37 control loops. The interesting part is that the sensing technology can work with existing sensors such as tunnel magnetoresistance (TMR) or Hall Effect sensors. TMR sensors typically consist of two ferromagnetic layers separated by a non-magnetic spacer layer. When a magnetic field is applied, the relative alignment of the magnetic moments in these layers affects the electron tunnelling probability, leading to a measurable change in resistance. This change in resistance forms the basis for sensing various parameters such as magnetic field strength, position and direction. The sensors, placed in an array of seven in the motor on a PCB for the position and the currents, provide two degrees of freedom for the position and three for the current. The other two sensors are used for functional safety, which is enabled in the algorithm and improves the accuracy of the measurement. The functional safety does not require full redundancy of the sensor array because any degradation can be detected by performing a correlation check between the sensors. The distance to the coils is critical and the choice of sensor depends on the magnetic field. If the array is closer to the air gap, Hall Effect sensors can be used; if further away, then the more sensitive TMR sensors are more appropriate. The added value of TMR is being able to place the array further away from the coils, which reduces the impact of the changing fields created by the changing voltage (the dv/dt). With motors running at 800 V and above, this is critical. Advances in TMR improve the options for the mounting position to reduce the dv/dt. For example, the module, typically 4 mm thick, can be C-shaped to ease the mounting around the shaft with span of 120° to cover at least one pole pair and ideally two pairs. This allows the system to infer, in real time, the magnetic field of the stator and the rotor with a low-cost microcontroller to determine the position of the rotor. The advantage of this approach is that the PCB in the motor, unlike other inductive posi tion sensors, does not need a target to make the magnetic measurement. Removing the target shrinks the axial length of the motor. There is also a cost improvement of around 50% for the bill of materials over using separate current sensors and resolver motor position sensors. Measuring the magnetic field data of the air gap detects deviations for the calibration data to trigger functional safety errors. Providing a quantitative error also means other functions can be implemented, particularly predictive maintenance, to predict failures in the motor from the error size and pattern. The technology can go up to 40,000 rpm and is limited only by the 10 kHz angular update rate for the sampling, which is determined by the analogue-todigital converter in the microcontroller. The maximum error is 0.2° at 4000 rpm in a 200 A demonstration system, and this is a pattern error that might be correctible in the future but already meets system requirements. The technology then needs to be integrated into a system to assess the system requirements. From a cost perspective, one of the biggest challenges is the oil cooling in the motor because a splash of hot oil could damage the PCB. Therefore, either an enclosure or a conformal coating is used, depending on the space available. Summary There are many different ways of testing an EV electric motor for dynamic performance. Simulators with multiphysics engines can provide high-performance data acquisition systems, and the latest power analysers can then provide accurate data in the lab and be used for mobile testing. The new generation of in-motor test systems is improving the accuracy of the data with existing sensors, and enabling functional safety and predictive maintenance along with real-time motor data, while providing more integration and reducing the bill of materials cost. Acknowledgements With thanks to Javier Bilao de Mendizabal at CTS, Mircea Popescu at Ansys/Synopsys, Alexander Stock at Hottinger Brüel & Kjær (HBK) and Michael Rietvelt at Yokogawa Test and Measurement. E-Mobility Engineering | January/February 2026 The SL2000 high-speed data acquisition unit (Image: Yokogawa)
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