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This paper proposes a novel torque measurement and control technique for cycling-assisted electric bikes (E-bikes) considering various external load conditions. For assisted E-bikes, the electromagnetic torque from the permanent magnet (PM) motor can be controlled to reduce the pedaling torque generated by the human rider. However, the overall cycling torque is affected by external loads, including the cyclist's weight, wind resistance, rolling resistance, and the road slope. With knowledge of these external loads, the motor torque can be adaptively controlled for these riding conditions. In this paper, key E-bike riding parameters are analyzed to find a suitable assisted motor torque. Four different motor torque control methods are proposed to improve the E-bike's dynamic response with minimal variation in acceleration. It is concluded that the wheel acceleration is important to determine the E-bike's synergetic torque performance. A comprehensive E-bike simulation environment is developed with MATLAB/Simulink to evaluate these adaptive torque control methods. In this paper, an integrated E-bike sensor hardware system is built to verify the proposed adaptive torque control. [ABSTRACT FROM AUTHOR]

This paper proposes a high-resolution permanent magnet (PM) motor drive based on acceleration estimation and control. The PM motor is widely implemented in the printed circuit board (PCB) manufacturing process. To achieve the demanded 1 μm drilling resolution, a sine/cosine incremental encoder is usually installed for motion control. In this paper, several improvements are developed to increase the motion control steady-state accuracy balancing transient response. First, the interpolation of every two encoder counts is proposed to increase the position sensing resolution. In this case, the transient response is improved through the high-resolution position feedback. Second, a closed-loop observer with two independent bandwidths is proposed for acceleration estimation. By using the interpolated position for acceleration estimation, the vibration-reflected high-frequency torque harmonics can be compensated through the acceleration closed-loop control. It reduces the steady-state error under the same sensing hardware. According to experimental results, both transient response and steady-state error can be improved on a PM motor using the proposed position interpolation and acceleration control. [ABSTRACT FROM AUTHOR]

For pulse width modulation (PWM) inverter drives, an LC filter can cascade to a permanent magnet (PM) machine at inverter output to reduce PWM-reflected current harmonics. Because the LC filter causes resonance, the filter output current and voltage are required for the sensorless field-oriented control (FOC) drive. However, existing sensors and inverters are typically integrated inside commercial closed-form drives; it is not possible for these drives to obtain additional filter output signals. To resolve this integration issue, this paper proposes a sensorless LC filter state estimation using only the drive inside current sensors. The design principle of the LC filter is first introduced to remove PWM current harmonics. A dual-observer is then proposed to estimate the filter output current and voltage for the sensorless FOC drive. Compared to conventional model-based estimation, the proposed dual-observer demonstrates robust estimation performance under parameter error. The capacitor parameter error shows a negligible influence on the proposed observer estimation. The filter inductance error only affects the capacitor current estimation at high speed. The performance of the sensorless FOC drive using the proposed dual-observer is comparable to the same drive using external sensors for filter voltage and current measurement. All experiments are verified by a PM machine with only 130 μH phase inductance. [ABSTRACT FROM AUTHOR]

Different from the design of conventional permanent magnet (PM) motors, high-speed motors are primarily limited by rotor unbalanced radial forces, rotor power losses, and rotor mechanical strength. This paper aimed to propose a suitable PM motor with consideration of these design issues. First, the rotor radial force is minimized based on the selection of stator tooth numbers and windings. By designing a stator with even slots, the rotor radial force can be canceled, leading to better rotor strength at high speed. Second, rotor power losses proportional to rotor frequency are increased as motor speed increases. A two-dimensional sensitivity analysis is used to improve these losses. In addition, the rotor sleeve loss can be minimized to less than 8.3% of the total losses using slotless windings. Third, the trapezoidal drive can cause more than a 33% magnet loss due to additional armature flux harmonics. This drive reflected loss is also mitigated with slotless windings. In this paper, six PM motors with different tooth numbers, stator cores, and winding layouts are compared. All the design methods are verified based on nonlinear finite element analysis (FEA). [ABSTRACT FROM AUTHOR]

This paper improves a permanent magnet (PM) machine saliency-based drive performance based on the selection of a suitable injection signal. For the saliency-based position estimation, a persistently high-frequency (HF) voltage signal is injected to obtain a measurable spatial saliency feedback signal. The injection signal can be sine-wave or square-wave alternating current (AC) voltage manipulated by the inverter's pulse width modulation (PWM). Due to the PWM dead-time effect, these HF voltage injection signals might be distorted, leading to secondary harmonics on the saliency signal. In addition, the flux saturation in machine rotors also results in other saliency harmonics. These nonlinear attributes cause position estimation errors on saliency-based drives. In this paper, two different voltage signals are analyzed to find a suited voltage which is less sensitive to these nonlinear attributes. Considering the inverter dead-time, a sine-wave voltage signal reduces its influence on the saliency signal. By contrast, the flux saturation causes the same amount of error on two injection signals. Analytical equations are developed to investigate position errors caused by the dead-time and flux saturation. An interior PM machine with the saliency ratio of 1.41 is tested for the experimental verification. [ABSTRACT FROM AUTHOR]

This paper aims to develop a multisensor data fusion technology-based smart home system by integrating wearable intelligent technology, artificial intelligence, and sensor fusion technology. We have developed the following three systems to create an intelligent smart home environment: (1) a wearable motion sensing device to be placed on residents' wrists and its corresponding 3D gesture recognition algorithm to implement a convenient automated household appliance control system; (2) a wearable motion sensing device mounted on a resident's feet and its indoor positioning algorithm to realize an effective indoor pedestrian navigation system for smart energy management; (3) a multisensor circuit module and an intelligent fire detection and alarm algorithm to realize a home safety and fire detection system. In addition, an intelligent monitoring interface is developed to provide in real-time information about the smart home system, such as environmental temperatures, CO concentrations, communicative environmental alarms, household appliance status, human motion signals, and the results of gesture recognition and indoor positioning. Furthermore, an experimental testbed for validating the effectiveness and feasibility of the smart home system was built and verified experimentally. The results showed that the 3D gesture recognition algorithm could achieve recognition rates for automated household appliance control of 92.0%, 94.8%, 95.3%, and 87.7% by the 2-fold cross-validation, 5-fold cross-validation, 10-fold cross-validation, and leave-one-subject-out cross-validation strategies. For indoor positioning and smart energy management, the distance accuracy and positioning accuracy were around 0.22% and 3.36% of the total traveled distance in the indoor environment. For home safety and fire detection, the classification rate achieved 98.81% accuracy for determining the conditions of the indoor living environment., Competing Interests: The authors declare no conflict of interest.