Accurate prediction of the remaining useful life (RUL) of batteries is of great importance for the health management of different equipment and machines, such as electric vehicles and smartphones. It gives operators information about when the battery should be replaced. Predicting the batteries’ RUL using the data only from early cycles can also be beneficial for manufacturers. For example, it can reduce the batteries’ testing costs during the research and development phase. This article focuses on batteries’ RUL early prediction using data-driven methods. The differential capacity ( $dQ/dV)$ and differential voltage ( $dV/dQ)$ curves can reveal the potential capacity and voltage of a battery, respectively, and they are known to be indicators of the batteries’ degradation. We will present a practical method for batteries’ RUL early prediction using features extracted from those two curves. Accordingly, 19 features generated from the $dQ/dV$ and $dV/dQ$ curves are analyzed and extracted. The Sparse Bayesian Learning (SBL) method is a popular machine learning method in the field of RUL prediction, and it is used to achieve an RUL early prediction for batteries. In the end, the training and test errors are investigated to evaluate the presented method’s efficiency. Moreover, we compared our results with two other methods (lasso and elastic net), which have been recognized as best performing methods in this field so far, and the comparisons showed our proposed method outperforms those two methods in the term of accuracy. The presented method is generic and can be used for RUL early prediction of different batteries.

The unified approach based upon the Reduced Reynolds technique is applied to develop a deterministic transient mixed lubrication line contact model. This model is used in spur gear applications to comprehensively show effects of roughness, working conditions, i.e., rotational speeds and loads on pressure ripples and severity of asperity contacts. Results show effects of the speed, the load, as well as the RMS value are coupled which makes it difficult to evaluate lubrication states by only considering one variable. Considering the Ree-Eyring non-Newtonian behavior could alleviate pressure ripples significantly, compared with the Newtonian fluid assumption. Small RMS values of surfaces, which could be achieved by superfinish techniques, would be desirable when evaluating gear tooth surface contact performances.

Flexible pins eliminate the need for straddle mounting, and therefore enable the maximum possible number of planets to be used for any particular epicyclic ratio of power transmission systems. Having more planet gears will significantly increase the input torque density. In this type of design, the pin stiffness and position tolerances are important parameters as they affect the dynamic performances significantly. The present study addresses this issue by modeling, the design of double cantilevered flexible pin, and analyzing the contributions of pin stiffness and misalignment applying the lumped parameter approach. The proposed model formulates the coupled lateral-torsional dynamic response of a planetary spur gear, including the effects of mesh stiffness and phasing as a function of pin error. The resultant equations of motion are applied to examine the effects of pin stiffness and position errors on the natural modes and structural dynamic response. The effects of pin stiffness on deviation of the tooth contact forces of the sun-planet and ring-planet gear pairs are analyzed to understand the relationship between mesh characteristic and input speed variations. The calculated supporting forces of the planet gear are examined to understand the load sharing characteristic due to pin errors, pin stiffness and input load of the power transmission system.