A force/torque sensor is often requird to delect small while its maximum load is also often large. In other words, the dynamic range of force/torque sensors should be higher. In order to extend the dynamic range of the force/torque sensor, a high dynamic range force/torque sensor that uses both semiconductor strain gauges and general strain gauges was developed. Experimental results show that the dynamic range can be extended without complicating the structure of the strain body.

We present a method for three-dimensional (3D), point cloud-based, six-dimensional (6D) simultaneous localization and mapping (SLAM) with high accuracy and very low computational cost. The two key points of our high accuracy and real-time SLAM process are feature extraction and scan matching optimization. For the 3D laser-based SLAM using the iterative closest point algorithm, we consider correct corresponding point pair searching for achieving high accuracy. Therefore, we propose extracting feature points from 3D point clouds for correct corresponding point pair searching. To extract features such as edges and corners in real-time, we propose the use of a trained neural network (NN). The NN used in our feature extraction scheme is a simple backpropagation (BP) NN with two hidden layers, which allows building a real-time system for 6D SLAM. To optimize the scan matching, we propose the use of particle swam optimization (PSO) and the extracted feature points. The PSO increases the accuracy of the estimated position by matching the most features with a global map stitched with all features. Compared with the state-of-art methods, the proposed method achieved the best performance for the KITTI Odometry Benchmark.

In this paper, a novel DC bus voltage control strategy of the traction system is proposed for overhead line and energy storage device (ESD) hybrid railway vehicles. This strategy realizes the increase of regenerative brake energy and reduction of consumed energy in the powering by boosting the traction motor voltage. When a DC bus voltage boost is applied, the traction system is separated from the overhead line by a breaker, which is generally equipped. This paper proposes a strategy considering both breaker operation and energy management of onboard ESD. The static state is calculated to reveal the effect of the reduction of energy consumption and the energy management of ESD by applying the proposed control method. The calculation results confirmed a 10.1% and 8.87% decrease in total energy consumption when the DC bus voltage was boosted to 1950V for single and multiple travel between several stations, respectively. Moreover, the proposed energy management method was verified at 4 patterns of the DC bus voltage. A numerical simulation was also performed to verify the dynamic characteristic, especially from an overvoltage point of view. As a result, the DC bus voltage can be regulated without detecting overvoltage.

This paper presents an approach for the range extension control of a three-wheel electric vehicle prototype. By using the torque distribution vector to aggregate motor speeds, the physical model of the vehicle is mapped to an aggregation-and-distribution model (AaDM), which possesses the passivity property. Based on the AaDM, motion control and energy optimization can be designed separately. In particular, a speed controller was designed for the system to operate in the automatic cruise mode. A disturbance observer was designed to operate in the human driving mode. In this study, the conditions for the controllers were obtained to sufficiently ensure the L₂ stability of the control system. The conditions can be checked conveniently without establishing the dynamical equation of the overall system. Under the practically reasonable assumption on motor parameters, the analytical solutions of the optimal torque distribution ratios and d-axis currents were approximately derived in this study. Various test scenarios were considered to validate the proposed control systems. The test results show that in either operation modes, the system can prevent wheel slip, thereby simultaneously improving motion control and energy minimization.

Conventional dual active bridge (DAB) converters based on the non-resonant circuit topology have the sever limitation of soft switching under light load conditions without complex multi-pulse modulations such as dual or triple phase shift. To address this technical problem, this paper explores a symmetrical series-resonant bidirectional dc-dc converter (SSR-BDC). The SSR-BDC features the variable frequency phase shift modulation (VF-PSM) for the minimization of reactive power in a wide range of soft switching operations. The CLLC series resonant tanks provide symmetrical steady-state characteristics of the load power and voltage regulations in forward and reverse power flows, which is advantageous for the seamless exchange of power flows in power electronics applications based on a simple control logic. The principle of the power regulation is described with the frequency-domain analysis of SSR-BDC, whereby the theory for determining the most suitable phase shift angle is clarified. The experimental verifications on a 500W prototype are presented, and the practical effectiveness is revealed; the actual efficiency improves by 1.4% and reactive power reduces by 75% after adopting the VF-PSM.

Interior permanent magnet synchronous motors (IPMSMs) have been widely used as traction motors in electric vehicles. Finite element analysis is commonly used to design IPMSMs but is highly time-intensive. To shorten the design period for IPMSMs, various surrogate models have been constructed to predict relevant characteristics, and they have been used in the optimization of IPMSM geometry. However, to date, no surrogate models have been able to accurately predict the characteristics over the wide speed range required for automotive applications. Herein, we propose a method for accurately predicting the speed-torque characteristics of an IPMSM by using machine learning techniques. To improve the prediction accuracy, we set the motor parameters as the prediction target of the machine learning methods. We then used the trained surrogate model and a real-coded genetic algorithm to minimize the volume of the permanent magnet and showed that the design time can be significantly reduced compared with the case where only finite element analysis is used.

In this paper, a control strategy for reducing voltage harmonics of an induction motor in a low-torque condition is proposed. The motor drive system consists of an open-end winding induction motor and two voltage source inverters (VSIs); the dual-inverter has a DC voltage source and a floating capacitor. The motor losses, especially those caused by pulse width modulation in a VSI, increase when the inverter operates at a low modulation index (MI). In proposed method, the use of the output voltage difference between the inverters constituting the dual-inverter lowers the harmonics by keeping the MI high owing to the phase angle difference variation between the two inverters. The performance of harmonics reduction is theoretically analyzed and experimentally verified. The experimental results show that the voltage THD is reduced by 15 percentage points (pp) and 51pp, compared with those of single-inverter and conventional dual-inverter operations, respectively, at a torque of 0.5Nm (0.1p.u.). By improving the motor efficiency using the proposed method, the total efficiency can be improved even if the inverter efficiency is slightly reduced.

This study investigates a circuit for starting and synchronizing a single-phase synchronous reluctance motor. This motor comprises a resistor or capacitor for starting, a diode for synchronization, and a changeover switch, which are connected to the two-phase stator winding of the single-phase SynRM. The circuit effectiveness is confirmed using a small experimental machine.

To perform finite element analysis (FEA) for estimating the characteristics of synchronous machines (SMs), a current condition corresponding to an operating point is necessary as an analysis input. Accurate identification for the current condition is a difficult problem because it is strongly susceptible to magnetic saturation, e.g., the cross-coupling saturation between d- and q-axes. Especially in a wound-field SM, its flexibility makes the identification more complicated; in other words, three independent variables, such as the amplitude and phase of armature current and amplitude of field current, have to be identified for SMs, whereas two variables are required for a permanent magnet SM or synchronous reluctance machine. Thus, numerous researchers have studied construction of the identification method. This paper proposes a novel identification method for the current condition of wound-field SMs using saturation functions defined by the flux maps. It is demonstrated that the proposed method can correctly identify a large number of current conditions in a short computation time. In addition, the influences of magnetic saturation on the characteristics of the SM were investigated by comparing the identification results obtained by the linear and nonlinear FEA. It is revealed that the magnetic saturation drastically affects the current condition and must be considered in the identification methodology.

Wireless power transfer (WPT) systems have receieved considerable interest for promoting the sale of electric vehicles (EVs). In order to increase the use of WPT systems, electromagnetic field (EMF) leakage should be evaluated at neighborhood and faraway points. Generally, these are measured in the shield room. However, systems that use many coils and cores require very large shield rooms, and accurate measurements are difficult to obtain. In this study, we investigate the coil scaling law to simplify EMF evaluation. Satisfied conditions that enable an equivalent evaluation between full-scale coils and mini-scale ones are derived. The effectiveness of this method is demonstrated using by simulations and experiments.

This paper proposes a kernel principal component analysis (KPCA) based multivariate statistical process control (KPCA-MSPC) method for fault detection of refrigeration showcase systems using a feature selection method with maximal information coefficient (MIC). Refrigeration showcase system data include non-linear relationships among pairs of features, and only normal data can be available for training generally. KPCA-MSPC is suitable for the fault detection because it is an unsupervised method and can handle non-linear relationships. In showcase systems, a large number of measured data can be obtained and they can be utilized as features for fault detection. However, considering system costs, the number of sensors installed in the showcase systems and the amount of data stored in data centers are limited. Therefore, a feature selection method based on MIC and k-nearest neighbor algorithm (KNN) (MIC-KNN-FS) suitable for KPCA-MSPC is proposed. The effectiveness of the combination of KPCA-MSPC and the proposed MIC-KNN-FS for showcase systems is verified by comparison with the Laplacian Score feature selection method (LS-FS) and the KNN feature selection method (KNN-FS), which are typically utilized as feature selection methods, and cumulative autoencoders (CAE) and MSPC based on PCA (PCA-MSPC), which are unsupervised fault detection methods.