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This comprehensive study delves deeply into the realm of electric vehicle (EV) battery temperature management, with a central focus on optimizing cooling systems using ethylene glycol solutions. The research rigorously examines the interplay between ethylene glycol concentrations and cooling methods on EV battery performance. ANSYS and MATLAB along with analytical methods were used for this project. Key findings reveal a consistent inverse relationship between ethylene glycol concentration and cooling efficiency, favoring lower concentrations. Indirect cooling, achieved with a 30% ethylene glycol solution, emerges as the most effective method, while direct cooling proves less efficient. Notably, mineral oil cooling at 0.04 m/s outperforms air cooling, albeit with higher energy consumption. Consequently, air cooling is recommended for smaller battery packs due to its lower power demand. The study also underscores the importance of selecting ethylene glycol concentrations based on environmental conditions, advocating lower concentrations for warmer climates and higher concentrations for colder regions. In summary, this research significantly advances EV technology by advocating for customized cooling system designs that consider environmental factors, battery specifications, and practical implementation. It provides a critical roadmap for enhancing EV thermal management systems' efficiency and sustainability. Further research is needed to explore the long-term effects of diverse cooling methodologies on battery longevity and performance within the practical constraints of EV integration. • Cooling effectiveness increases with lower concentration. • Indirect cooling outperforms direct methods. • Higher coolant flowrate enhances heat extraction but requires more energy. • A 50% ethylene glycol solution is suitable for most weather conditions. [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]

Serially connected batteries present a cell-to-cell variation in their electrochemical behavior that tends to increase over the lifetime. The decision for the best circular economy option for aged or defective battery packs – i.e., repair, remanufacturing, or recycling – requires comprehensive testing, analysis, and an according data basis. Remanufacturing of battery packs refers to the replacement of individual (sub-)modules or cells which are defective or show a diverging aging behavior to the rest of the battery units. A detection algorithm is required to decide if a significant inhomogeneity in a serial connection of batteries is present. For this reason, virtual battery packs are built based on the measurement of 12 individual batteries of the same type. Cell-to-cell variations are determined at the begin of life and a novel representation for the quantitative and qualitative analysis is given. This work extracts impedance-based features of different serial battery pack configurations through a novel comparative analysis approach. The features are extracted from Bode and Nyquist plots and the real and imaginary parts of the impedance itself. The detectability is analyzed depending on the number of cells and the underlying effects are discussed. A detailed sensitivity analysis is carried out for the most promising features, in which the influence of the cell-to-cell variations, the aging condition, and the aging mechanism are analyzed. The feature that shows the highest sensitivity, the so called low-frequency minimum, is able to detect single outliers within a high number of serially connected cells. [Display omitted] • Detection of inhomogeneities in serially connected lithium-ion batteries. • Novel representation of cell-to-cell variations for analysis of Nyquist plots. • Comprehensive analysis of multiple features for the detection of inhomogeneities. • Sensitivity analysis of the studied features concerning the detection accuracy. [ABSTRACT FROM AUTHOR]

• A pack comprising the electro-thermal coupling of individual cells was developed. • Z shape module shows a significant advantage over a ladder configuration. • The correlation between system inhomogeneity and design parameters was quantified. • Contact resistance has the highest impact on the inhomogeneity of a system. • There is not a strong correlation between system inhomogeneity and C-rate. Cell-to-cell variations can originate from manufacturing inconsistency or poor design of the battery pack/thermal management system. The potential impact of such variations may limit the energy capacity of the pack, which for electric vehicle applications leads to reduced range, increased degradation along with state of health dispersion within a pack. The latter is known to reduce the accessible energy and the overcharging/discharging of some of the cells within a system, which may cause safety concerns. This study investigates the short-term impact of such effects, which is highly important for designing of an energy storage system. A generic pack model comprising individual cell models is developed in Simscape and validated for a 1s-15p module architecture. The results highlight that a number of cells and interconnection resistance values between the cells are the dominant factors for cell-to-cell variation. A Z shape module architecture show a significant advantage over a ladder configuration due to the reduced impact of interconnection resistance on differential current flow within the module. Current imbalance is significantly higher for a ladder system and its magnitude is not dependent on the module current. Capacity variation does not have a significant impact on the system. By increasing the capacity variation from 9% to 40% the current inhomogeneity increases from 4% to 13%, whilst 25% resistance variation leads to 22% current dispersion. Further, a linear relationship is observed between the current inhomogeneity and thermal gradient (Δ T). A 30 °C Δ T leads to 24% current variation within the module. [ABSTRACT FROM AUTHOR]