Heat removal and thermal management are critical for the safe and efficient operation of lithium-ion batteries and packs. Effective removal of dynamically generated heat from cells presents a substantial challenge for thermal management optimization. This study introduces a novel liquid cooling thermal management method aimed at improving temperature uniformity in a battery pack. A complex nonlinear hybrid model is established through traditional full-factor design and back propagation neural network (BPNN) approximation. This model links input parameters such as the number of baffles, baffle angle, and inlet speed to output parameters including maximum temperature, temperature difference, and pressure drop. Global multiobjective optimization is carried out using the Nondominated Sorting Genetic Algorithm II to sidestep locally optimal solutions. Pareto optimal solutions are sorted using multiple criteria decision-making techniques. Through thermal management optimization, the maximum temperature rise of the battery relative to the initial temperature is controlled within 7.68 K, the temperature difference is controlled within 4.22 K (below the commonly required 5 K), and the pressure drop is only 83.92 Pa. Results presented in this work may help enhance the performance and efficiency of battery-based energy conversion and storage. The optimization technique used in this work helps maximize the benefit of an innovative battery thermal management technique.

This study proposes a stepped channel liquid-cooled battery thermal management system based on lightweight. The impact of channel width, cell-to-cell lateral spacing, contact height, and contact angle on the effectiveness of the TCS is investigated through using numerical simulation. The weight sensitivity factor is adopted to evaluate the effect of TCS weight (m_TCS) on the maximum temperature (T_max) of battery pack. Results suggest that the channel width plays the most critical role, followed by cell-to-cell lateral spacing and contact angle, while the contact height has minimal influence. Four parameters that affect the thermal balance performance of battery pack, including the number of channels, and baffles, baffle angle, and coolant inlet velocity, are presented using orthogonal experiment. Results indicate that the number of channels and baffle angle have a significant influence on the thermal balance of battery pack, while thermal performance is largely insensitive to coolant inlet velocity and number of baffles. Based on the analysis stated in this work, an improved design of the TCS is presented that reduces weight by 54.08% while increasing T_max only by 2.52 K.

Object detection is a crucial task of autonomous driving. This paper addresses an effective algorithm for pedestrian detection of the panoramic depth map transformed from the 3D-LiDAR data. Firstly, the 3D point clouds are transformed into panoramic depth maps, and then the panoramic depth maps are enhanced. Secondly, the grounds of the 3D point clouds are removed. The remaining point clouds are clustered, filtered and projected onto the previously generated panoramic depth maps, and new panoramic depth maps are obtained. Finally, the new panoramic depth maps are jointed to generate depth maps with different sizes, which are used as input of the improved PVANET for pedestrian detection. The 2D image of the panoramic depth map applied to the proposed algorithm is transformed from 3D point cloud, effectively containing the panorama of the sensor, and is more suitable for the environment perception of autonomous driving. Compared with the detection algorithm based on RGB images, the proposed algorithm cannot be affected by light, and can maintain the normal average precision of pedestrian detection at night. In order to increase the robustness of detecting small objects like pedestrians, the network structure based on the original PVANET is modified in this paper. A new dataset is built by processing the 3D-LiDAR data and the model trained on the new dataset perform well. The experimental results show that the proposed algorithm achieves high accuracy and robustness in pedestrian detection under different illumination conditions. Furthermore, when trained on the new dataset, the model exhibits average precision improvements of 2.8–5.1 % over the original PVANET, making it more suitable for autonomous driving applications.

Aiming at the position and pose control requirement of the ankle rehabilitation robot, two schemes of 3-PRS (Prismatic-Revolute-Spherical) parallel ankle rehabilitation robots are proposed, and the analysis and simulation of the control strategy are carried out. Two schemes of 3-PRS ankle rehabilitation robots are statically analysed by the finite element method. Through the stress comparison of the two schemes, the stable one is determined. Three control strategies including the traditional PID control, PID control based on the genetic algorithm and adaptive fuzzy PID control are proposed. A multidisciplinary co-simulation platform is established by the combination of ADAMS, SOLIDWORKS and MATLAB, and the three control strategies are verified. The best control strategy for the rehabilitation robots is determined by comparing control effects. This study lays a solid foundation for research on the control of the rehabilitation robots.

The 3D printing method based on digital light processing (DLP) technology can transform liquid resin materials into complex 3D models. However, due to the limitations of digital micromirror device (DMD) specifications, the normal DLP 3D printing method (NDPM) cannot simultaneously process large-size and small-feature parts. Therefore, a scalable DLP 3D printing method (SDPM) was proposed. Different printing resolutions for a part were designed by changing the distance between the projector and the molding liquid level. A scalable DLP printer was built to realize the printing resolution requirements at different sizes. A series of experiments were performed. Firstly, the orthogonal experimental method was used, and the minimum and maximum projection distances were obtained as 20.5 cm and 30.5 cm, respectively. Accordingly, the layer thickness, exposure time, and waiting leveling time were 0.08 mm, 3 s, and 6 s and 0.08 mm, 7 s, and 10 s. Secondly, single-layer column feature printing was finished, which was shown to have two minimum printing resolutions of 101 μm and 157 μm at a projection distance of 20.5 cm and 30.5 cm. Thirdly, a shape accuracy test was conducted by using the SDPM. Compared with the NDPM, the shape accuracy of the small-feature round, diamond, and square parts was improved by 49%, 42%, and 2%, respectively. This study verified that the SDPM can build models with features demonstrating high local shape accuracy.

In this research, a molecular dynamics (MD) model was adopted to investigate the essence of the effect of strain rate on the mechanical behavior of the Fe14.6Ni (at%) elastocaloric refrigeration alloy. The study showed that the mechanical behavior of the Fe14.6Ni (at%) alloy was dependent on the strain rate. Besides, the investigation of temperature demonstrated that the strain rate influenced mechanical behavior by changing the transient temperatures. Furthermore, it was found that the adiabatic temperature change (ΔT_adi) was high and up to 51 K, which was a 1.57 times improvement. Finally, the conclusion was drawn that the strain rate influenced the mechanical behavior by changing the transient total kinetic energy and the phase content evolution processes, which was the essence of the effect of strain rate on the mechanical behavior. This work has clarified the essence and enriched the theory of the effect of strain rate on the mechanical behavior of elastocaloric refrigeration alloys.