AI-Powered Requirements Engineering & Design Optimization of Electric Powertrains

The development of electric vehicle powertrains is driven by diverse and often conflicting requirements. In early development phases, these requirements are often vague, incomplete, and continuously refined and subject to change as development progresses. Moreover, powertrain designs must be competitive regarding multiple key performance indicators (KPIs) such as performance, cost, energy efficiency, and package integration. This challenges engineers to concurrently develop the powertrain design alongside the requirements on which the design is based on. Managing this combination of uncertain requirements and multi-KPI design optimization represents a complex challenge in automotive engineering. The present work introduces a requirements engineering approach based on OPED (Optimization of Electric Drives). OPED digitalizes the transition from requirements to technical solutions by integrating parametric system models with an AI-based evolutionary optimization algorithm. This enables systematic exploration of trade-offs, robust handling of uncertainties, and the effective specification of requirements. The outcome is a Pareto front of optimal and feasible powertrain solutions, providing engineers and decision makers with a quantitative basis for requirement definition and product design in the development process. A case study demonstrates the approach by determining the optimal torque requirement for the powertrain of an electric passenger car. OPED evaluates the influence of the torque requirement on cost, energy efficiency, and package integration. A Pareto front is generated that contains optimal powertrain solutions alongside the respective torque requirement. Results show that OPED effectively combines requirements engineering and system design optimization, thereby supporting agile and robust powertrain development.