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The safety and reliability of lithium-ion battery system is the main safeguard promoting widespread applications. However, the reliability assessment of the battery system is still insufficient due to dynamic changes and ageing propagation between cells. To evaluate the reliability of battery system accurately, an improved reliability assessment method integrating the multi-physics model and universal generating function (UGF) method is proposed, taking imbalanced current and uneven cooling into account. In the multi-physics model, an electric-thermal-flow-ageing coupling model is established. The UGF-based method is employed to evaluate the multi-state system reliability of battery system. Based on the reliability method, the interaction influence of imbalanced current and uneven cooling on the reliability of the battery system is analyzed contrastively. To explore the influence of parallel cells on reliability, different redundancy strategies are evaluated and analyzed. Meanwhile, suitable uneven cooling schemes are studied. The results show that the degradation rate of cells is accelerated while incorporated the imbalanced current and uneven cooling. Compared with the traditional reliability assessment, the maximum reliability deviation reaches approximately 1780 cycles. Unlike initial assumption, increasing the parallel cells does not always improve the reliability of battery packs due to the improvement of probability of current imbalance. • A reliability assessment method for battery system is developed. • Multi-physical model contains electric, thermal, flow and ageing. • Analysis interaction of imbalanced current and uneven cooling for reliability assessment. • The effect of imbalanced current on different redundancy strategies is analyzed. • Increasing parallel cell does not always improve the system reliability. [ABSTRACT FROM AUTHOR]

Complex environments and variable working conditions lead to irreversible attenuation of battery pack capacity in electric vehicles (EVs). Online capacity estimation is of great significance for battery pack management and maintenance. This work proposes a state-of-health (SOH) attenuation model considering driving mileage and seasonal temperature for battery health estimation. Firstly, a variable forgetting factor recursive least square (VFFRLS) algorithm is proposed for battery model parameter identification. It adaptively adjusts the forgetting factor according to current fluctuations. Then, an extended Kalman-particle filter (EPF) algorithm is proposed for online capacity estimation. In addition, a battery pack SOH attenuation model is constructed considering seasonal temperature and driving mileage. Finally, the performance of the proposed model and algorithm is verified with nine months of actual vehicle data. The experimental results show that the proposed parameter identification and capacity estimation algorithm can accurately estimate the model parameters and capacity. The average capacity of the battery module decreases with the total mileage. The compensation of monthly driving mileage and ambient temperature factors effectively improves the accuracy of SOH model. • A SOH attenuation model considering temperature and mileage is proposed for EVs. • A variable forgetting factor RLS is proposed for battery parameter identification. • Multidimensional equation of state is constructed for capacity estimation with EPF. • Nine month real vehicle data are used to validate the proposed algorithm and model. [ABSTRACT FROM AUTHOR]

The thermal safety of lithium-ion (Li-ion) batteries is of great importance for their further development and application. The accurate evaluation of the thermal safety boundary has always been the key issue. Due to the coupling effect of the internal, external, and randomness factors, the risk probability is difficult to evaluate using existing deterministic analyses. Therefore, a safety risk assessment method for the thermal abuse of Li-ion battery packs is proposed, and an improved bisection-method-based analysis algorithm for the thermal safety boundary is established. Moreover, a multiphysics model is developed considering an equivalent circuit, thermal abuse, and a fluid dynamics model. Furthermore, stochastic models of the battery parameters and loading are constructed to describe the randomness. The temperature and power stress–strength interference models are integrated to evaluate the thermal safety risk of the battery pack. Then, the models are validated by the temperature and analyzing the stochastic parameters. Finally, several case studies are implemented, including the analyses of thermal safety boundary, effect of stochastic parameters, safety risk probability, and thermal runaway propagation in different scenarios. The results denote that the effect of internal resistance dispersion is dominant and that the risk probability will increase with degradation. • A novel risk probability evaluation method for battery pack is proposed. • The proposed method considers the multiphysics and randomness for the first time. • The improved bisection method-based algorithm can improve the efficiency of analysis. • The effect of stochastic parameters on the thermal safety boundary is analyzed. • The thermal safety risk probability of new and degraded battery pack is evaluated. [ABSTRACT FROM AUTHOR]

This study proposes a hybrid kernel function relevance vector machine (HKRVM) optimized model for battery prognostics and health management. To monitor battery state of health (SOH), two ageing features (AFs) are extracted from the incremental capacity curve to quantify capacity degradation. To further predict remaining useful life (RUL), the AFs are treated with the BOXCOX transformation to enhance the linearity between AFs and cycles. Then, a metabolic extreme learning machine is developed to successionally predict the degradation trends of AFs quickly and accurately. The HKRVM is proposed to capture the underlying relationship between AFs and capacity. To determine the optimal weights and kernel parameters in HKRVM, the biological evolution in the genetic algorithm (GA) is integrated into the grey wolf optimizer (GWO) to further improve the population diversity and optimization performance of the basic GWO. Furthermore, 23 benchmark functions are employed to illustrate the effectiveness of the genetic grey wolf optimizer (GGWO). Finally, the extracted and predicted AFs are fed into the optimized HKRVM for validation. For four battery packs, the mean error of estimated SOH is 2.7773% and the predicted RUL is 21.3333 cycles, which is better than the Gauss process regression, support vector regression, and some particle-filter-based methods. • Two AFs from battery cells are extended to battery packs for aging evaluation. • BOXCOX transformation is utilized to enhance the linearity between AFs and cycles. • A MELM is proposed to predicted the AFs successionally. • An HKRVM is developed for synchronous SOH estimation and RUL prediction. • A GGWO is proposed to determine the optimal weight and kernel parameters of HKRVM. [ABSTRACT FROM AUTHOR]

Large-scale commercialization of electric vehicles (EVs) seeks to develop battery systems with higher energy efficiency and improved thermal performance. Integrating simulation-based design optimization in battery development process expands the possibilities for novel design exploration. This study presents a dual-stage multiphysics simulation optimization methodology for comprehensive concept design of Lithium-ion (Li-ion) battery packs for EV applications. At the first stage, multi-objective optimization of electrochemical thermally coupled cells is performed using genetic algorithm considering the specific energy and the maximum temperature of the cells as design objectives. At the second stage, the energy efficiency and the thermal performances of each optimally designed cell are evaluated under pack operation to account for cell-to-pack interactions under realistic working scenarios. When operating at 1.5 C discharge current, the battery pack comprising optimally designed cells for which the specific energy and the maximum temperature are equally weighted delivers the highest specific energy with enhanced thermal performance. The most favorable pack design shows 8% reduction in maximum pack temperature and 16.1% reduction in module-to-module temperature variations compared to commercially available pack. The methodology for design optimization presented in this work is generic, providing valuable knowledge for future cell and pack designs that employ different chemistries and configurations. • Novel battery pack design methodology is proposed through a multiphysics simulation optimization framework. • Multi-objective optimization is used for electrochemical thermally coupled battery cells. • Thermal performance of battery cells is essential to improved battery pack performance. • Generic simulation-based optimization methodology for future design of battery cells and packs. [ABSTRACT FROM AUTHOR]

Since a battery pack consists of hundreds of cells in series and parallel, inconsistencies between cells make it difficult to create an explicit model to simulate its behaviors effectively. Therefore, the widely used and sophisticated model-based methods (such as Kalman filters) are difficult to apply to SOC (state of charge) estimation of battery packs. In this paper, a data-driven method based on Gaussian process regression (GPR) is proposed to provide a feasible solution. Its superiority includes the ability to approximate nonlinearity accurately, nonparametric modeling, and probabilistic predictions. First, a feature extraction strategy, including data preprocessing, correlation analysis, and principal component analysis, is employed to obtain a compacted input set with a high correlation with SOC. Second, the squared exponential kernel function is used, and the automatic relevance determination is applied to optimize the weights of features. Third, besides the regular GPR model, an autoregressive GPR model is also constructed to further improve estimation accuracy and confidence. The experimental results verify that the autoregressive model has better SOC estimation performance than the regular model, and its estimation error under different dynamic cycles, temperatures, aging conditions, and even extreme conditions is lower than 3.9%, and the confidence interval is also much narrower. • A feature extraction method is employed to obtain a crucial and compacted data set. • Gaussian process regression is used to predict the state of charge of battery pack. • Automatic relevance determination is used to optimize the weights of features. • An autoregressive model is created to improve estimation accuracy and confidence. • The method is verified in different dynamic cycles, temperatures, and aging states. [ABSTRACT FROM AUTHOR]

The consistency of battery cells directly influences the maximum available energy and the efficiency of the battery pack, and the energy utilization efficiency (EUE) is a key parameter for the balancing of batteries. Therefore, this paper focuses on the consistency modeling and state estimation of battery packs. In this study, a Copula-based battery pack consistency modeling method is developed. The proposed method shows superiority compared with two existing methods, because it can describe the statistical characteristics of the battery consistency parameters, and the dependence structure between parameters. The squared Euclidean distances between the marginal empirical cumulative distribution functions of the test data and that of the proposed model for capacity, resistance, and SOC are 0.029, 0.169, and 0.025, respectively. The errors of the correlation coefficients between the proposed model and the test data are within 0.12. Then the framework of battery pack EUE estimation using the consistency model is proposed. The accuracy of the proposed method is verified based on the test results of a battery pack with 95 cells connected in-series. The EUE estimation error is within 0.6% at various discharge current rates. The EUE estimation results could provide support for the performance evaluation and balancing of battery packs. • A Copula-based lithium-ion battery pack consistency modeling method is proposed. • Comparisons between proposed modeling method and traditional methods are performed. • A framework to estimate the battery pack energy utilization efficiency is proposed. • The estimation error of battery pack energy utilization efficiency is within 0.6%. [ABSTRACT FROM AUTHOR]

Various battery thermal management systems have been proposed in the literature to keep the battery operating temperature within the optimum operating range of 15 °C to 35 °C. Doing so leads to increased battery pack service life, safer operation, and reduced costs. Here, a comparative assessment of battery thermal management systems is presented, focusing on the development of the most recent proposed category of battery thermal management systems, liquid-to-vapor phase change cooling systems, and a competitive assessment is performed among these systems and with other battery thermal management systems. It is found that for prismatic battery packs it is best to use the tube based system, where the coolant fills tubes that are embedded within a metallic cold plate that separates the batteries. However, for cylindrical battery packs only one configuration of the cooling system has been proposed in the literature and it is determined that the best coolant among the possible options is ammonia. Ammonia performs the best for both cylindrical and prismatic battery packs. Further research is recommended to adapt tube based systems for cylindrical battery packs, and to investigate other integrations between conventional cooling systems and liquid-to-vapor phase change systems. • Comparative assessment of recently developed categories of battery thermal management systems. • Focus on liquid-to-vapor phase change cooling systems. • Compares the liquid-to-vapor phase change cooling systems with conventional systems. [ABSTRACT FROM AUTHOR]