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In commercial vehicle, Leaf Spring design is an important milestone during product design and development. Leaf springs are the most popular designs having multiple leaves in contact with each other and show hysteresis behavior when loaded and unloaded. Commonly used methods for evaluation of leaf spring strength like endurance trials on field and Rig testing are time consuming and costly. On the other hand, virtual testing methods for strength and stiffness evaluation give useful information early in the design cycle and save considerable time and cost. They give flexibility to evaluate multiple design options and accommodate any design change early in development cycle. A study has been done in Volvo-Eicher to correlate Rig result with Finite Element Analysis (FEA) simulation result of Multi-stage Suspension Leaf Spring, entirely through Finite Element Analysis route.

In commercial vehicle, Leaf Spring design is an important milestone during product design and development. Leaf springs are the most popular designs having multiple leaves in contact with each other and show hysteresis behavior when loaded and unloaded. Commonly used methods for evaluation of leaf spring strength like endurance trials on field and Rig testing are time consuming and costly. On the other hand, virtual testing methods for strength and stiffness evaluation give useful information early in the design cycle and save considerable time and cost. They give flexibility to evaluate multiple design options and accommodate any design change early in development cycle. A study has been done in Volvo-Eicher to correlate Rig result with Finite Element Analysis (FEA) simulation result of Multi-stage Suspension Leaf Spring, entirely through Finite Element Analysis route.

An integrated adaptive cruise control (ACC) and cooperative ACC (CACC) was implemented and tested on three heavy-duty tractor-trailer trucks on a closed test track. The first truck was always in ACC mode, and the followers were in CACC mode using wireless vehicle-vehicle communication to augment their radar sensor data to enable safe and accurate vehicle following at short gaps. The fuel consumption for each truck in the CACC string was measured using the SAE J1321 procedure while travelling at 65 mph and loaded to a gross weight of 65,000 lb, demonstrating the effects of: inter-vehicle gaps (ranging from 3.0 s or 87 m to 0.14 s or 4 m, covering a much wider range than previously reported tests), cut-in and cut-out maneuvers by other vehicles, speed variations, the use of mismatched vehicles (standard trailers mixed with aerodynamic trailers with boat tails and side skirts), and the presence of a passenger vehicle ahead of the platoon.

Buses are always one of the main and favorite sources of public transit. Thousands of people die or injure every year in bus accidents. Bus seat can also cause severe injury to the occupants in case of frontal impact. Seat structure of the bus should absorb sufficient energy to minimize the passenger injury. Most of the occupants seated in the second row or further back were injured by hitting the seat back in the row in front of them. In India, AIS023 (Automotive Industry Standards) is one of the several mandatory standards from CMVR (Central Motor Vehicles Rules) to ensure the seat strength and occupant safety during accidents. This standard specifies minimum and maximum deformations range for the seat back to minimize the passenger injury with adequate seat strength. Present study includes the Finite Element Analysis (FEA) and correlation of bus seat as per AIS023 test setup with LS-Dyna explicit tool. Reasonable correlation was found between test and simulation results.

NREL completed a temporal and geospatial analysis of telematics data to estimate the fraction of platoonable miles traveled by class 8 tractor trailers currently in operation. This paper discusses the value and limitations of very large but low time-resolution data sets, and the fuel consumption reduction opportunities from large scale adoption of platooning technology for class 8 highway vehicles in the US based on telematics data. The telematics data set consist of about 57,000 unique vehicles traveling over 210 million miles combined during a two-week period. 75% of the total fuel consumption result from vehicles operating in top gear, suggesting heavy highway utilization. The data is at a one-hour resolution, resulting in a significant fraction of data be uncategorizable, yet significant value can still be extracted from the remaining data. Multiple analysis methods to estimate platoonable miles are discussed.

Public conveyance such as a bus is a major contributor to socio - economic development of any geography. The international market for passenger bus needed to be made viable in terms of passenger comfort, minimum operational costs of the fleet by reduced fuel consumption through light weighting and yet robust enough to meet stringent safety requirements. Optimized design of bus body superstructure plays vital role in overall performance and safety, which necessitates to evaluate bus structure accurately during initial phase of design. This paper presents a robust methodology in numerical simulation for enhancing the structural characteristics of a bus body with simultaneous reduction in the weight by multi-material optimization while supplemented with sensitivity and robustness analysis. This approach ensures significant reduction in vehicle curb weight with promising design stiffness.

This paper focuses on developing an advanced analytical tire-terrain interaction model for full vehicle performance prediction purposes. The truck tire size 315/80R22.5 is modeled using the Finite Element Analysis (FEA) technique and validated against manufacturer experimental data in static and dynamic domains. While the terrain is modeled using Smoothed-Particle Hydrodynamics (SPH) technique and calibrated using experimental results of pressure-sinkage and direct shear tests. The contact between the FEA tire model and the SPH soil model is defined using the node symmetric node to segment with the edge treatment algorithm. The model setup consists of four tires appended back to back over a box filled with soil particles to represent a multi-axle off-road truck. The distances between the four tires are similar to the distances between the four axles of an off-road truck.