Dual-Clutch Transmission Torque Control for Low-Jerk Shifting
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Solution Overview
Problem
Current vehicle transmission control systems using PID schemes with lookup tables fail to organically control engine torque and clutch torque, resulting in inability to minimize jerk during shifting processes in dual clutch transmissions (DCTs).
Innovation Solution
An apparatus and method that incorporates a dual clutch transmission (DCT) dynamic model and a machine learning-based Gaussian process (GP) model to determine optimal engine torque and compensate for it, synchronizing engine speed with clutch rotation speed, thereby minimizing jerk during shifting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If PID control scheme with lookup table is used for transmission shifting, then control simplicity is maintained, but jerk minimization during shifting cannot be achieved
Solution Approach 1:
The patent replaces the traditional PID control scheme with a model predictive control (MPC) system that uses a dual clutch transmission dynamic model. This substitution enables organic coordination of engine torque and clutch torque by solving an optimization problem that explicitly minimizes jerk, thereby eliminating the harmful shaking during shifting while maintaining control feasibility through the dynamic model framework.
Solution Approach 2:
The patent changes the control parameters from fixed lookup table values to dynamically optimized torque values. By using the dynamic model to calculate optimal engine torque and clutch torque at each control step, and incorporating jerk minimization as an objective function in the MPC framework, the system adapts torque parameters in real-time to minimize shaking during shifting operations.
2Ease of manufacture
If engine torque and clutch torque are controlled independently using PID, then control implementation is straightforward, but organic coordination between torques cannot be achieved
Solution Approach 1:
The patent merges the control of engine torque and clutch torque into a unified model predictive control framework. Instead of independent PID controllers, both torques are simultaneously optimized by solving a single MPC problem that uses the dual clutch transmission dynamic model, ensuring organic coordination between the two torque sources during shifting operations.
Solution Approach 2:
The patent implements a feedback mechanism where the dynamic model continuously receives actual system states (engine speed, clutch speeds, torques) and adjusts the optimal torque commands accordingly. The MPC controller uses this feedback to recalculate and update the optimal engine torque and clutch torque at each control step, ensuring coordinated control adapts to real-time system conditions.
3Device complexity
If traditional lookup table method is used for shifting control, then computational complexity is low, but shift quality optimization cannot be achieved
Solution Approach 1:
The patent transitions from static lookup table control to dynamic model-based control. The dual clutch transmission dynamic model continuously adapts to changing operating conditions by calculating optimal torques based on current engine speed, clutch speeds, and gear states. This dynamic approach enables real-time shift quality optimization while the MPC framework manages computational complexity through efficient optimization algorithms.
Data Source
AI summary
An apparatus and a method for controlling a transmission of a vehicle includes storage that stores a dual clutch transmission (DCT) dynamic model and a machine learning-based Gaussian process (GP) model, and a controller configured for determining a first engine torque used for optimal shifting according to the DCT dynamic model, determines an engine torque compensation value according to the machine learning-based GP model, and controls a shifting of the vehicle according to the first engine torque compensated by the engine torque compensation value.