This research proposes the use of Artificial Neural Networks (ANN) to predict the road input for road load data generation for variants of a vehicle as vehicle parameters are modified. This is important to the design engineers while the vehicle variant is still in the initial stages of development, hence no prototypes are available and accurate proving ground data acquisition is not possible. ANNs are, with adequate training, capable of representing the complex relationships between inputs and outputs. This research explores the implementation of the ANN to predict road input for vehicle variants using a quarter vehicle test rig. The training and testing data for this research are collected from a validated quarter vehicle model.
Artificial Road Load Generation Using Artificial Neural Networks
Sae Technical Papers
SAE 2015 World Congress & Exhibition ; 2015
2015-04-14
Conference paper
English
Artificial road load generation using artificial neural networks
Automotive engineering | 2015
|Artificial Road Load Generation Using Artificial Neural Networks
British Library Conference Proceedings | 2015
|Road Traffic Prediction Using Artificial Neural Networks
IEEE | 2018
|FORECASTING ROAD TRANSPORT ENERGY CONSUMPTION USING ARTIFICIAL NEURAL NETWORKS
British Library Conference Proceedings | 2005
|