Toward safe carbon–neutral transportation: Battery internal short circuit diagnosis based on cloud data for electric vehicles

The lubrication characteristics of dynamically loaded journal bearings are significantly affected by misalignments. However, existing research on misalignment primarily focuses on fixed misalignment models, and studies on the nonlinear dynamic response of dynamically loaded bearings under dynamic misalignment are yet to be reported. To address this research gap, this study proposed a dynamic misalignment model coupled with rotor dynamics and transient mixed elastohydrodynamic lubrication. The model comprehensively considers factors such as journal eccentric translation, surface topography, oil film cavitation, and bearing elastic deformation. Upon model validation, numerical simulations are conducted to reveal the single-cycle nonlinear tribological characteristics of dynamically loaded bearings subjected to transient time-varying dynamic loads and time-varying misalignment moments. Based on the dynamic misalignment model, a series of parameter studies were conducted to explore the influence of operating conditions such as load magnitude and rotational speed on lubrication characteristics. The research also evaluated the impact of various structural parameters, including surface roughness, bearing bush thickness, and radial clearance, on the performance of dynamically loaded bearings. The numerical results indicate that the impact of fixed-value misalignment on the performance is highly dependent on the manually set deflection angles. Under light-load and high-speed nonstationary operating conditions, the effect of dynamic misalignment on the lubrication characteristics of dynamically loaded journal bearings was significantly amplified. Furthermore, a smoother surface, thicker bearing bush, and smaller radial clearance intensify dynamic misalignment's influence, compared to fixed-value misalignment, on the transient rough contact behavior at the interface. The findings of this study provide valuable guidance and references for the theoretical analysis and optimization design of dynamically loaded journal bearings. 

Dynamic misalignment effects on performance of dynamically loaded journal bearings

The connecting rod small-end bearing system is one of engine's rotating friction pairs under the most severe condition. Significant deformation, high temperature, and the coupling action of multi-bearing tribology and multi-body dynamics make the simulation challenging. This study develops a new tribo-dynamics model considering deformation and thermal effects for the small-end bearing system. A full-scale engine test is conducted, and the predicted severe wear position is consistent with the experimental one. Results show the axial uneven wear and temperature rise happens in the bearing lower surface after considering elastic deformation and thermal effects. The piston pin is decelerated to stop nearly and then accelerated by the friction torque during compression and work strokes that differs from the simplified rigid model. 

A new tribo-dynamics model for engine connecting rod small-end bearing considering elastic deformation and thermal effects

Internal short circuit (ISC) fault diagnosis of battery packs in electric vehicles is of great significance for the effective and safe operation of battery systems. This article presents a new ISC diagnosis method based on a machine learning algorithm. In this method, the incremental capacity curves are employed to divide the voltage curves into multiple sections. The dynamic time warping (DTW) algorithm is used to describe the similarity between partial voltage curves of different cells. Furthermore, four features are selected to describe the DTW distribution and statistics characteristics, and then the ISC diagnosis model based on the gradient boosting decision tree (GBDT) algorithm is constructed. The GBDT algorithm-based method realizes the accurate detection and location of early ISC fault using only partial voltage curves under arbitrary operating conditions, rather than relying on complete charging/discharging curves under specific operating conditions, and the final detection accuracy can be up to 99.4%.

Data-Driven Fault Diagnosis of Internal Short Circuit for Series-Connected Battery Packs Using Partial Voltage Curves

Internal short circuit (ISC) fault can significantly degrade a lithium-ion battery's lifetime, and in severe cases can lead to fatal safety accidents. Therefore, it is critical to diagnose the ISC fault in its early stage for preventing early ISC from evolving into serious safety accidents. In this article, we develop a purely data-driven method using machine learning algorithms for diagnosing the early ISC fault based on different relaxation voltage features. The 15, 30, 45, and 60-min relaxation voltage features are extracted and preliminarily selected to train and verify the Gaussian process regression (GPR) model. Furthermore, the particle swarm optimization algorithm is used to optimize the input features for improving the accuracy of the ISC diagnosis. Three typical data-driven approaches including back propagation neural network, long short-term memory network, and support vector regression are applied to compare the model performance with the GPR model. The results show that the GPR model performs better than the other three models, whose diagnostic error is less than 6.45%. 

Quantitative Diagnosis of Internal Short Circuit for Lithium-Ion Batteries Using Relaxation Voltage

Unveiling micro internal short circuit mechanism in a 60 Ah high-energy-density Li-ion pouch cell

Tribo-dynamic performances and vibration transmission of lubricated translational joints in marine engines

Experimental and numerical study based on ductile failure for the tri-hub burst of turbocharger turbine

The working environment of the diesel exhaust manifold is harsh. On one hand, exhaust gas with high temperature continues to flush on the inner wall of the manifold, and on the other hand, the manifold is also affected by some complex factors, such as diesel engine vibration. If the design is unreasonable, air leakage, cracking, and other failure phenomena are easy to appear. In this article, aiming at the aforementioned problems in the exhaust manifold design process, a high-speed diesel exhaust manifold is taken as an example and analyzed by computational fluid dynamics and finite element method technology. The temperature field distribution of the exhaust manifold is simulated by the fluid–structure interaction method, and then the thermal stress distribution is simulated based on the temperature field of the exhaust manifold. According to the thermal stress of the exhaust manifold and the vibration stress in three directions (axial, transverse, and vertical), combined with the time domain acceleration signals, the high-cycle fatigue life of the exhaust manifold is obtained. The exhaust manifold structure is optimized based on the minimum high-cycle fatigue life position. Then, the high-cycle fatigue life analysis of the optimized structure shows that the optimized structure can significantly improve the life of the exhaust manifold. Then, the optimized structure is further analyzed for low-cycle fatigue life to check whether it meets the requirements of low-cycle fatigue life. After simulation, it can be known that the low-cycle fatigue life meets the design requirements. Finally, sensitivity analysis of the fatigue life under different loading conditions (thermal load and vibration intensity) shows that the thermal load has a significant influence on the fatigue life, so the thermal load should be paid attention to in the design process.

Marine diesel exhaust manifold failure and life prediction under high-temperature vibration

A coupled plasticity-damage model of K418 nickel-based superalloy with pressure and temperature dependence

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