Real-Time Hydro-Mechanical Transmission System Simulations for Model-Guided Assessment of Complex Shift Sequence 2021-01-0715

Model-guided development of drivetrain control and calibration is a key enabler of robust and efficient vehicle design process. A number of CAE tools are available today for modeling hydro-mechanical systems. Automatic transmission behaviors are well understood to effectively tune the model parameters for targeted applications. Drivetrain models provide physical insight for understanding the effects of component interactions on system behaviors. They are also widely used in HIL/SIL environments to debug control strategies. Nonetheless, it is still a challenge to predict shift quality, especially during a sequence of multiple events, with enough accuracy to support model-guided control design and calibration. The inclusion of hydraulic circuits in simulation models often results in challenges for numerical simulation. The complex interaction of component behaviors can make it difficult to tune a large number of model parameters in a manner consistent with an engineer’s intuition concerning the physical behavior. Missing physical effects in a single component may throw off the entire tuning process. Each application often requires a distinct method to refine the model, making the ease of model adjustments particularly important. In this work, Simscape™ is used to construct a predictive hydro-mechanical system model to replicate a complex shift sequence for a vehicle equipped with a 10-speed transmission. A spool-type pressure regulator valve is modeled to command clutch engagement and release actions. Simscape allows the inclusion of custom models in a native MATLAB® environment. Custom model adjustments based on input from domain experts are effortless, and can include details such as clutch piston seal friction. A real-time open-loop simulation yields excellent agreement with vehicle acceleration data for the entire sequence of 1-3-5-6-4-3-1 shifts. A shift control signal is altered to demonstrate its utility as an enabler of model-guided control design. Key shift quality attributes are evaluated based on the predicted vehicle acceleration to compare advanced control concepts. The model provides the means to efficiently assimilate the knowledge of domain experts to isolate and correct shortfalls during iterative model development process. In summary, a truly collaborative modeling environment proves to be a key to building a predictive drivetrain model especially for high-fidelity applications.