An accurate estimation of the state of health (SOH) of Li-ion batteries is critical for the efficient and safe operation of battery-powered systems. Traditional methods for SOH estimation, such as Coulomb counting, often struggle with sensitivity to measurement noise and time-consuming tests. This study addresses this issue by combining incremental capacity (IC) analysis and a novel neural network, Kolmogorov–Arnold Networks (KANs). Fifteen features were extracted from IC curves and a 2RC equivalent circuit model was used to identify the internal resistance of batteries. Recursive least squares were used to identify the parameters of the equivalent circuit model. IC features and internal resistance were considered as input variables to establish the SOH estimation model. Three commonly used machine learning methods (BP, LSTM, TCN) and two hybrid algorithms (LSTM-KAN and TCN-KAN) were used to establish the SOH estimation model. The performance of the five models was compared and analyzed. The results demonstrated that the hybrid models integrated with the KAN performed better than the conventional models, and the LSTM-KAN model had higher estimation accuracy than that of the other models. The model achieved a mean absolute error of less than 0.412% in SOH prediction in the test and validation dataset. The proposed model does not require complete charge and discharge data, which provides a promising tool for the accurate monitoring and fast detection of battery SOH.

Permanent magnet synchronous motors (PMSMs) are extensively utilized in production and manufacturing fields due to their wide speed range, high output torque, fast speed response, small size and light weight. PMSMs are susceptible to inter-turn short circuit faults, demagnetization faults, bearing faults, and other faults arising from irregular vibrations and frequent start–brake cycles. While fault diagnosis for PMSMs offers an effective means to enhance operational efficiency, the multi-sensor information fusion is often overlooked. In industrial production processes, the collected data inevitably suffers from noise contamination, which can adversely impact diagnostic outcomes. To enhance the robustness of diagnostic methods in noisy environments and mitigate the risk of overfitting, a PMSM fault diagnosis method based on image features of multi-sensor fusion is proposed. Firstly, the vibration acceleration signals of the PMSM at different positions were acquired. Then, the newly designed multi-signal Gramian Angular Difference Fields (MGADF) method combines sensor signals from three different installation locations into a single image. Next, the multi-texture features are fused to extract the features of the image. Various machine models are compared in the fault feature learning and classification, and the results show that the proposed diagnostic method has good diagnostic accuracy and robustness, with an average diagnostic accuracy of 99.54% and a standard deviation of accuracy of 0.19. It has excellent performance even in noisy environments. The method is non-invasive and can be extended and applied to the condition monitoring and diagnosis of industrial motors.

Background: The general counter-roller flow-forming (CRFF) process rarely considers the roller’s offset position for the symmetric rollers. However, the rollers’ offset position can regulate the tube shape, force, and other features. Studying the novel asymmetric CRFF process, which is the CRFF process with the rollers’ offset position, is essential. Methods: The influence of the rollers’ offset position, the tube blank thickness, thickness reduction on the material deformation, flow-forming force, final tube middle radius, and thickness in the CRFF process are studied using AA5052 aluminum tube experiments and numerical simulation. Result: The final tubes with three tube blank thicknesses, four thickness reduction, and four rollers’ offset positions were obtained by the symmetric and asymmetric CRFF processes. Conclusions: AA5052 aluminum alloy tube can be made by the novel asymmetric CRFF process using a small rollers’ offset position (−17.5–0%). Different rollers’ positions could change the tube’s middle radius. With negative rollers’ offset position, the outer roller force is larger than the inner roller force. The force differences increase with the increase of tube blank thickness, the increase of thickness reduction, and the decrease of rollers’ offset position. The asymmetric CRFF process helps design and construct large tube flow-forming equipment.

The aim of this paper is to solve the problem for battery electric vehicles of low-precision and time-consuming inspection. A novel method of driving cycle development for battery electric vehicles’ operational safety is proposed in this paper. First, three inspection items are proposed based on relevant testing standards. The inspection calculation method of operational safety is developed based on the acceleration changing rate. Then the multi-cycle inspection method with the stable pedal mode is developed, and the Gauss filtering algorithm is applied for data preprocessing. A rapid inspection driving cycle construction method based on support vector machine is proposed, and a driving cycle is built with a total time of 204 s by fusing and splicing kinematic fragments. Finally, the proposed inspection calculation method is used to validate the operational safety inspection items by tracking the established rapid inspection driving cycle based on the test bench. The results shown are those that qualified the range of acceleration changing rate for driving stability [−0.35, −0.04]. The range for gliding smoothness is [0.05, 0.09]. The range for braking coordination is [−0.04, 0.095]. The maximum RMSE between the constructed rapid inspection segments is 9%, and the maximum RMSE between the tested driving segments is 6%. Test results meet design requirements. The thresholds for operational safety inspection items are evaluated based on the test results. We set less than 0.5 as the safety threshold for driving stability. During the experiment, gliding was less than 0.1 as the safety threshold for gliding comfort, and during braking it was less than 0.1 as the safety threshold for vehicle braking coordination.

Along with the advancement of modern control technology, electric vehicles have gradually become the leading force in the development of the automobile industry. Interior permanent magnet synchronous motors (IPMSMs) have gradually become a mainstream electric vehicle drive component due to their high power density and excellent controllability. This type of motor can form a high-precision closed-loop control system with position sensors, which improves the overall performance. However, the operating conditions are complicated, and position sensor failure can easily occur, which reduces the stability of the motor system. For the fault-tolerant control problem of position sensor failure, pulse high-frequency voltage injection and an improved sliding mode observer (SMO) are employed to achieve the sensorless control of both the low-speed and medium-high-speed sections of the electric motor. Combining the advantages of high-frequency voltage injection in the low-speed section with a high accuracy of position estimation and the advantages of improved SMO in the high-speed section with low computational volume, the composite sensorless control strategy is proposed for IPMSM in the full-speed section. Secondly, an improved SMO using a segmented composite function as the control function is proposed to improve the robustness of the system. It is proposed to address the issue of the smooth transition between the low-speed section and the medium- and high-speed section by using linear weighting. Finally, in order to validate the accuracy of the sensorless control technology, a rapid control prototype of IPMSMs based on Speedgoat is constructed by using a semi-physical simulation system. The efficacy of the control algorithms in the low-speed section, high-speed section and transition interval is verified.

In the current recycling process of reclaimed asphalt pavement (RAP), due to the serious damage of aggregate gradation and the large amount of aged asphalt still wrapped around the surface of the treated aggregate, the low recycling rate and poor performance of the recycled asphalt mixture are the major problems of RAP. In view of the shortcomings of RAP recycling technology, it is urgent to research new treatment methods and design specialized asphalt-stripping equipment to solve the existing problems. In this paper, based on theoretical analysis and EDEM discrete element simulation, a principle prototype for efficient micro-damage fine stripping of asphalt on the RAP surface is developed and tested. The results demonstrate that the principle prototype has a satisfactory asphalt-stripping effect and achieves fine stripping of aged asphalt on the surface of aggregate without large-scale crushing. This principle prototype has significant engineering application values, which provides design solutions and data support for further equipment development.