Temperature heavily affects the behavior of any energy storage chemistries. In particular, lithium-ion batteries (LIBs) play a significant role in almost all storage application fields, including Electric Vehicles (EVs). Therefore, a full comprehension of the influence of the temperature on the key cell components and their governing equations is mandatory for the effective integration of LIBs into the application. If the battery is exposed to extreme thermal environments or the desired temperature cannot be maintained, the rates of chemical reactions and/or the mobility of the active species may change drastically. The alteration of properties of LIBs with temperature may create at best a performance problem and at worst a safety problem. Despite the presence of many reports on LIBs in the literature, their industrial realization has still been difficult, as the technologies developed in different labs have not been standardized yet. Thus, the field requires a systematic analysis of the effect of temperature on the critical properties of LIBs. In this paper, we report a comprehensive review of the effect of temperature on the properties of LIBs such as performance, cycle life, and safety. In addition, we focus on the alterations in resistances, energy losses, physicochemical properties, and aging mechanism when the temperature of LIBs are not under control.

The ability to accurately predict lithium‐ion battery life‐time already at an early stage of battery usage is critical for ensuring safe operation, accelerating technology development, and enabling battery second‐life applications. Many models are unable to effectively predict battery life‐time at early cycles due to the complex and nonlinear degrading behavior of lithium‐ion batteries. In this study, two hybrid data‐driven models, incorporating a traditional linear support vector regression (LSVR) and a Gaussian process regression (GPR), were developed to estimate battery life‐time at an early stage, before more severe capacity fading, utilizing a data set of 124 battery cells with lifetimes ranging from 150 to 2300 cycles. Two type of hybrid models, here denoted as A and B, were proposed. For each of the models, we achieved 1.1 % (A) and 1.4 % (B) training error, and similarly, 8.3 % (A) and 8.2 % (B) test error. The two key advantages are that the error percentage is kept below 10 % and that very low error values for the training and test sets were observed when utilizing data from only the first 100 cycles.The proposed method thus appears highly promising for predicting battery life during early cycles.

Lithium-ion batteries must undergo a series of quality control tests before being approved for sale. In this study, quality control tests were carried out on two types of lithium-ion pouch batteries, here denoted as type A (with stacked electrode configuration) and type B (with a jelly-roll arrangement) to assess the effectiveness of the tests. Electrochemical tests, which included capacity and impedance measurements, found that both types of batteries met the specifications. However, computed tomography (CT) scan, disassembly, and material characterization revealed quality concerns in battery assembly and material composition. Results showed that, for an A cell, cathode extended past anode at the top and bottom of the roll, and a CT scan revealed inhomogeneities in the electrode near the corners. Similarly, analysis of a B cell revealed gaps in the winding structure and cathode material discrepancies. More specifically, the lithium nickel manganese cobalt oxide (NMC) material specified by the battery manufacturer turned out to be lithium cobalt oxide (LCO). The findings indicate that systematic quality control tests are needed to properly identify defects in batteries before they are used in products.