Traditionally, calibration has been a time-consuming, manual endeavor. However, as powertrain systems grow more complex and interconnected, innovative AI-driven approaches are essential to keep pace with technological advances. We will explore the shift toward virtual environments, reducing reliance on costly physical prototypes and enhancing efficiency. We will also explore the impact of advanced machine learning techniques on the calibration process, and how optimization algorithms streamline tasks and reduce physical testing. Through real-world examples from EV powertrain calibration, learn how new approaches accelerate testing cycles, setting a new standard for performance optimization and positioning the industry for advancement in a digital-first landscape.

• How virtual calibration streamlines testing, reducing costs and prototype dependency
• How advanced machine learning optimizes calibration, enhances accuracy and efficiency
• Use of optimization algorithms and simulation tools, minimizing physical testing
• How transient and global machine learning models aid vehicle verification/validation
• Real-world EV powertrain calibration examples, supporting faster development/environmental goals