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Using Model-Based Design, engineers model complex systems and simulate them on their desktop environment for analysis and design purposes. Model-Based Design supports a wide variety of C/C++ code generation applications that include stand-alone simulation, rapid control prototyping, hardware-in-the-loop testing, and production or embedded code deployment. Many of these code generation scenarios impose different requirements on the generated code. Stand-alone simulations usually need to run fast, for parameter sweep or Monte Carlo studies, but do not need to execute in true hard real-time. Hardware-in-the-loop tests by definition use engine control unit (ECU) component hardware that requires a hard real-time execution environment to protect the physical devices. Code generated for production ECUs must satisfy hard real-time, efficiency, legacy code, and other requirements involving verification and validation efforts.

Typical approaches to regulating sound performance of vehicles and products rely upon A-weighted sound pressure level or sound power level. It is well known that these parameters do not provide a complete picture of the customer’s perception of the product and may mislead engineering efforts for product improvement. A leading manufacturer of agricultural equipment set out to implement a process to include sound quality targets in its product engineering cycle. First, meaningful vehicle level targets were set for a tractor by conducting extensive jury evaluation testing and by using objective metrics that represent the customer’s subjective preference for sound. Sensitivity studies (“what-if” games) were then conducted, using the predicted sound quality (SQ) index as validation metric, to define the impact on the SQ performance of different noise components (frequency ranges, tones, transients).