Predictive Driveline Lash Control for Hybrid Powertrains
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Solution Overview
Problem
In hybrid drive powertrains, managing driveline lash becomes increasingly complex due to transitions between multiple torque generative devices, leading to issues like clunks, jerks, and reduced powertrain reliability, as manufacturing tolerances and wear cause torque reversals and interactions within the transmission.
Innovation Solution
A method is developed to predict driveline lash conditions by monitoring axle torque request signals, determining predicted axle torque values, and anticipating zero torque crossings, allowing for proactive control to mitigate lash impacts on vehicle performance and component durability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If multiple torque generative devices are utilized in a hybrid drive powertrain, then power and versatility are improved, but driveline lash management becomes more complex and reliability deteriorates
Solution Approach 1:
The system performs preliminary prediction of driveline lash conditions by monitoring axle torque request signals and determining predicted axle torque values at a lead time before actual zero torque crossings occur. This advance detection enables proactive control actions to be taken, preventing clunks and jerks before they happen, thereby maintaining reliability while using multiple torque generative devices
2Adaptability or versatility
If torque transitions are managed between multiple torque generative devices, then adaptability is improved, but driveline lash conditions worsen due to torque reversals
Solution Approach 1:
The control system determines predicted axle torque request values at a lead time before actual torque transitions occur. By predicting future torque conditions in advance, the system can prepare appropriate control actions to mitigate driveline lash during torque transitions between multiple generative devices, maintaining adaptability while reducing harmful lash effects
Solution Approach 2:
The system continuously monitors axle torque request signals and uses this feedback to update predictions of driveline lash conditions. This closed-loop approach allows the system to adapt to changing operating conditions and adjust control strategies in real-time to prevent driveline lash during torque transitions
3Reliability
If predictive control is implemented to reduce driveline lash, then reliability is improved, but device complexity increases
Solution Approach 1:
The system replaces complex mechanical lash mitigation mechanisms with a predictive control algorithm that processes torque request signals and determines predicted axle torque values computationally. This substitution of mechanical complexity with computational logic achieves reliable driveline lash reduction while maintaining relatively simple physical hardware architecture
Data Source
AI summary
A method to predict a driveline lash condition includes monitoring an axle torque request signal, determining a predicted axle torque request value at a lead time based upon the monitored axle torque request signal, and predicting the driveline lash condition at the lead time based upon the predicted axle torque request value indicating an upcoming zero torque condition.