Abstract
Abstract
In this research, the advanced hybrid neural network (AHNN) friction-component model, presented in Part 1 of this two-part paper, is integrated with an automotive drivetrain model for system simulations. The AHNN model accurately predicts the dynamic behaviours of transmission friction components over a broad operating range. It also allows variable sampling time steps in a numerical integration process. In this investigation, the AHNN model is trained using experimental data obtained from a powertrain dynamometer test stand. Since typical dynamometer measurements are acquired at locations away from friction components, a backtracking algorithm is developed to evaluate friction component torque during engagement. The trained AHNN model, together with a comprehensive drivetrain model, is implemented to simulate the shifting process of an automatic transmission system under various operating conditions, including different oil-temperature and engine-throttle levels. Simulation results demonstrate that the AHNN friction component model can be effectively utilized as a part of the drivetrain model to accurately predict transmission shift dynamics.
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