Advanced System Simulation Wheel Loader Model for Transient Response and Architecture Studies 2015-01-2824
Understanding the complex and dynamic nature of wheel-loader's operation requires a detailed system model. This paper describes the development of a conventional wheel-loader's system model that can be used to evaluate the transient response. The model includes engine details such as a mean value engine model, which takes into account turbocharger dynamics and engine governor controller. This allows the model to predict realistic performance and fuel consumption over a drive cycle. The wheel-loader machine is modeled in LMS Amesim® and the engine governor controller is modeled in Matlab/SIMULINK®.
In order to simplify the model, hydraulic loads from the boom / bucket mechanism, steering and cooling fan are modeled as hydraulic load inputs obtained from typical short V-drive cycle. Critical wheel-loader drive cycle inputs into the model have been obtained from testing and have been used to validate the system response and cycle fuel consumption. The inputs include driver accelerator pedal, machine ground speed, digging forces and hydraulic loads. Predicted engine speed, machine ground speed and cycle fuel consumption were found to correlate very well with the test results. It is shown that a detailed engine model and governor model will be needed to capture the transient performance of a wheel-loader. An energy distribution study showed significant power losses in the wheel-loader driveline. The system model is modified to study parallel and series electric hybrid architectures. The model has been effectively utilized to identify improvements in potential driveline efficiency and fuel savings opportunities over a conventional wheel-loader.
Citation: Saha, R., Madurai Kumar, M., Hwang, L., Zou, N. et al., "Advanced System Simulation Wheel Loader Model for Transient Response and Architecture Studies," SAE Technical Paper 2015-01-2824, 2015, https://doi.org/10.4271/2015-01-2824. Download Citation
Author(s):
Rohit Saha, Mahesh Madurai Kumar, Long-Kung Hwang, Naiwei Zou, Chen Yu, Zhao Yunfeng, Albert Luo