Typical e-bikes magnetoelastic torque sensors employ bulky fluxgate magnetometers to resolve few μT/Nm. Instead, we propose the use of a miniaturized gradiometric arrangement of GaAs Hall plates sensitive to the radial magnetic field and a design approach that reduces the impact of axial misalignment. The proposed architecture provides comparable performance to commercial solutions, with reduced sizing and simplified magnetization requirements. Experiments conducted on a prototype with a full-scale (FS) upper limit of 200 Nm show a resolution of 7 mNm/ √ Hz with a combined standard error of 1.85%FS.

We present a novel high-accuracy rotary magnetic sensor system composed of a two-track coded multi-pole magnet and a dual-spot multi-axes magnetic sensor. The novelty is the measurement of two orthogonal magnetic field components in each of the two sensing spots of the sensor, one associated with each magnet track. As the two orthogonal signals in each spot are naturally in quadrature, i.e. they represent a sine and a cosine signal, the measurement principle is virtually independent of the magnet pole size and pitch. We can therefore design a magnet with much larger poles which in turn generate stronger magnetic flux, allowing for an increased air gap between sensor and magnet. The calibrated encoder system was characterized to deliver 13 bits of absolute accuracy and 17 bits of resolution over the full 360° range.

Due to electrification, magnetic sensors are increasingly deployed in magnetically polluted environments. To reject stray fields, differential sensing schemes are typically used. In this paper, we rely instead on a single-point gradiometric sensing scheme based on the force exerted on a magnet, which is directly related to the magnetic field gradient. Unlike the prior art, our concept uses a differential MEMS force-sensing mechanical transducer. The overall sensor rejects not only magnetic stray fields, but also mechanical disturbances such as vibration and gravity. It is the first single-point MEMS gradiometer that can operate unshielded in various orientations. Our prototype realization achieves a noise density of 4 nT/mm/√Hz in a ±300 μT/mm measurement range. We demonstrate its operational effectiveness in a bus-bar current sensing application. Finally, the paper discusses the remaining weaknesses and provides an outlook for MEMS-based magnetic sensors.

Magnetoelastic torque sensors are prevalent in ebikes. The full-scale magnetic field is typically below 1 mT and read out via discrete fluxgates in commercial sensor modules. To achieve a more compact integration, we explore in this paper Hall-based sensors. Using a multiphysics simulation model, we analyze the system design trade-off between sensitivity and robustness against mechanical misalignment. Our model shows that Gallium Arsenide Hall-based sensors can provide a noise-free resolution of 10 bits in a 500-Hz bandwidth, with a full-scale upper limit of 200 N•m. Experimental validation confirms our simulation results. These findings establish a theoretical foundation for developing high-performance miniaturized magnetoelastic sensors for e-bikes and beyond.

Emerging applications in the field of robotics require tactile sensors with low dynamic errors. Such sensors are often made of elastomers, which exhibit time-dependent drift and hysteresis, leading to lower sensing accuracy. In this work, we systematically study the dominant material-inherent sources for the dynamic error within a tactile sensor, based upon a diverse range of commercially available elastomers. We demonstrate that the initial error associated with the Mullins effect can be completely eliminated, that the residual transient force error related to viscoelastic drift is reduced by 57%, and that the hysteresis force error is reduced by 20% with an optimized selection of suitable elastomers.

This survey paper compiles and benchmarks the performance of integrated magnetic sensors, in terms of their noise and full-scale magnetic field. In total, we collected performance metrics of 31 sensors realized in various technologies. For each sensor, we derived a noise model to estimate the noise under the same conditions. We obtained a comprehensive benchmark with a focus on integrated devices. We also measured the noise spectrum of 11 of those sensors to confirm our noise model. Given that for many emerging applications, such as tactile, current, torque and biomagnetic sensing, the signal of interest is the field gradient to reject magnetic stray fields, we extended the benchmark to gradiometers. We developed a low-noise measurement setup and a low-field gradient (full scale: 10 μT/mm) generation setup to characterize magnetic gradiometers in this regime. The paper discusses the key sensor trade-off between precision and range, and summarizes the technology trends graphically on a trade-off curve, providing broad insight. This paper has been ACCEPTED for publication in IEEE Access.