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Thermal runaway propagation of the power battery pack is an essential factor affecting the safety of electric vehicles. The commonly adopted propagation inhibition methods mainly include adding heat insulation materials and enlarging battery spacing, which could cause problematic heat dissipation and lower the system energy density. Herein, an innovative battery thermal management system composed of non-uniform thermal conductivity phase change materials and assisted liquid cooling is proposed. Combining the phase change materials with high and low thermal conductivity balances heat transfer and heat insulation requirements. The cooling performance and the ability of thermal runaway propagation mitigation of the proposed schemes are numerically studied. The results show that the proposed strategy can meet the heat dissipation requirements under normal operation and control the thermal runaway in a safe range by transferring the heat generated from the battery thermal runaway in the set direction. The maximum battery temperature and the temperature difference are 38.1 °C and 2.1 °C, respectively, under 3C discharge. Under thermal runaway conditions, the strategy successful confines the thermal runaway propagation within the middle row. The maximum battery temperature in other rows can be controlled under the irreversible thermal runaway reaction temperature of 200 °C. Further study found that increased thermal conductivity benefits the battery heat dissipation and reduces the risk of thermal runaway. However, it propagates faster and broader once the thermal runaway is triggered. In comparison, the decrease of thermal conductivity is beneficial to the mitigation of propagation but may reduce the overall heat dissipation of the battery module. This study can provide a new way to solve the contradiction between battery temperature control and thermal runaway spread suppression. • An innovative strategy is proposed to mitigate thermal runaway propagation. • Non-uniform phase change materials with different thermal conductivity are adopted. • The thermal runaway generated heat can be limited to transfer in a specific direction. • A suitable combination of high and low thermal conductivity for PCM is necessary. • Temperature control and thermal runaway suppression can be achieved simultaneously. [ABSTRACT FROM AUTHOR]

The employment of readily obtainable parameters, including voltage and temperature, derived from the battery management system facilitates the real-time evaluation of lithium-ion batteries health status. However, the complex aging mechanisms and noise interference inherent in lithium-ion batteries present substantial challenges to accurate estimation. Hence, this research proposes a multi-model fusion approach utilizing Gramian Angular Field encoding to accurately predict the health status of lithium-ion batteries. Battery voltage and temperature data are acquired through system aging experiments. The Gramian Angular Field-based image encoding transforms these data into images, accentuating the subtle characteristics of the data and thereby enhancing its recognizability by neural networks. Subsequently, two-dimensional Convolutional Neural Networks are employed to extract features from the images, followed by one-dimensional Convolutional Neural Networks to reduce the dimensions of the feature matrix. The battery health status is then predicted utilizing Long Short-term Memory Networks. The culmination is the evaluation of the model performance through the application of quantitative error metrics. The study results show that the method can pinpoint the prediction error to within 2%, significantly improving accuracy over traditional prediction methods. It proves the great potential of direct data, such as voltage and temperature, in predicting battery health. • Predicting lithium-ion battery health state based on multi-easily accessible data. • The temperature and voltage data are converted into 2D images by applying GAF method. • Feature extraction and SOH prediction using CNN-LSTM fusion models. [ABSTRACT FROM AUTHOR]

The demand for high energy density in lithium-ion battery packs for electric vehicles poses a challenge to maintaining its optimum operating temperature while reducing the risk of thermal runaway (TR) propagation. This study proposes a novel hybrid TR propagation mitigation system that balances heat transfer and thermal insulation requirements using low and high thermal conductivity phase change materials (PCM), heat pipes (HP), and air-cooling. The design and optimization of such a mitigation system are complex due to the many design parameters involved. The Adaptive-Kriging-High dimensional model representation (Adaptive-Kriging-HDMR) is used to establish a surrogate model of the system, and the sensitivity of the system's design parameters is evaluated with the maximum battery temperature and the system weight as the targets, thereby improving the efficiency of model calculation and reducing the dimension of optimization parameters. Then, the design of the sensitive parameters is optimized using an extended elitist non‐dominated sorting genetic algorithm (E-NSGA-II) multi-objective optimization algorithm. The results show that the modeling difficulty and optimization calculation time are significantly reduced by using a surrogate model. The calculation time for a single surrogate model only takes a few seconds instead of several hours for the original three-dimensional heat transfer and flow calculation. The thermal conductivity of high thermal conductivity-PCM, the distance between battery and low thermal conductivity-PCM, the battery spacing, and the HP length significantly affect the system. The optimized system substantially reduces the overall weight of the battery system while ensuring its good heat dissipation capability. In the case of TR in a single battery, the system succeeds in limiting the TR propagation to the same row, with the maximum battery temperature in the second row being only 64.3 °C, well below the battery TR trigger point. Under more severe conditions, such as TR occurring in two batteries simultaneously, the maximum battery temperature in the second row is 155.5 °C, and no TR spreads to the adjacent row. This study provides a rapid and effective method for designing a TR propagation mitigation system. It can serve as a reference for the engineering design and optimization of battery thermal management systems. [ABSTRACT FROM AUTHOR]

Summary: With the increasing demand for the driving range of new energy vehicles, the energy of power battery is increasing. As a temperature‐sensitive component, the life, safety, and charge–discharge performance of lithium‐ion power battery will be affected by the working temperature, which makes the battery thermal management system particularly important. Concerning the fact that the thermal contact resistance between the power battery and the heat transfer components has become the bottleneck of battery thermal management system, this study firstly establishes a traditional sandwich structure of phase change material–based thermal management system and then try to reduce the thermal contact resistance of the system by increasing the thermal conductivity of the cooling part and filling the interface with high thermal conductivity material. The thermal contact resistance and the heat flux between the power battery and the thermal management module are experimentally tested and calculated. Based on the external and internal temperature of battery, the effect of contact resistance on the performance of a thermal management system is discussed. The results show that when increasing the thermal conductivity of the cooling part, the battery external and internal temperatures under 3C, 4C, and 5C discharge are reduced by 8.3%, 8.1%, 7.6% and 5.2%, 9.3%, 6.3%, respectively, as compared to the traditional sandwich structure of phase change material–based thermal management system. And the temperatures are further reduced by 6.5%, 7.7%, 6.3% and 7.8%, 6.1%, 7.3% when the interface between the battery and the thermal management module are filled with graphene with high thermal conductivity. Furthermore, the power battery can work within the optimum operating temperature range for a long period and can ensure battery safety by reducing the thermal contact resistance. [ABSTRACT FROM AUTHOR]

• The physical effects of battery correlated equivalent circuit model parameters. • Low and medium-frequency EIS sensitive to battery aging. • Combined circuit parameters and gated recurrent unit to estimate battery SoH. • Considering the effect of ambient temperature and SoC on EIS. Electrochemical impedance spectroscopy (EIS) is one of the critical techniques to characterize the state of health (SoH) of lithium-ion batteries, which has different performances under different SoH and dynamic working conditions. However, the identification parameters of full-frequency EIS will consume too much computing time. Considering that low and medium-frequency EIS plays a dominant role in battery aging, the present paper constructs a simplified equivalent circuit (SECM) based on low and medium-frequency EIS. Based on the fitted SECM parameters, three SECM parameters are selected as features by grey correlation analysis. Combined with the ambient temperature and SoC under dynamic conditions, the Gated recurrent unit neural network estimates the SoH of lithium-ion batteries. The proposed method has good accuracy and robustness for different SoC states and ambient temperature variations. This method's error values are less than 2 %, which proves the critical value of low and medium-frequency EIS signals in battery management systems. [ABSTRACT FROM AUTHOR]

• A hybrid thermal management system with PCM, HP and liquid cooling is proposed. • A surrogate model is established based on Adaptive-Kriging-HDMR method. • Four sensitive factors are determined through global sensitivity analysis. • The optimized system is evaluated in the condition of cycling and thermal runaway. The hybrid thermal management scheme for lithium-ion battery combining the advantages of various thermal management strategies has been widely adopted. However, due to the complex influence parameters involved in the hybrid thermal management system, its optimal design has become a difficult problem. In this study, a hybrid thermal management system based on phase change material (PCM), liquid cooling, and heat pipe is first designed, and then a precise and reliable numerical heat transfer model is established. In order to optimize the system and improve the optimization efficiency, the Adaptive-Kriging-High dimensional model representation (HDMR) method is used to construct a surrogate model of the thermal management system, and the influencing factors sensitivity analysis and optimization design of the hybrid thermal management system are also conducted. The results show that the thermal conductivity of PCM, the thickness of PCM, the length of heat pipe and the velocity of inlet water have a significant influence on the maximum temperature and temperature difference of the battery system. According to the optimization design of these four factors based on the multi-objective particle swarm optimization (MOPSO), it is found that the optimized thermal management system has the best ability to dissipate heat and maintain temperature uniformity as compared to the original design. In addition, this optimization system has the ability to prevent thermal runaway propagation under the condition of thermal abuse conditions. With these prominent performances, the proposed method is expected to provide insights into the engineering design and optimization of the battery thermal management system for electric vehicle. [ABSTRACT FROM AUTHOR]