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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]

This study presents a multidisciplinary end-to-end design, build, and test drive experience of a Formula Society of Automatic Engineers (FSAE) electric vehicle. The design team members' background include computer engineering, electrical engineering, and mechanical engineering expertise. Each team member worked on a sub-team namely mechanical, drivetrain, supervisory control and data acquisition (SCADA), and battery management system (BMS). The mechanical team modified/redesigned a gasoline FSAE car frame to accommodate the battery pack, cooling systems, electric drive train, and SCADA hardware. The drivetrain team designed a combinational logic-based tractive system control board to enhance the safety and reliability of the drivetrain. The SCADA team used a model-view-controller software architecture and agile development practices to design and implement a custom electric vehicle operating system and the main brain hardware. The BMS team designed the sensing network and communication protocol to provide an update on the state of the health of the energy source. A thorough description of each sub-system is provided herein. System integration and field test results are also included. [ABSTRACT FROM AUTHOR]

This study proposes a novel hybrid energy storage system (HESS) composed of a battery pack and a superconducting magnetic energy storage (SMES) for electric vehicle. Typically, the SMES has a higher power density and lower energy density than other energy storage devices, while battery has higher energy density. Thus, the overall HESS performance can be increased in power and energy density by combining SMES and batteries. The proposed HESS is designed based on bidirectional Z‐source inverter (ZSI). Compared to other SMES/battery‐based HESS topologies that are two stage designs (including DC/DC and AC/DC converters), in this topology, SMES and battery can be incorporated into the Z‐source network which results in lower cost and improved HESS performance. Furthermore, the battery converter has been eliminated due to the buck/boost feature of the ZSI. The fuzzy control method and filters are used to distribute power between the SMES and battery. This study also describes the proposed HESS performance principles and its operation in different modes. [ABSTRACT FROM AUTHOR]

Abstract: This work aims to make a comparative analysis of the unbalanced discharging phenomenon for battery packs with series/parallel configurations due to the temperature differences among the cells. A theoretically‐based model is developed for the battery pack and constant power discharging processes are simulated by the model. At a constant temperature difference, lowering the operating temperature increases the divergence among the cell terminal voltages for the series pack and the cell discharging currents for the parallel pack. Increasing the temperature differences decreases the dischargeable energy of the series battery pack, whereas it has little effect on that of the parallel configuration. Given the same temperature difference, the cell energy differences within the parallel battery pack are 5–10 times higher than those within the series configuration, which shows that the temperature can be maintained much more uniformly for a parallel battery pack than for series configuration. [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]