Abstract
A new shift schedule with 4-parameter of construction vehicle was discussed and analyzed. The power train model of construction vehicle is vital to automatic shift and difficult to be expressed with mathematic model, while intelligent control is effective for solving the problem. A multi-layer back-propagation neural network (BPNN) model was proposed to describe the model of construction vehicle. The BPNN was trained based on input/output data taken from experiment before that. Based on the BPNN, improved algorithms were proposed to accelerate calculation of optical shift point and control approach. Experimental results showed that the shift strategy with 4-parameter was better than that with 3-parameter and could improve the efficiency of torque converter and save energy, and BPNN was effective to improve shift decisions intelligence of construction vehicle.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
