Browsing by Subject "Battery simulation"

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Efficient models and algorithms for mass conservation and morphology evolution in lithium metal batteries
(2023-04-19) Jang, Taejin; Subramanian, Venkat R.; Manthiram, Arumugam; Mitlin, David; Hwang, Gyeong S.; Roberts, Scott A.
The demand for energy storage devices with high energy density, coulombic efficiency, long-term stability, and high capacity while ensuring safety has never been higher. However, the efforts towards carbon neutrality and exploration of next-generation batteries for electric vehicles and mobile applications are still insufficient to meet the demands. The development of Lithium-ion batteries is a giant leap in achieving the utilization of lithium, which has high reactivity, mobility, and superior energy density along with high output voltage. However, there is one major area of improvement to advance the conventional lithium-ion batteries: the anode electrode. Lithium metal has the highest energy density among the other potential candidates for anodes, ahead of conventional graphite electrodes which are based on the intercalation of lithium ions. Despite the early research interests, the metal anode was not commercially successful due to safety concerns and inferior cyclability. Even today, those defects are challenging and need further research. Thus, to resolve the above-mentioned difficulties, there is a significant need for a fundamental understanding of the morphology changes during the deposition and stripping, specifically, the anomalies in the microscale, such as the formation of dendrites, local cavitation, and initial surface defects. These translate into macroscale as dendrite growth, depletion of the electrolyte by the continuous solid-electrolyte interface (SEI) layer growth, and formation of isolated regions called 'dead lithium'. This thesis focuses on the physics-based models and algorithms at different scales and varying complexity of the system to simulate the evolution of the anode surface in lithium metal batteries. This includes continuum, mesoscale and multiscale models conserving the system's total mass. The different approaches, such as coordinate transformation and phase-field model, are discussed with proper mathematical reformulation. Lastly, an effective algorithm for fast and accurate simulation is proposed with selected examples.
Hybrid neural net and physics based model of a lithium ion battery
(2011-05) Refai, Rehan; Chen, Dongmei, Ph. D.; Fernandez, Benito R.
Lithium ion batteries have become one of the most popular types of battery in consumer electronics as well as aerospace and automotive applications. The efficient use of Li-ion batteries in automotive applications requires well designed battery management systems. Low order Li-ion battery models that are fast and accurate are key to well- designed BMS. The control oriented low order physics based model developed previously cannot predict the temperature and predicts inaccurate voltage dynamics. This thesis focuses on two things: (1) the development of a thermal component to the isothermal model and (2) the development of a hybrid neural net and physics based battery model that corrects the output of the physics based model. A simple first law based thermal component to predict the temperature model is implemented. The thermal model offers a reasonable approximation of the temperature dynamics of the battery discharge over a wide operating range, for both a well-ventilated battery as well as an insulated battery. The model gives an accurate prediction of temperature at higher SOC, but the accuracy drops sharply at lower SOCs. This possibly is due to a local heat generation term that dominates heat generation at lower SOCs. A neural net based modeling approach is used to compensate for the lack of knowledge of material parameters of the battery cell in the existing physics based model. This model implements a neural net that corrects the voltage output of the model and adds a temperature prediction sub-network. Given the knowledge of the physics of the battery, sparse neural nets are used. Multiple types of standalone neural nets as well as hybrid neural net and physics based battery models are developed and tested to determine the appropriate configuration for optimal performance. The prediction of the neural nets in ventilated, insulated and stressed conditions was compared to the actual outputs of the batteries. The modeling approach presented here is able to accurately predict voltage output of the battery for multiple current profiles. The temperature prediction of the neural nets in the case of the ventilated batteries was harder to predict since the environment of the battery was not controlled. The temperature predictions in the insulated cases were quite accurate. The neural nets are trained, tested and validated using test data from a 4.4Ah Boston Power lithium ion battery cell.