Effect of preload forces on multidimensional signal dynamic behaviours for battery early safety warning

Providing early safety warning for batteries in real-world applications is challenging.In this study,comprehensive thermal abuse experiments are conducted to clarify the multidimensional signal evolution of battery failure under various preload forces.The time-sequence relationship among expansion force,voltage,and temperature during thermal abuse under five categorised stages is revealed.Three characteristic peaks are identified for the expansion force,which correspond to venting,internal short-circuiting,and thermal runaway.In particular,an abnormal expansion force signal can be detected at temperatures as low as 42.4°C,followed by battery thermal runaway in approximately 6.5 min.Moreover,reducing the preload force can improve the effectiveness of the early-warning method via the expansion force.Specifically,reducing the preload force from 6000 to 1000 N prolongs the warning time(i.e.,227 to 398 s)before thermal runaway is triggered.Based on the results,a notable expansion force early-warning method is proposed that can successfully enable early safety warning approximately 375 s ahead of battery thermal runaway and effectively prevent failure propagation with module validation.This study provides a practical reference for the development of timely and accurate early-warning strategies as well as guidance for the design of safer battery systems.

Effect of a preload force on anchor system frequency

The interrelationship between preload forces and natural frequencies of anchors was obtained from the structure of an anchor and its mechanical characteristics. We established a numerical model for the dynamic analysis of a bolt support system taking into consideration the working surroundings of the anchor. The natural frequency distribution of the system under various preload forces of the anchor was analyzed with ANSYS. Our results show that each order of the system frequency varied with an increase in preload forces. A single order frequency decreased with an increase in the preload force. A preload force affected low-order frequencies more than high-order frequencies. We obtained a functional relationship by fitting preload forces and fundamental frequencies, which was in agreement with our theretical considerations. This study provides theoretical support for the detection of preload forces.

Static Force-Based Modeling and Parameter Estimation for a Deformable Link Composed of Passive Spherical Joints With Preload Forces

To balance the contradiction between higher flexibility and heavier load bearing capacity,we present a novel deformable manipulator which is composed of active rigid joints and deformable links.The deformable link is composed of passive spherical joints with preload forces between socket-ball surfaces.To estimate the load bearing capacity of a deformable link,we present a static force-based model of spherical joint with preload force and analyze the static force propagation in the deformable link.This yields an important result that the load bearing capacity of a spherical joint only depends on its radius,preload force,and static friction coefficient.We further develop a parameter estimation method to estimate the product of preload force and static friction coefficient.The experimental results validate our model.80.4%of percentage errors on the maximum payload mass prediction are below 15%.

Research on Optimal Preload Method of Controllable Rolling Bearing Based on Multisensor Fusion

Angular contact ball bearings have been widely used in machine tool spindles,and the bearing preload plays an important role in the performance of the spindle.In order to solve the problems of the traditional optimal preload prediction method limited by actual conditions and uncertainties,a roller bearing preload test method based on the improved D-S evidence theorymulti-sensor fusion method was proposed.First,a novel controllable preload system is proposed and evaluated.Subsequently,multiple sensors are employed to collect data on the bearing parameters during preload application.Finally,a multisensor fusion algorithm is used to make predictions,and a neural network is used to optimize the fitting of the preload data.The limitations of conventional preload testing methods are identified,and the integration of complementary information frommultiple sensors is used to achieve accurate predictions,offering valuable insights into the optimal preload force.Experimental results demonstrate that the multi-sensor fusion approach outperforms traditional methods in accurately measuring the optimal preload for rolling bearings.