Concept for a Freight Efficient Heavy Duty Truck Chassis

Ostergaard, Erik; Angelini, Nicholas; Aguero, Raymond

doi:10.4271/2020-01-5010

Cited by 1 publication

(1 citation statement)

References 8 publications

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“…The loads on four wheels regarding those five road events were created under the assumption of orthogonal load cases. Details on the construction of these load cases can be found in the paper by Ostergaard et al [21] It was assumed that all load conditions would be somewhere in between the abovementioned cases. For each of the load cases, linear static analysis was conducted using the inertia-relief method [22].…”

Section: Static Analysismentioning

confidence: 99%

Lightweight Chassis Design of Hybrid Trucks Considering Multiple Road Conditions and Constraints

Singh

Seo

et al. 2020

WEVJ

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The paper describes a fully automated process to generate a shell-based finite element model of a large hybrid truck chassis to perform mass optimization considering multiple load cases and multiple constraints. A truck chassis consists of different parts that could be optimized using shape and size optimization. The cross members are represented by beams, and other components of the truck (batteries, engine, fuel tanks, etc.) are represented by appropriate point masses and are attached to the rail using multiple point constraints to create a mathematical model. Medium-fidelity finite element models are developed for front and rear suspensions and they are attached to the chassis using multiple point constraints, hence creating the finite element model of the complete truck. In the optimization problem, a set of five load conditions, each of which corresponds to a road event, is considered, and constraints are imposed on maximum allowable von Mises stress and the first vertical bending frequency. The structure is optimized by implementing the particle swarm optimization algorithm using parallel processing. A mass reduction of about 13.25% with respect to the baseline model is achieved.

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Section: Static Analysismentioning

confidence: 99%