Battery failures are obvious after being subject to abuse conditions however predicting these failures in advance is crucial when using test and validation techniques to understand battery potential. Lithium-ion battery cells are widely used due to their high energy and power densities. When abusive conditions like the three-point bend loading are applied to lithium-ion batteries, what occurs to the mechanical behaviours and components is still mostly unknown. To further this understanding, this paper investigates the mechanical behaviour of the separator in the LiCoO2/Graphite cylindrical 18650 cells. Internal short circuit (ISC) behaviour, strain rate dependency, and electrochemical status of the cells (i.e. SOC dependency) are studied to understand failure patterns. Furthermore, a simple and effective constitutive model for the separator layer is formed, facilitating further mechanical analysis and numerical simulation of lithium-ion battery study. The occurrence of ISC is investigated by jellyroll deformation where the casing is removed, and quasi-static load is applied. A numerical simulation model is developed to further investigate sequential structural failures and temperature changes. Simulation results showed good accuracy with experimental results and are useful to predict structural failure of cells. The number of failures including electrolyte leakage, change in shape, sudden voltage drop/temperature rise, and gas venting is observed.

This article outlines the objective, experimental design, and methodology used to evaluate the impact of first-life degradation on the second-life performance of lithium-ion batteries. To achieve this, 18 new cylindrical cells were subject to continuous electrical cycling at various charge-discharge rates until they reached 80% State of Health (SoH). The ageing process was carried out at a constant temperature of 25 °C. Reference Performance Tests (RPTs) using 1C and C/10 charge-discharge cycles at 25 °C were conducted on new cells and repeated at each ageing stage to quantify the SoH based on the level of retained capacity.

Lithiumion (Li-ion) batteries have become increasingly useful within the automotive industry and modern life applications due to high energy and power densities. However, these batteries suffer capacity loss due to different ageing mechanisms in various applications. Despite several existing models, lack of accurate predictability of capacity degradation limits the advancement of Li-ion batteries. The present work focuses on prediction of battery useful capacity degradation using long-short term memory (LSTM) transfer learning neural network model. At first, a base model was developed and trained using all the (100 ^% ) degradation data available at 0°C and 10°C environmental temperatures. Thereafter, the training of the base model was fixed, and additional hidden layers were added on top of the base model to further fine tune it with only the initial 30% degradation data available at 25°C environmental temperature. The remaining (70 ^% ) data of the 25°C case was tested for model prediction. To decide the number of fixed hidden layers to be transferred from base model to transfer model and the number of additional hidden layers on top, an optimization for minimum cross validation error was performed. It was found that the resulting model was able to forecast the remaining battery degradation with 96% accuracy. The model prediction was also compared with LSTM deep learning architecture without using transfer learning. The LSTM with transfer learning model was found to be 17% higher in prediction accuracy than that without utilizing transfer learning.

Induction motor is an extremely non-linear system and it poses a great challenge for the fault detection scheme due to large and complex data processing. Induction motor fault can lead to huge losses and excessive downtimes with regards to maintenance and production. An external fault like unbalanced voltage supply could be very sever and result in excessive losses, mechanical oscillations, overvoltage, and interference with control electronics. Detection of an abnormality like unbalanced voltage supply is a challenging task in the interaction of electrical motor and the power grid. In this paper, a new method is presented to diagnose unbalanced voltage supply at the incipient stage. In the proposed method, first of all, a new approach is introduced to formulate the total number of winding turns associated with a particular slot. After the formulation, the unbalanced voltage supply is diagnosed through signal processing, symbolic time series analysis and D-Markov state transition. The proposed method also distinguishes motor operation under balanced and unbalanced voltage supply. In the proposed work, hardware setup is designed for experimental verification. For validation of the method, an experimental setup is designed to justify and distinguish the motor operation under balanced and unbalanced voltage supply.