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Lithium‐ion cell chemistries are favored in the automotive sector, as they enable electric vehicles (EVs) to compete with traditional gasoline‐powered vehicles in terms of performance, range, and cost. The life and performance of these packs depend upon the Battery management system which monitors and controls the pack. The modeling, simulation, and analysis of a lithium‐ion battery pack that closely resembles an actual automobile battery are the focus of this paper. Real cells that are commercially available and specifically developed for automotive applications are modeled in the simulation environment. The cell characterization and formation of pack topology with heat exchange are addressed. A commercial electric car's power train model and EV dynamics are used in the simulation environment to test the modeled battery pack over various drive cycles. The performance analysis is upscaled by considering different aspects like resistive forces while driving, ambient temperature, initial conditions while the vehicle starts, etc. The battery manufacturer can verify the battery's performance under different load conditions, with a high degree of similarity to real‐world scenarios. The simulations are conducted on MATLAB/Simulink 2021b software. [ABSTRACT FROM AUTHOR]

Batteries are essential to mobilization and electrification as they are used in a wide range of applications, from electric vehicles to small mobile devices. All these devices are powered with AC or DC inside their systems, so they require different battery systems depending on their technical requirements. Batteries show unique characteristics depending on their types, and their needs vary based on their performance, ambient conditions, and so forth. One of the main demands for them is thermal stability. For batteries, thermal stability is not just about safety; it's also about economics, the environment, performance, and system stability. This paper has evaluated over 200 papers and harvested their data to build a collective understanding of battery thermal management systems (BTMSs). These studies are specifically designed to solve different problems. This paper has been prepared to show what these systems are, how they work, what they have been designed for, and under what conditions they should be applied. The BTMSs have been evaluated based on their method, method tools, discharge rate, maximum temperature, temperature difference values, and ambient and inlet temperatures. After evaluating over 200 studies, the results indicate that the passive BTMSs are not useful the cases where the temperature reaches higher values suddenly, especially for system systems that require higher discharge rates. On the other hand, active cooling methods do not manage the temperature difference in the battery cells. However, hybrid cooling methods address both cases admirably by compensating for both of their weaknesses and bringing out their advantages. The general optimum temperature for lithium battery batteries is 55°C. Even though there are many other parameters that need to be considered before making a decision for a BTMS design, the best performance for an optimum system seems to be methods 34, 38, and 22 as they are able to provide lower maximum temperature and temperature difference in the cells. [ABSTRACT FROM AUTHOR]

The heat dissipation around battery cells should be thoroughly examined to keep the battery pack running properly. This article mainly focuses on the 3D analysis of thermal distribution in lithium‐ion battery (LIB; 14650, 18650 and 26650) with varied geometry sizes and the thermal distribution of LIB packs (6s and 12s series) at different charge/discharge rate. It has been concluded that the capacity loss increases at higher SOC levels for the calendar aging as derived from the lumped model approach. Additionally, the rate of heat generation with the increase in battery size and its effect on the overall battery temperature has been observed. The proposed work has been simulated in COMSOL Multiphysics 5.6 for observing the temperature variations within the battery pack and the vital findings are stated from the conclusions. [ABSTRACT FROM AUTHOR]

There is not much research carried out on factors such as temperature differences in different regions of battery pack, which is highly important and poses a great challenge. Temperature changes across batteries affect the battery pack performance and its life span largely. Thus, the present work proposes the Integrated Finite Element‐Meta‐Optimization Framework for solving this problem. The integrated framework includes the formulation of Finite Element Model of a battery pack and Meta‐Optimization Framework for multi‐objective minimization of three objectives such as the inter‐cell temperature differences, temperature standard deviation and the total volume of the cell. In this framework, firstly Support Vector Regression (SVR) model is formulated based on the data and the SVR parameters are optimized using a genetic algorithm. It can conclude that the resulting model has a coefficient of correlation metric of 99%. The simulated annealing algorithm is then applied to find the optimal values of inter‐cell and cell‐enclosure distances for minimization of inter‐cell temperature differences and the total volume of the cell. Optimization analysis concluded that the volume of the battery pack was decreased by 29%, temperature difference (K) by 42% and SD of the temperature over the entire pack decreased by 55%. [ABSTRACT FROM AUTHOR]