Advanced Modelling of Frequency Dependent Damper Using Machine Learning Approach for Accurate Prediction of Ride and Handling Performances

Abstract

<div class="section abstract"><div class="htmlview paragraph">Accurate ride and handling prediction is an important requirement in today's automobile industry. To achieve the same, it is imperative to have a good estimation of damper model. Conventional methods used for modelling complex vehicle components (like bushings and dampers) are often inadequate to represent behaviour over wide frequency ranges and/or different amplitudes.</div><div class="htmlview paragraph">This is difficult in the part of OEMs to model the physics-based model as the damper’s geometry, material and characteristics property is proprietary to part manufacturer. This is also usually difficult to obtain as a typical data acquisition exercise takes lots of time, cost, and effort. This paper aims to address this problem by predicting the damper force accurately at different velocity/ frequency and amplitude of measured data using Artificial Neural Networks (ANN). The predicted damper force histories were found to be quite accurate as the error in ride and handling between the measured and the thus predicted time histories at various locations were found to be less than 15%. This approach is found to be extremely useful in collecting enormous amounts of customer usage data with minimum instrumentation and small sized data loggers. This has given a big fillip to customer usage data collection in the automotive industry, where the size of the loggers has been a constraint in the collection of such data.</div><div class="htmlview paragraph">New modelling methods circumvent these limitations by using laboratory measurements with neural networks. The new methods enable accurate simulation for nonlinear, frequency dependent components, having multiple inputs and outputs, under arbitrary excitation. This paper describes one such method, known as Empirical Dynamics Modelling. Examples are presented for vehicle shock absorbers. Benefits and limitations are discussed, along with requirements for interfacing to a conventional virtual prototyping environment. Results show particularly good correlation between simulation and testing compare with traditional method.</div></div>