An accurate state of charge (SOC) estimation of the on-board lithium-ion battery is of paramount importance for the efficient and reliable operation of electric vehicles (EVs). Aiming to improve the accuracy and reliability of battery SOC estimation, an improved adaptive Cubature Kalman filter (ACKF) is proposed in this paper. The battery model parameters are online identified with the forgetting factor recursive least squares (FRLS) algorithm so that the accuracy of SOC estimation can be further improved. The proposed method is evaluated by two driving cycles, i.e., the New European Driving Cycle (NEDC) and the Federal Urban Driving Schedule (FUDS), and compared with the existing unscented Kalman filter (UKF) and standard CKF algorithms to verify its superiority. The experimental results reveal that comparing with the UKF and standard CKF, the improved ACKF algorithm has a faster convergence rate to different initial SOC errors with higher estimation accuracy. The root mean square error of SOC estimation without initial SOC error is less than 0.5% under both the NEDC and FUDS cycles.

Chromatic confocal sensors are widely used in various precision measurement fields because of their high measurement accuracy, fast response speed, and good stability. Unlike traditional fiber-coupled structures, we propose an integrated compact chromatic confocal sensing system that can overcome the device-integrating constraints met in industrial environments. Aiming at the distortion of the peak waveform caused by the inconsistent spectral response of the system and to accurately extract the peak wavelength, a spectral characteristic compensation algorithm and a peak wavelength extraction method based on Gaussian curve fitting are proposed. Based on these methods, a segmented curve calibration algorithm is applied to achieve accurate mapping between peak wavelength and position. For the thickness measurement of transparent objects, a simple thickness measurement model and its calibration procedure are proposed, which do not need to obtain previous parameters, such as incident angle or refractive index. Finally, the performance of the proposed sensing system is tested by displacement measurement and thickness measurement experiments. The experimental results show that the root mean square error (RMSE) of displacement measurement is less than 0.1 μm, and the RMSE of thickness measurement is less than 1 μm, which verifies the effectiveness and feasibility of the proposed sensing system.

This study proposes a distinctive and robust spatial and geometric histograms (SGHs) feature descriptor for three‐dimensional (3D) local surface description. The authors also introduce a new local reference frame for the generation of their SGH descriptor. To fully describe a local surface, the SGH descriptor considers both spatial distribution and geometrical characteristics in its underlying support region. To encode neighbourhood information, the SGH descriptor is constructed using histogram statistics with spatial partition and interpolation strategies. The performance of the SGH descriptor was rigorously tested on six public datasets for applications of both 3D object recognition and registration. Compared to eight state‐of‐the‐art descriptors, experimental results show that SGH achieves the best performance on noise‐free data. It also produces the best results even under different nuisances. The promising descriptiveness and robustness of their SGH descriptor have been fully demonstrated.

Dynamic wireless power transfer is a potentially effective method to solve issues related to the range anxiety of electric vehicles (EVs) and reduce the cost of on-board batteries. A novel multiple legs inverter topology with a reflected load identification method for dynamic EV charging is proposed in this paper. In the proposed circuit topology, several inductor-capacitor-capacitor (LCC) reactive power compensation resonant networks and primary pads are selectively excited through a sole primary converter. Besides, a high-response and simple method for the reflected load identification is proposed to rapidly and precisely detect the EV’s position, providing accurate power regulation reference to the converter. With the proposed method, the system can realize high-response and closed-loop power control precisely without any additional wireless communication and position detection devices. Simulation and experimental results verified the efficiency of the proposed scheme. Additionally, the cost comparison results reveal that the proposed scheme could reduce costs by nearly 78% in comparison with the conventional scheme.