torque; determine, coefficients as a function of the impeller speed, turbine speed and target turbine torque; update an adaptive lookup table based on the coefficients; and determine an adapted turbine torque based on the updated adaptive lookup table.