Lifetime and Aging Degradation Prognostics for Lithium-ion Battery Packs Based on a Cell to Pack Method

Aging diagnosis of batteries is essential to ensure that the energy storage systems operate within a safe region. This paper proposes a novel cell to pack health and lifetime prognostics method based on the combination of transferred deep learning and Gaussian process regression. General health indicators are extracted from the partial discharge process. The sequential degradation model of the health indicator is developed based on a deep learning framework and is migrated for the battery pack degradation prediction. The future degraded capacities of both battery pack and each battery cell are probabilistically predicted to provide a comprehensive lifetime prognostic. Besides, only a few separate battery cells in the source domain and early data of battery packs in the target domain are needed for model construction. Experimental results show that the lifetime prediction errors are less than 25 cycles for the battery pack, even with only 50 cycles for model fine-tuning, which can save about 90% time for the aging experiment. Thus, it largely reduces the time and labor for battery pack investigation. The predicted capacity trends of the battery cells connected in the battery pack accurately reflect the actual degradation of each battery cell, which can reveal the weakest cell for maintenance in advance.

Biological Stability of Water-Based Cutting Fluids: Progress and Application

The application of cutting fluid in the field of engineering manufacturing has a history of hundreds of years, and it plays a vital role in the processing efficiency and surface quality of parts. Among them, water-based cutting fluid accounts for more than 90% of the consumption of cutting fluid. However, long-term recycling of water-based cutting fluid could easily cause deterioration, and the breeding of bacteria could cause the cutting fluid to fail, increase manufacturing costs, and even endanger the health of workers. Traditional bactericides could improve the biological stability of cutting fluids, but they are toxic to the environment and do not conform to the development trend of low-carbon manufacturing. Low-carbon manufacturing is inevitable and the direction of sustainable manufacturing. The use of nanomaterials, transition metal complexes, and physical sterilization methods on the bacterial cell membrane and genetic material could effectively solve this problem. In this article, the mechanism of action of additives and microbial metabolites was first analyzed. Then, the denaturation mechanism of traditional bactericides on the target protein and the effect of sterilization efficiency were summarized. Further, the mechanism of nanomaterials disrupting cell membrane potential was discussed. The effects of lipophilicity and the atomic number of transition metal complexes on cell membrane penetration were also summarized, and the effects of ultraviolet rays and ozone on the destruction of bacterial genetic material were reviewed. In other words, the bactericidal performance, hazard, degradability, and economics of various sterilization methods were comprehensively evaluated, and the potential development direction of improving the biological stability of cutting fluid was proposed.

Adaptive Coordinated Path Tracking Control Strategy for Autonomous Vehicles with Direct Yaw Moment Control

It is a striking fact that the path tracking accuracy of autonomous vehicles based on active front wheel steering is poor under high-speed and large-curvature conditions. In this study, an adaptive path tracking control strategy that coordinates active front wheel steering and direct yaw moment is proposed based on model predictive control algorithm. The recursive least square method with a forgetting factor is used to identify the rear tire cornering stiffness and update the path tracking system prediction model. To adaptively adjust the priorities of path tracking accuracy and vehicle stability, an adaptive strategy based on fuzzy rules is applied to change the weight coefficients in the cost function. An adaptive control strategy for coordinating active front steering and direct yaw moment is proposed to improve the path tracking accuracy under high-speed and large-curvature conditions. To ensure vehicle stability, the sideslip angle, yaw rate and zero moment methods are used to construct optimization constraints based on the model predictive control frame. It is verified through simulation experiments that the proposed adaptive coordinated control strategy can improve the path tracking accuracy and ensure vehicle stability under high-speed and large-curvature conditions.

Application of Optimal-Jerk Trajectory Planning in Gait-balance Training Robot

To accommodate the gait and balance disorder of the elderly with age progression and the occurrence of various senile diseases, this paper proposes a novel gait balance training robot (G-Balance) based on a six degree-of-freedom parallel platform. Using the platform movement and IMU wearable sensors, two training modes, i.e., active and passive, are developed to achieve vestibular stimulation. Virtual reality technology is applied to achieve visual stimulation. In the active training mode, the elderly actively exercises to control the posture change of the platform and the switching of the virtual scene. In the passive training mode, the platform movement is combined with the virtual scene to simulate bumpy environments, such as earthquakes, to enhance the human anti-interference ability. To achieve a smooth switching of the scene, continuous speed and acceleration of the platform motion are required in some scenarios, in which a trajectory planning algorithm is applied. This paper describes the application of the trajectory planning algorithm in the balance training mode and the optimization of jerk (differential of acceleration) based on cubic spline planning, which can reduce impact on the joint and enhance stability.

Numerical Modelling of the Powder Metallurgical Manufacturing Chain of High Strength Sintered Gears

This paper presents a digital model for the powder metallurgical (PM) production chain of high-performance sintered gears based on an integrated computational materials engineering (ICME) platform. Discrete and finite element methods (DEM and FEM) were combined to describe the macroscopic material response to the thermomechanical loads and process conditions during the entire production process. The microstructural evolution during the sintering process was predicted on the meso-scale using a Monte-Carlo Model. The effective elastic properties were determined by a homogenization method based on modelling a representative volume element (RVE). The results were subsequently used for the FE modelling of the heat treatment process. Through the development of multi-scale models, it was possible obtain characteristics of the microstructural features. The predicted hardness and residual stress distributions allowed the calculation of the tooth root load bearing capacity of the heat-treated sintered gears.

Target Vehicle Selection Algorithm for Adaptive Cruise Control Based on Lane-changing Intention of Preceding Vehicle

To improve the ride comfort and safety of a traditional adaptive cruise control (ACC) system when the preceding vehicle changes lanes, it proposes a target vehicle selection algorithm based on the prediction of the lane-changing intention for the preceding vehicle. First, the Next Generation Simulation dataset is used to train a lane-changing intention prediction algorithm based on a sliding window support vector machine, and the lane-changing intention of the preceding vehicle in the current lane is identified by lateral position offset. Second, according to the lane-changing intention and collision threat of the preceding vehicle, the target vehicle selection algorithm is studied under three different conditions: safe lane-changing, dangerous lane-changing, and lane-changing cancellation. Finally, the effectiveness of the proposed algorithm is verified in a co–simulation platform. The simulation results show that the target vehicle selection algorithm can ensure the smooth transfer of the target vehicle and effectively reduce the longitudinal acceleration fluctuation of the subject vehicle when the preceding vehicle changes lanes safely or cancels their lane change maneuver. In the case of a dangerous lane change, the target vehicle selection algorithm proposed in this paper can respond more rapidly to a dangerous lane change than the target vehicle selection method of the traditional ACC system; thus, it can effectively avoid collisions and improve the safety of the subject vehicle.

Competitive Failure of Bolt Loosening and Fatigue under Different Preloads

Existing research on the competitive failure relationship, failure mechanism, and influencing factors of bolt loosening and fatigue under different preloads is insufficient. This study analyzes the competitive failure relationship between bolt loosening and fatigue under composite excitation through competitive failure tests of bolt loosening and fatigue under different preloads. The results indicated that the failure mode of the bolt is only related to the load ratio (R) and is unrelated to the initial preload and excitation amplitude, which only determine the failure life of the bolt. The small axial loads of composite excitation can restrain bolt failure, and the significant degree of this restraining effect is different for different preloads. Subsequently, a fracture analysis of the bolt was performed to verify the competitive failure relationship of the bolt from a microscopic perspective, and the competitive failure mechanism of the bolt was determined. Based on the findings, we propose a calculation equation for the optimal preload of 8.8 grade high-strength bolts that can serve as a reference for engineering applications.

RSDM: A Powerful Direct Method to Predict the Asymptotic Cyclic Behavior of Elastoplastic Structures

Mechanical engineering structures and structural components are often subjected to cyclic thermomechanical loading which stresses their material beyond its elastic limits well inside the inelastic regime. Depending on the level of loading inelastic strains may lead either to failure, due to low cycle fatigue or ratcheting, or to safety, through elastic shakedown. Thus, it is important to estimate the asymptotic stress state of such structures. This state may be determined by cumbersome incremental time-stepping calculations. Direct methods, alternatively, have big computational advantages as they focus on the characteristics of these states and try to establish them, in a direct way, right from the beginning of the calculations. Among the very few such general-purpose direct methods, a powerful direct method which has been called RSDM has appeared in the literature. The method may directly predict any asymptotic state when the exact time history of the loading is known. The advantage of the method is due to the fact that it addresses the physics of the asymptotic cycle and exploits the cyclic nature of its expected residual stress distribution. Based on RSDM a method for the shakedown analysis of structures, called RSDM-S has also been developed. Despite most direct methods for shakedown, RSDM-S does not need an optimization algorithm for its implementation. Both RSDM and RSDM-S may be implemented in any Finite Element Code. A thorough review of both these methods, together with examples of implementation are presented herein.

Effect of Machined Surface Integrity on Fatigue Performance of Metal Workpiece: A Review

Fatigue performance is a serious concern for mechanical components subject to cyclical stresses, particularly where safety is paramount. The fatigue performance of components relies closely on their surface integrity because the fatigue cracks generally initiate from free surfaces. This paper reviewed the published data, which addressed the effects of machined surface integrity on the fatigue performance of metal workpieces. Limitations in existing studies and the future directions in anti-fatigue manufacturing field were proposed. The remarkable surface topography (e.g., low roughness and few local defects and inclusions) and large compressive residual stress are beneficial to fatigue performance. However, the indicators that describe the effects of surface topography and residual stress accurately need further study and exploration. The effect of residual stress relaxation under cycle loadings needs to be precisely modeled precisely. The effect of work hardening on fatigue performance had two aspects. Work hardening could increase the material yield strength, thereby delaying crack nucleation. However, increased brittleness could accelerate crack propagation. Thus, finding the effective control mechanism and method of work hardening is urgently needed to enhance the fatigue performance of machined components. The machining-induced metallurgical structure changes, such as white layer, grain refinement, dislocation, and martensitic transformation affect the fatigue performance of a workpiece significantly. However, the unified and exact conclusion needs to be investigated deeply. Finally, different surface integrity factors had complicated reciprocal effects on fatigue performance. As such, studying the comprehensive influence of surface integrity further and establishing the reliable prediction model of workpiece fatigue performance are meaningful for improving reliability of components and reducing test cost.