This study aims to design, develop, and evaluate the traction performance of an electric all-wheel-drive (AWD) tractor based on the power transmission and electric systems. The power transmission system includes the electric motor, helical gear reducer, planetary gear reducer, and tires. The electric system consists of a battery pack and charging system. An engine-generator and charger are installed to supply electric energy in emergency situations. The load measurement system consists of analog (current) and digital (battery voltage and rotational speed of the electric motor) components using a controller area network (CAN) bus. A traction test of the electric AWD tractor was performed towing a test vehicle. The output torques of the tractor motors during the traction test were calculated using the current and torque curves provided by the motor manufacturer. The agricultural work performance is verified by comparing the torque and rpm (T-N) curve of the motor with the reduction ratio applied. The traction is calculated using torque and specifications of the wheel, and traction performance is evaluated using tractive efficiency (TE) and dynamic ratio (DR). The results suggest a direction for the improvement of the electric drive system in agricultural research by comparison with the conventional tractor through the analysis of the agricultural performance and traction performance of the electric AWD tractor.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8838039PMC
http://dx.doi.org/10.3390/s22030785DOI Listing

Publication Analysis

Top Keywords

traction performance
16
awd tractor
12
electric
10
electric all-wheel-drive
8
performance electric
8
power transmission
8
electric motor
8
gear reducer
8
system consists
8
traction test
8

Similar Publications

Background And Objective: Neurofibromatosis-1 (NF1) dystrophic scoliosis is a challenging disease to manage surgically, with multiplanar curves progressing rapidly and unpredictably. Conservative management with bracing is often unsuccessful, and many patients necessitate instrumented fusion to halt progression of their curves. In rare cases, patients can present with spontaneous vertebral subluxation, significantly complicating the surgical management of this already complex disease process.

View Article and Find Full Text PDF

Comparison of Recruitment Method on Clinical Outcomes Following Cervical Disc Arthroplasty.

Spine (Phila Pa 1976)

January 2025

Indiana Spine Group Location of investigation Indiana Spine Group, 13225 N. Meridian Street, Carmel, IN 46032.

Study Design: Retrospective cohort.

Objective: To compare the clinical outcomes of trial versus standard clinical practice (SCP) patients following cervical disc arthroplasty (CDA).

Background: CDA is hypothesized to reduce the shear strain and related complications resulting from fusion procedures.

View Article and Find Full Text PDF

An Open-source Python Tool for Traction Force Microscopy on Micropatterned Substrates.

Bio Protoc

January 2025

Laboratoire Interdisciplinaire de Physique (LIPhy), Université Grenoble Alpes, CNRS, Grenoble, France.

Cell-generated forces play a critical role in driving and regulating complex biological processes, such as cell migration and division and cell and tissue morphogenesis in development and disease. Traction force microscopy (TFM) is an established technique developed in the field of mechanobiology used to quantify cellular forces exerted on soft substrates and internal mechanical tissue stresses. TFM measures cell-generated traction forces in 2D or 3D environments with varying mechanical and biochemical properties.

View Article and Find Full Text PDF

With the remarkable advances in diagnostic ultrasound equipment, there is a growing need for ultrasound diagnosis of muscle and soft tissue injuries in sports injuries. Among these, hamstring strains are often difficult to treat and require early and accurate diagnosis. Injuries to the proximal part of the hamstring often take a long time to heal.

View Article and Find Full Text PDF

Machine learning (ML) methods continue to gain traction in hydrological sciences for predicting variables at large scales. Yet, the spatial transferability of these ML methods remains a critical yet underexamined aspect. We present a metamodel approach to obtain large-scale estimates of drain fraction at 10 m spatial resolution, using a ML algorithm (Gradient Boost Decision Tree).

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!