Accurate estimation of the state of charge (SOC) and temperature of batteries is essential to ensure the safety of energy storage systems. However, it is very difficult to obtain multiple states of the battery with fewer sensors. In this paper, a joint estimation method for a lithium iron phosphate battery’s SOC and temperature based on ultrasonic reflection waves is proposed. A piezoelectric transducer is affixed to the surface of the battery for ultrasonic–electric transduction. Ultrasonic signals are excited at the transducer, transmitted through the battery, and transmitted back to the transducer by reaching the underside of the battery. Feature indicator extraction intervals of the battery state are determined by sliding–window matching correlation analysis. Virtual samples are used to expand the data after feature extraction. Finally, a backpropagation (BP) neural network model is applied to the multistate joint estimation of a battery in a wide temperature range. According to the experimental results, the root mean square error (RMSE) of the lithium-ion battery’s SOC and temperature estimation results is 7.42% and 0.40 °C, respectively. The method is nondestructive and easy to apply in battery management systems. Combined with the detection of gas production inside the battery, this method can improve the safety of the battery system.

Three dimensional shape measurement in the microscopic range becomes increasingly important with the development of micro manufacturing technology. Microscopic fringe projection techniques offer a fast, robust, and full-field measurement for field sizes from approximately 1 mm² to several cm². However, the depth of field is very small due to the imaging of non-telecentric microscope, which is often not sufficient to measure the complete depth of a 3D-object. And the calibration of phase-to-depth conversion is complicated which need a precision translation stage and a reference plane. In this paper, we propose a novel telecentric phase-shifting projected fringe profilometry for small and thick objects. Telecentric imaging extends the depth of field approximately to millimeter order, which is much larger than that of microscopy. To avoid the complicated phase-to-depth conversion in microscopic fringe projection, we develop a new system calibration method of camera and projector based on telecentric imaging model. Based on these, a 3D reconstruction of telecentric imaging is presented with stereovision aided by fringe phase maps. Experiments demonstrated the feasibility and high measurement accuracy of the proposed system for thick object.

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.

Humans recognize and manipulate objects relying on the multidimensional force features captured by the tactile sense of skin during the manipulation. Since the current sensors integrated in robots cannot support the robots to sense the multiple interaction states between manipulator and objects, achieving human‐like perception and analytical capabilities remains a major challenge for service robots. Prompted by the tactile perception involved in robots performing complex tasks, a multimodal tactile sensory system is presented to provide in situ simultaneous sensing for robots when approaching, touching, and manipulating objects. The system comprises a capacitive sensor owning the high sensitivity of 1.11E‐2 pF mm⁻¹, a triboelectricity nanogenerator with the fast response speed of 30 ms, and a pressure sensor array capable of 3D force detection. By Combining transfer learning models, which fuses multimodal tactile information to achieve high‐precision (up to 95%) recognition of the multi‐featured targets such as random hardness and texture information under random sampling conditions, including random grasp force and velocity. This sensory system is expected to enhance the intelligent recognition and behavior‐planning capabilities of autonomous robots when performing complex tasks in undefined surrounding environments.

Accurate state characterization of batteries is conducive to ensuring the safety, reliability, and efficiency of their work. In recent years, ultrasonic non-destructive testing technology has been gradually applied to battery state estimation. In this paper, research on the state characterization of lithium-ion batteries based on ultrasonic guided wave (UGW) scanning is carried out. The laser Doppler vibrometer (LDV) and the X-Y stage are used to obtain the surface scanning UGW signal and the line scanning UGW signal of lithium-ion batteries under different states of charge and different aging degrees. The propagation law of UGWs in the battery is analyzed by surface scanning signals, then the energy spectrum of the signals is calculated, showing that the aging of the battery attenuates the transmission energy of UGWs. The “point” parameters are extracted from the scanning point signals. On this basis, the “line” parameters composed of line scanning multi-point signals are extracted. By analyzing the changing law of parameters during the charge–discharge process of batteries, several characteristic parameters that can be used to characterize the battery state of charge and state of health are obtained. The method has good consistency in the state characterization of the three batteries and provides a new approach for non-destructive testing and evaluation of battery states.