Integrated Chassis Control of Active Front Steering and Yaw Stability Control Based on Improved Inverse Nyquist Array Method

An integrated chassis control (ICC) system with active front steering (AFS) and yaw stability control (YSC) is introduced in this paper. The proposed ICC algorithm uses the improved Inverse Nyquist Array (INA) method based on a 2-degree-of-freedom (DOF) planar vehicle reference model to decouple the plant dynamics under different frequency bands, and the change of velocity and cornering stiffness were considered to calculate the analytical solution in the precompensator design so that the INA based algorithm runs well and fast on the nonlinear vehicle system. The stability of the system is guaranteed by dynamic compensator together with a proposed PI feedback controller. After the response analysis of the system on frequency domain and time domain, simulations under step steering maneuver were carried out using a 2-DOF vehicle model and a 14-DOF vehicle model by Matlab/Simulink. The results show that the system is decoupled and the vehicle handling and stability performance are significantly improved by the proposed method

Atomistic Modeling of Solid Interfaces in All-solid-state Li-ion Batteries

All-solid-state Li-ion battery based on solid electrolyte is a promising next-generation battery technology, providing intrinsic safety and higher energy density. Despite the development of solid electrolyte materials with high ionic conductivity, the high interfacial resistance and interfacial degradation at the solid electrolyte–electrode interfaces limit the electrochemical performance of the all-solid-state batteries. Fundamental understanding about the solid-solid interfaces is essential to improve the performance of all-solid-state batteries. In this dissertation, I perform first principles computation to bring new understanding about these solid interfaces. Using our developed computation approach based on large materials database, I calculated the intrinsic electrochemical stability window of solid electrolytes and predicted interphase decomposition products. I revealed the effects of different types of interphase layers on the interface stability and battery performance, and also provided interfacial engineering strategies to improve interface compatibility. Lithium metal anode can provide significantly higher energy density of Li-ion batteries. However, only a limited number of materials are known to be stable against lithium metal due to its strong reducing nature. Using first-principles calculations and large materials database, I revealed the general trend of lithium reduction behavior in different material chemistry. Different from oxides, sulfides, and halides, nitride anion chemistry exhibits unique stability against lithium metal, which is either thermodynamically intrinsic or a result of stable passivation. Therefore, many nitrides materials are promising candidate materials for lithium metal anode protection. Since solid electrolytes in all-solid-state batteries are often polycrystalline, the grain boundaries can have an important impact on the ion diffusion in solid electrolytes. I performed molecular dynamics simulations to study the ion diffusion at grain boundaries in solid electrolyte materials, and showed the distinct diffusion behavior at grain boundaries different from the facile ion transport in the bulk. In addition, I studied the order-disorder transition induced by mechanical strain in lithium garnet. Such transition can lead to orders of magnitude change in ionic diffusivity. This series of work demonstrated that computational modeling techniques can help to gain critical fundamental understandings of the solid interfaces in all-solid-state Li-ion battery, and to provide practical engineering strategies to improve the battery performance

Faster VoxelPose: Real-time 3D Human Pose Estimation by Orthographic Projection

While the voxel-based methods have achieved promising results for multi-person 3D pose estimation from multi-cameras, they suffer from heavy computation burdens, especially for large scenes. We present Faster VoxelPose to address the challenge by re-projecting the feature volume to the three two-dimensional coordinate planes and estimating X, Y, Z coordinates from them separately. To that end, we first localize each person by a 3D bounding box by estimating a 2D box and its height based on the volume features projected to the xy-plane and z-axis, respectively. Then for each person, we estimate partial joint coordinates from the three coordinate planes separately which are then fused to obtain the final 3D pose. The method is free from costly 3D-CNNs and improves the speed of VoxelPose by ten times and meanwhile achieves competitive accuracy as the state-of-the-art methods, proving its potential in real-time applications.Comment: 22 pages, 7 figures, submitted to ECCV 202

MotionBERT: A Unified Perspective on Learning Human Motion Representations

We present a unified perspective on tackling various human-centric video tasks by learning human motion representations from large-scale and heterogeneous data resources. Specifically, we propose a pretraining stage in which a motion encoder is trained to recover the underlying 3D motion from noisy partial 2D observations. The motion representations acquired in this way incorporate geometric, kinematic, and physical knowledge about human motion, which can be easily transferred to multiple downstream tasks. We implement the motion encoder with a Dual-stream Spatio-temporal Transformer (DSTformer) neural network. It could capture long-range spatio-temporal relationships among the skeletal joints comprehensively and adaptively, exemplified by the lowest 3D pose estimation error so far when trained from scratch. Furthermore, our proposed framework achieves state-of-the-art performance on all three downstream tasks by simply finetuning the pretrained motion encoder with a simple regression head (1-2 layers), which demonstrates the versatility of the learned motion representations. Code and models are available at https://motionbert.github.io/Comment: ICCV 2023 Camera Read