This presentation was made at the NAFEMS Americas Conference "Simulation in the Automotive Industry: Creating the Next Generation Vehicle" held on the 16-18 March 2021.

The automotive engineering community is now confronting the largest technology transformation since its inception. The demand to manufacture cleaner, safer, and smarter vehicles, along with providing an overall enhanced driving and ride experience, has never been higher. As a result, engineering teams must discover, evaluate, and successfully implement leading-edge technology and methods to produce reliable, effective results.

Hence, the challenges for automotive engineers are enormous and require a significant increase in the upfront use of numerical simulation capabilities, methods, and processes such that they’re able to efficiently design, manufacture and deliver these innovative technologies to market in greater speeds than ever before.

Resource Abstract

Demonstrated in this paper is the application of Load Reconstruction to a Baja car suspension. Discussed in the paper will be how to extend this technology to an embedded application for EV and other advanced vehicles.



The performance of a structural design significantly depends upon the assumptions made on input load. In order to estimate the input load, during the design and development stage of the suspension assembly of a BAJA car, designers and analysts invest immense amount of time and effort to formulate the mathematical model of the design. These theoretical formulations may include idealization errors which can affect the performance of the car as a final product. Due to the errors associated with the assumption of design load, several components might have more weight or may have less strength than needed. This discrepancy between the assumed input load (lab or theoretical studies) and the actual load from the environment can be eliminated by performing a real life testing process using load recovery methodology. Commercial load cells exist in industry to give engineers insight to understanding the complex real world loading of their structures. A significant limitation to the use of load cells is that the structure needs to be modified to accept the load cell and not all desired loading degrees of freedom can be measured. The testing procedure followed in this paper replaced load cells with strain gauges and used strain response in conjunction with a correlation matrix from FEA to estimate the true value of input load under real life conditions. The suspension assembly itself will act as a transducer by converting the load into change in electrical resistance of attached strain gauges. The true load acting on the suspension assembly can be estimated from the strain response recorded from the suspension components. Strain gauge placement was determined using True-Load software which creates a correlation matrix relating to the strain response at the gauge locations due to user defined unit load cases. The strain gauge measurements together with the correlation matrix calculated by True-Load give the best estimate of actual load. After determining the true load, the designers redesigned the structural components that can guarantee a better performance in real life situations. The major objectives of this work are to enhance the total performance of the car by designing components to its optimum performance and to identify the true load acting on the rear suspension components.



An extension of this technology can be applied to real time load measurement in the consumer product. The correlation matrix used for load reconstruction can be embedded into a small microprocessor on the consumer product. Robust strain gauges need to be embedded into the consumer product the corresponding strain signals will be sent to the microprocessor. The microprocessor in turn can perform the simple matrix multiplication to calculate the loads and tools may be provided for quality checks on the calculated loading. When a structure can sense the environmental loading in real time, the structure in essence possesses a sense of touch. Control algorithms may be constructed to respond to the touch sensation from load reconstruction. This is ideal for working with the new and highly sophisticated EV marketplace.