Floating-point arithmetic is widely used in automotive embedded software to scale
Controller Area Network signals and calibration parameters with fractional
factors such as 0.1. However, floating-point operations, even on
microcontrollers equipped with floating-point units, can increase execution time
and CPU load. In AUTOSAR architectures, converting floating-point scaling to
fixed-point is not trivial because scaling semantics must be integrated
consistently across components, yet AUTOSAR platform toolchains offer only
limited automation at the Application Data Type level. Although CompuMethod
definitions can express scaling, integration typically remains manual and
distributed across application software components, reducing consistency and
reusability. This study presents an architecture-driven methodology that
formalizes fixed-point scaling as a centralized architectural service, realized
through a parser-driven fixed-point macro generation pipeline. Standardized CAN
DBC and calibration metadata are parsed to automatically generate integer-only
macros for raw-to-physical and physical-to-raw transformations. The generated
macros are integrated into dedicated AUTOSAR-compliant Scaling Service software
components, consolidating scaling logic and improving reliability and
maintainability. The approach requires no changes to toolchains, compiler
settings, or hardware, enabling direct deployment in AUTOSAR-based software. The
methodology was applied to a production-grade Integrated Charging Control Unit
targeting Electric Vehicle Communication Controller software. Evaluation
included cycle-accurate profiling, edge-based timing, isolated CPU load
calculation, and average current measurement. Results show a 98.84% reduction in
floating-point operations and a 92.67% reduction in conversion-related source
lines. Task execution time decreased by 16.13%, CPU load decreased by 6.88%, and
average current consumption showed a repeatable 0.81% reduction. These results
demonstrate that the proposed methodology improves execution efficiency and is
applicable to production AUTOSAR-based ECUs.