Browse Topic: Agricultural vehicles and equipment
KPIT experts address challenges of maintaining legacy architectures while introducing centralized compute, OTA, new energy platforms and AI layers - driving integration complexity and validation effort. KPIT Technologies is providing the executive leadership for this year's SAE COMVEC, a forum for global leaders in trucking, construction equipment, agricultural machinery and defense vehicles to address the technologies, regulations and innovations impacting transportation today and in the coming years. The theme for COMVEC 2026 (www.sae.org/events/comvec), which takes place in Schaumburg, Illinois, from September 29 to October 1, is “Resolving Current Challenges While Reimagining the Future.” “For commercial and off-highway, this theme captures a structural contradiction the industry lives with every day: transform the entire product architecture while continuing to deliver near-zero downtime, tight margins and proven reliability,” Satish Kumar, senior VP at KPIT, said in a pre-event interview with Truck & Off-Highway Engineering.
In the conversation surrounding electrification, the vehicle itself typically dominates the headlines. But those operating on remote jobsites in the mining, construction and agriculture sectors know the machine is only half the equation. These industries prioritize reliability and uptime and require machines that can handle grueling shifts in demanding environments without compromise. Power providers in these heavy-duty, off-highway markets must move beyond the battery itself to explore a holistic approach to infrastructure when it comes to powering remote jobsites. The first step to success is understanding the fundamental differences between off-highway duty cycles and on-highway applications. While on-highway applications like long-hauling trucks benefit from steady-state operation and passive airflow for cooling, off-highway machines often operate at high torque for extended periods, with little to no forward movement. In these scenarios, there is no passive cooling to rely on or regular refueling stations at the next exit. Success, therefore, is defined by the engineering required to ensure that electric machines deliver the same productivity as diesel, even when operated at their limits in the most rugged, remote conditions.
This study details the development and experimental validation of a high-fidelity one-dimensional (1D) simulation model for a two-speed transmission designed for off-road vehicles, such as tractors and backhoe loaders used in agricultural and civil engineering applications. The model, implemented in the AMESim platform from Siemens, integrates physics-based loss sub-models for all major components, including gears, bearings, seals, and fluid drag (churning) losses. After development, the model was rigorously validated against test bench data, with efficiency measurements taken across various speed, torque, and oil level combinations, demonstrating a strong correlation with experimental results. A detailed analysis enabled the quantification of the contribution of each loss mechanism, identifying the countershaft gears and input shaft bearings as the primary contributors. Furthermore, a Machine Learning (ML)–based calibration framework, employing Bayesian Optimization, was implemented to reduce discrepancies between simulation and experiment and to generate a synthetic dataset for the creation of fast-executing surrogate models. The study concludes that the proposed methodology constitutes an effective tool for efficiency analysis and optimization during early design stages, establishing a foundation for future integration with ML techniques and the development of digital twins.
How to ensure off-highway combustion systems operate with sufficient control to meet tightening emissions standards and evolving fuel landscapes without sacrificing reliability. Off-highway equipment is being asked to do more with less. Less margin for emissions, less tolerance for downtime and less room for inefficiency, while operating under some of the most demanding duty cycles in the transport sector. Tier 4 and Tier 5 emissions standards have reshaped engine calibration strategies. Renewable diesel and biodiesel blends are entering worksites and farms at scale. At the same time, construction, mining and agricultural machines are expected to run for 20-25 years, often at sustained high load and far from service infrastructure. In this environment, combustion systems are far from being phased out.
Precision agriculture, also known as smart farming, was once reserved for early adopters or large-scale operations, but is now an expectation within the farming industry. Across various regions and farm sizes, smart farming techniques are changing the way crops are planted as well as how they are monitored and harvested. However, farmers today are under increasing pressure to reduce labor, decrease chemical inputs, conserve water and operate in tighter windows. Couple this with factors such as narrow seasonal windows, productivity demands and safety considerations, and the need for smarter decisions becomes imperative. Going one step further, global food demands and environmental pressures are further increasing demand for precise, accurate and intelligent farming solutions.
Among the enduring challenges of storing energy – for wind or solar farms, or backup storage for the energy grid or data centers – is batteries that can hold large amounts of electricity for a long time.
Gear noise is a common challenge that all gear manufacturers must contend with. In tractors, while it is often sufficiently low in intensity to not pose a significant issue, there are instances where gear whine may occur which is noticeable. In such cases, identifying the source and effectively addressing the problem can prove to be particularly difficult. This paper addresses the root cause analysis carried out for the evaluation of factors influencing whine noise behavior of Spiral bevel gear pair (SO2) in a tractor transmission system. Numerous publications have been published on gear noise of spiral bevel gear pair, too many to list here. However, once the gearbox assembled into the transmission, such models are of limited practical value. The work explained in this paper is a typical example offers avenues in correcting the issue using more limited means.
One of agricultural tractors most important aspects is operator comfort. In addition to working long hours, tractor operators may be at risk for health problems due to vibrations and mechanical shocks. The tactile vibrations of a tractor are a major consideration when choosing one for agricultural use. This project's mandate includes a study of tractor vibration control problems. It is essential to investigate the governing system in order to determine the cause of the problem. Evaluating the vibrations transmitted via the tractor and using the design of experiments (DOE) approach to lessen vibrations on particular tactile regions were the study's goals. There are several measures currently under investigation which can be used to reduce the vibrations caused by resonance in this paper, these include reducing the natural frequency so as to be able to avoid resonance with the second order engine frequency and the damping coefficient; this will ensure the amplitude of vibration at resonance becomes minimal. The tactile testing is done on the specific tactile places. The findings give an insight into the ways of reducing operator fatigue and improving the tractor ergonomics.
Any agricultural operation (such as cultivation, rotavation, ploughing, and harrowing) includes both productive and non-productive activities (like transportation, stops, and idling) in the field. Non-productive work can mislead the actual load profile, fuel consumption, and emissions. In this project, a machine learning-based methodology has been developed to differentiate between effective operations and non-productive activities, utilizing data collected in the field from data loggers installed on the machinery. Measurements were conducted on various machines across the country in all major applications to minimize the influence of any individual sample deviation and to account for variability in customer operating practices. Few critical parameters such as Engine Speed, Exhaust Gas Temperature, Actual Engine Percentage Torque, GPS Speed etc.) were selected after screening and analyzing more than 100 CAN and GPS parameters. The critical parameters were subsequently integrated with road features and various machine learning algorithms (such as KNN, Decision Tree, and Support Vector Machine (SVM). The results demonstrate that the current methodology effectively differentiates between productive operations and non-productive activities (such as transportation and idling) in major agricultural operations, thereby aiding in design-related decision-making
Agrícola Cana Caiana and Grunner have developed an innovative vehicle for sugarcane harvesting, focused on reducing fuel consumption. This optimization is vital and relevant for similar operations in the largest global producers: Brazil (724 mi t - 37%), India (439 mi t - 22%), China (103 mi t - 5.3%), Thailand (92 mi t - 4.7%), Pakistan (88 mi t - 4.5%), Mexico (55 mi t - 2.8%), Colombia (35 mi t - 1.8%), Indonesia (32 mi t - 1.6%), USA (31 mi t - 1.6%), and Australia (28 mi t - 1.4%). In Brazil, São Paulo leads with 383.4 mi t (54.1% of the 23/24 harvest), followed by Minas Gerais (81.3 mi t). This innovative agricultural machinery, a result of the owners' experience, has already sold over a thousand units, proving its impact on the efficiency of the sugar-alcohol sector. The Belei family's expertise generated this solution that optimizes resources and increases harvesting productivity, with the potential to advance sustainability and profitability globally, driving agricultural innovation. High market acceptance reinforces this machine's relevance to cost and efficiency challenges, representing a promising example for a more efficient and sustainable agribusiness, with advanced technology for harvesting practices [1].
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