Three-Dimensional Thermal Modeling of Internal Shorting Process in a 20Ah Lithium-Ion Polymer Battery

To better address the safety issues of a lithium-ion battery, understanding of its internal shorting process is necessary. In this study, three-dimensional (3D) thermal modeling of a 20 Ah lithium-ion polymer battery under an internal shorting process is performed. The electrochemical thermal coupling scheme is considered, and a multi-scale modeling approach is employed. An equivalent circuit model is used for characterizing the subscale electrochemical behaviors. Then, at the cell scale, the electrical potential field and thermal field are resolved. For modeling the internal shorting process, a block of an internal short is directly planted inside the lithium-ion battery. Insights of the temperature evolutions and 3D temperature distributions are drawn from the simulations. The effects of shorting resistance, through-plane thermal conductivity, and mini-channel cold-plate cooling are investigated with the simulations. A large amount of heat generation by a small shorting resistance and highly localized temperature rise are the fundamental thermal features associated with the internal shorting process. The through-plane thermal conductivity plays an important role in the maximum temperature evolutions inside the battery cell, while the external cooling condition has a relatively weak effect. But the cold plate cooling can benefit lithium-ion battery safety by limiting the high temperature area in the internal shorting process through heat spreading.

Ambient Temperature Effect on Performance of a Lithium-Ion Polymer Battery Cell for 12-Voltage Applications

Operating temperature has a significant impact on the performance, safety, and cycle lifetime of the lithium-ion batteries. The operating temperature of a battery is the result of the ambient temperature augmented by the heat generated by the battery. This paper presents the empirical investigation of the effect of ambient temperature on the performance of a Lithium-Nickel-Manganese-Cobalt-Oxide based cell with 3.6V nominal voltage and 20Ah capacity. The experiments are carried out in an environment chamber using five controlled temperatures at −20°C, −10°C, 0°C, 20°C, and 50°C, as the ambient temperatures. In each controlled temperature test, a constant current (10A, 20A, and 40A) continuously discharge the cell to a cut-off 2.5V. The cell discharging voltages and usable capacities are the battery performance indicators. The experimental tests show that discharging voltage at 50% DOD and the total discharging time to reach 2.5V (usable capacity) increase as the ambient temperature increases. The modeling and simulation of a battery cell temperature model is built in the Simulink platform. The correlations show that simulated and experimental discharging curves match well in the 0–80% DOD range and the discrepancy is under 7%. The developed simulation model could provide thermal management guidelines for lithium-ion polymer battery applications in 12 voltage SLI, start-stop, and 48 voltage mild hybrid electric vehicles.

Comparative Life Cycle Assessment of Mobile Power Banks with Lithium-Ion Battery and Lithium-Ion Polymer Battery

Mobile power bank (MPB) is an emerging consumer electronic that stores and delivers electricity to other electronics. Nowadays, MPBs are produced and discarded in massive quantities, yet their environmental impacts have never been quantitatively evaluated. Employing a life cycle assessment (LCA) approach, this study assesses the life cycle environmental impacts of MPBs, with a specific focus on comparing the environmental performance of different MPBs that are based on two types of batteries, namely, lithium-ion battery (LIB) and lithium-ion polymer battery (LIPB). The results suggest that battery production is the greatest contributor to the environmental impacts of both MPBs. LIPB based MPB is environmentally friendlier due to its higher energy density and longer cycle life. In addition, it is found that recycling can reduce the environmental burden of MPB industry as well as ease the vast depletion of metals such as cobalt and copper. The sensitivity analysis shows that figuring out an optimal retirement point and using less carbon-intensive electricity can reduce the climate change potential of MPBs. This study provides recommendations to further improve the environmental performance of MPB, including the usage of more sustainable cathode materials, market promoting direction, and formulation of end-of-life management policy.

Transient Temperature Distributions on Lithium-Ion Polymer SLI Battery

Lithium-ion polymer batteries currently are the most popular vehicle onboard electric energy storage systems ranging from the 12 V/24 V starting, lighting, and ignition (SLI) battery to the high-voltage traction battery pack in hybrid and electric vehicles. The operating temperature has a significant impact on the performance, safety, and cycle lifetime of lithium-ion batteries. It is essential to quantify the heat generation and temperature distribution of a battery cell, module, and pack during different operating conditions. In this paper, the transient temperature distributions across a battery module consisting of four series-connected lithium-ion polymer battery cells are measured under various charging and discharging currents. A battery thermal model, correlated with the experimental data, is built in the module-level in the ANSYS/Fluent platform. This validated module thermal model is then extended to a pack thermal model which contains four parallel-connected modules. The temperature distributions on the battery pack model are simulated under 40 A, 60 A, and 80 A constant discharge currents. An air-cool thermal management system is integrated with the battery pack model to ensure the operating temperature and temperature gradient within the optimal range. This paper could provide thermal management design guideline for the lithium-ion polymer battery pack.

Bubble Detection in Lithium-ion Polymer Cell Sheet Using Extreme Learning Machine

Background: As lithium-ion polymer battery has high energy density and it is easy to be manufactured into different shapes, it arouses more interests of both technology and application recently. The quality of the lithium-ion polymer battery is essential to all the applications, and the detection of bubble defect in cell sheets is critical to the quality control of batteries. Recent patents on flaw detection in cell sheet are reviewed. Method: A novel application is developed to detect bubble defect in cell sheets of lithium-ion polymer battery by using extreme learning machine. The image processing methods and the selected features for bubble detection are detailed. Gaussian mixture model density estimation for extreme learning machine is developed to solve the problem of lack of enough flaw samples for classification learning. Results: The comparison of classification correction rate of different methods showed that the classification accuracy of the proposed method was between 99% and 100%. The proposed method was able to keep the superior performance of accuracy with the different sample numbers, and it had most satisfactory performance with varies of sample number. Experimental results also showed that the number of nodes in the hidden layer had little influence on the classification accuracy in the proposed method. Conclusion: All these experiments have shown that the proposed method has the best performance and the proposed bubble detection method is more efficient than other learning-based methods, and the proposed method has the potential to defect detection in other image processing applications.