Thermal Failure Propagation in Lithium-Ion Battery Modules with Various Shapes

Thermal failure propagation is one of the most severe challenges for battery modules and it usually aggravates the thermal hazards, further resulting in serious accidents. This work conducted two groups of experiments to investigate the influence of discharging treatment and module shape on the thermal failure propagation of battery modules, where the triangle module, rectangle module, parallelogram module, line module, hexagon module, and square module were researched. Based on the results, it can be found that an evident domino effect existed on the thermal failure propagation of battery modules. Namely, the failure propagation process consisted of several phases and the number of phases depended on the shape of the module. Besides, it is indicated that discharging treatment on a battery module when it was in a high-temperature environment would aggravate its thermal failure propagation by bringing an earlier thermal failure, a quicker failure propagation, and a larger mass loss. Combining the results of safety and space utilization, it is revealed that the triangular module may be the best choice of battery module due to its smaller failure propagation speed and higher space utilization

Investigation into the Fire Hazards of Lithium-Ion Batteries under Overcharging

Numerous lithium-ion battery (LIB) fires and explosions have raised serious concerns about the safety issued associated with LIBs; some of these incidents were mainly caused by overcharging of LIBs. Therefore, to have a better understanding of the fire hazards caused by LIB overcharging, two widely used commercial LIBs, nickel manganese cobalt oxide (NMC) and lithium iron phosphate (LFP), with different cut-off voltages (4.2 V, 4.5 V, 4.8 V and 5.0 V), were tested in this work. Some parameters including the surface temperature, the flame temperature, voltage, and radiative heat flux were measured and analyzed. The results indicate that the initial discharging voltage increases with the growth of charge cut-off voltage. Moreover, the higher the cut-off voltage, the longer the discharging time to reach 2.5 V. An overcharged LIB will undergo a more violent combustion process and has lower stability than a normal one, and the increasing cut-off voltage aggravates the severity. In addition, it is also revealed that the NMC fails earlier than the LFP under the same condition. The temperatures for safety vent cracking, ignition, and thermal runaway of LIBs exhibit similar values for the same condition, which demonstrates that the LIB will fail at a certain temperature. Finally, the peak heat flux, total radiative heat flux, and total radiative heat will rise with the increase in voltage

Thermal Failure Propagation in Lithium-Ion Battery Modules with Various Shapes

Thermal failure propagation is one of the most severe challenges for battery modules and it usually aggravates the thermal hazards, further resulting in serious accidents. This work conducted two groups of experiments to investigate the influence of discharging treatment and module shape on the thermal failure propagation of battery modules, where the triangle module, rectangle module, parallelogram module, line module, hexagon module, and square module were researched. Based on the results, it can be found that an evident domino effect existed on the thermal failure propagation of battery modules. Namely, the failure propagation process consisted of several phases and the number of phases depended on the shape of the module. Besides, it is indicated that discharging treatment on a battery module when it was in a high-temperature environment would aggravate its thermal failure propagation by bringing an earlier thermal failure, a quicker failure propagation, and a larger mass loss. Combining the results of safety and space utilization, it is revealed that the triangular module may be the best choice of battery module due to its smaller failure propagation speed and higher space utilization

Experimental study on the thermal behaviors of lithium-ion batteries under discharge and overcharge conditions

To have a better understanding of the thermal behaviors of lithium-ion batteries (LIBs) under discharge and overcharge conditions, some tests were conducted by a cone calorimeter. Several parameters were measured such as the battery surface temperature, voltage, the time to thermal runaway, the time to maximum temperature, heat release rate and total heat released. The results indicate that the lithium-ion battery will have obvious warming up during discharge owing to the reversible heat and irreversible heat. It was observed that the current treatment (discharge) has a significant influence on the thermal behaviors of LIBs. It can accelerate the warming up, result in earlier thermal runaway, and reduce the heat released. In addition, overcharge will make LIBs more unstable and easier to attain the thermal runaway

Physics-Informed Recurrent Neural Networks with Fractional-Order Constraints for the State Estimation of Lithium-Ion Batteries

The state estimation of lithium-ion battery is the basis of an intelligent battery management system; therefore, both model-based and data-driven methods have been designed and developed for state estimation. Rather than using complex partial differential equations and the complicated parameter tuning of a model-based method, a machine learning algorithm provides a new paradigm and has been increasingly applied to cloud big-data platforms. Although promising, it is now recognized that big data for machine learning may not be consistent in terms of data quality with reliable labels. Moreover, many algorithms are still applied as a black box that may not learn battery inner information well. To enhance the algorithm generalization in realistic situations, this paper presents a fractional-order physics-informed recurrent neural network (PIRNN) for state estimation. The fractional-order characteristics from battery mechanism are embedded into the proposed algorithm by introducing fractional-order gradients in backpropagation process and fractional-order constraints into the convergence loss function. With encoded battery knowledge, the proposed fractional-order PIRNN would accelerate the convergence speed in training process and achieve improved prediction accuracies. Experiments of four cells under federal urban driving schedule operation conditions and different temperatures are conducted to illustrate the estimation effects of the proposed fractional-order PIRNN. Compared to the integer-order gradient descent method, the fractional-order gradient descent method proposed in this work can optimize network convergence and obtains regression coefficient larger than 0.995. Moreover, the experimental results indicate that the proposed algorithm can achieve 2.5% estimation accuracy with the encoding fractional-order knowledge of lithium-ion batteries

Physics-Informed Recurrent Neural Networks with Fractional-Order Constraints for the State Estimation of Lithium-Ion Batteries

The state estimation of lithium-ion battery is the basis of an intelligent battery management system; therefore, both model-based and data-driven methods have been designed and developed for state estimation. Rather than using complex partial differential equations and the complicated parameter tuning of a model-based method, a machine learning algorithm provides a new paradigm and has been increasingly applied to cloud big-data platforms. Although promising, it is now recognized that big data for machine learning may not be consistent in terms of data quality with reliable labels. Moreover, many algorithms are still applied as a black box that may not learn battery inner information well. To enhance the algorithm generalization in realistic situations, this paper presents a fractional-order physics-informed recurrent neural network (PIRNN) for state estimation. The fractional-order characteristics from battery mechanism are embedded into the proposed algorithm by introducing fractional-order gradients in backpropagation process and fractional-order constraints into the convergence loss function. With encoded battery knowledge, the proposed fractional-order PIRNN would accelerate the convergence speed in training process and achieve improved prediction accuracies. Experiments of four cells under federal urban driving schedule operation conditions and different temperatures are conducted to illustrate the estimation effects of the proposed fractional-order PIRNN. Compared to the integer-order gradient descent method, the fractional-order gradient descent method proposed in this work can optimize network convergence and obtains regression coefficient larger than 0.995. Moreover, the experimental results indicate that the proposed algorithm can achieve 2.5% estimation accuracy with the encoding fractional-order knowledge of lithium-ion batteries

A Simplified Analysis to Predict the Fire Hazard of Primary Lithium Battery

To better understand the fire risk of primary lithium batteries, the combustion properties of different numbers of primary lithium batteries were investigated experimentally in this work. Based on the t2 fire principle and total heat release results from the experiments, a simplified analysis was developed to predict the fire hazard, and especially the heat release rate, of primary lithium batteries. By comparing the experiment and simulation results, the simulation line agrees well with the heat release rate curve based on the oxygen consumption measurements of a single primary lithium battery. When multiple batteries are burned, each battery ignites at different times throughout the process. The ignition time difference parameter is introduced into the simulation to achieve similar results as during multiple batteries combustion. These simulation curves conform well to the experimental curves, demonstrating that this heat release rate simulation analysis is suitable for application in batteries fires

Thermal performance of PCM and branch-structured fins for cylindrical power battery in a high-temperature environment

Battery modules with phase change material (PCM) cooling inevitably suffer from heat-storage saturation and poor secondary-heat dissipation, especially in high-temperature environments or hot regions. To optimize thermal management, this study firstly explores the thermal behaviors of PCMs with different phase change temperatures (PCTs) in a high-temperature environment. The experimental results show that a PCM with a PCT of 46 °C offers the best cooling effect at a high ambient temperature of 40 °C in this study. For example, the maximum temperature of a cell without PCM reaches 53.3 °C, whereas that of the cell with PCMs having PCTs 40, 46, and 55 °C, are 59.2, 51.6, and 57.5 °C, respectively, during the dynamic cycling process. Nevertheless, the application of above PCM is still unsatisfying because the maximum temperature of the battery in the PCM module exhibits obvious increasing trend with cycles in 40 °C environment. On this basis, several novel fins with multiple heat-flow channels (of V, Y and X shapes) are designed and introduced into the PCM module to enhance the secondary heat dissipation capability. These fins with innovative branch structures deliver excellent performance in alleviating the battery temperature than the traditional rectangular fins, which can be attributed to the ability of the branch structures to increase the heat transfer area by adding heat transfer channels. The results of this work show that the X-shape delivers the best performance in a high-temperature environment of 40 °C by maintaining the maximum temperature of the cell below 47 °C