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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]

• Battery thermal management is studied with phase-change materials and heat pipes; • Phase change materials improve the temperature uniformity of the battery pack; • Phase change-heat pipe cooling reduces by 22.3% battery temperature than using air; • Phase change material RT31 reduces by 11.8 % battery temperature at 5–50 W·m−2·K−1; • Short circuit of combination cooling reduces by 29.9% temperature than air cooling. Lithium-ion battery packs generate high-level heat under harsh and rigorous conditions. The inadequate heat dissipation results in high pack temperature above the safe operating range. This study investigates an efficient and cost-effective battery thermal management system based on phase change materials and a micro heat pipe array. The battery is treated as a single domain. A battery pack with six series stacked batteries is analyzed using the Newman-Tiedemann-Gu-Kim model. The thermal performance of a battery thermal management system is analyzed using phase change materials with various melting temperature values, ambient temperature, and convective heat transfer coefficients of micro heat pipe arrays. Results show that the maximum temperature of the cooling system with micro heat pipe arrays and phase change materials is 33.8 °C, which is reduced by 22.3 % and 7.8 % compared to the air-cooled and cooling system with micro heat pipe arrays. At forced convection of 50 W·m−2·K−1, the maximum battery temperature values for the phase change material RT31, RT35, and RT42 are reduced by 11.8 %, 8.9 %, and 5.4 % compared to those at forced convection of 5 W·m−2·K−1. Under short-circuit conditions, the cooling system with micro heat pipe arrays and cooling system with micro heat pipe arrays − phase change materials reduce the maximum battery temperature by 12.4 % and 29.9 % compared to the air-cooled system. Results show that the developed model for the battery thermal management system provides a reference for designing phase change materials with micro heat pipe arrays. [ABSTRACT FROM AUTHOR]

With the increasing scale of battery systems, the impact of battery inconsistency due to aging on battery pack performance becomes increasingly significant. To achieve high-precision battery pack modeling, we propose an in-situ estimation method for battery inconsistency parameters. The proposed method utilizes current and voltage data recorded by the battery management system (BMS). The respective terminal voltage errors are used as the loss function, and the equivalent circuit model and the fully connected neural network are combined to realize the estimation of inconsistency parameters. Through optimization algorithm, the inconsistency parameters of all cells in the battery pack are simultaneously estimated. The estimation results of full capacity, high-end capacity, and internal resistance exhibit high accuracy with root-mean-square error (RMSE) values of 0.82% (0.393 Ah), 0.70% (0.336 Ah), and 3.34% (0.097 m Ω), respectively. The battery pack model constructed using the estimation results maintains high accuracy across various operating conditions, demonstrating the effectiveness of the proposed method for inconsistency estimation and battery pack modeling. • Estimate the battery inconsistency parameters based on the similarity of the battery SOC-OCV curves with the BMS data. • Combine the neural network and equivalent circuit model to simultaneously estimate the capacity, internal resistance and SOC parameters of each cell in the battery pack. • The accuracy of the proposed method was demonstrated by comparing measured parameters with estimated parameters, and the effectiveness was further demonstrated by battery pack model simulation results. [ABSTRACT FROM AUTHOR]