Streamlined Methodology for E-Motor Performance Evaluation using a configurable Universal Inverter

Electric motor benchmarking is often constrained by limited availability of motor-specific data, particularly when dealing with commercially available or third-party electric motors. This paper presents a streamlined and scalable methodology for characterizing unknown eMotors using a configurable universal inverter platform. The proposed approach is specifically designed for OEMs and Tier 1 suppliers seeking to evaluate performance metrics such as torque accuracy, peak and continuous capability, efficiency, and control behaviour—without prior access to key motor parameters or simulation data. A central challenge in this context is the stepwise electromagnetic characterization required to determine the phase current needed for accurate speed and torque control, especially under a Maximum Torque per Ampere (MTPA) or Maximum Torque per Watt (MTPW) strategy. As this requirement is highly dependent on the motor’s topology and electromagnetic properties, most conventional approaches rely on finite element method (FEM) simulations to derive the necessary control parameters. In contrast, the presented methodology assumes no such prior knowledge and instead utilizes only inverter-internal voltage and current measurements, complemented by control-side estimations. Apart from a standard external torque meter, no additional sensor instrumentation is required. The approach enables a low-threshold and efficient setup process, allowing rapid motor commissioning and performance benchmarking. The methodology is based on high level testbed automation and smart optimization solution. Experimental results demonstrate torque estimation errors within 2% of the reference demand, even in the absence of detailed motor models or simulation input. In the current study we demonstrated the methodology on a single motor within a standard eMotor testbench environment. The methodology was proven over a wide range of motor types. This solution significantly reduces the barrier to performance analysis of unknown motors, enabling faster design iterations and informed decision-making regarding inverter topology, control strategy, and system-level cost-performance trade-offs.