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Revealing Lithium-Ion Battery Internal Uncertainty through a Combined Electrochemical Based Degradation Battery Model with a Markov Chain Monte Carlo Approach
Technical Paper
2020-01-0450
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
The battery electrodes are porous and have complicated microstructures due to irregular sizes and shapes of pores. Electrode design parameters like porosity, thickness, particle size, and conductivity can vary from one local area to another in one cell. Even under the normal operating current, some local areas may experience over-charge/over-discharge, extensive degradation, and rapid heat generation. These local events are not easy to observe or measure at onset and can eventually lead to catastrophic failure of the whole cell if no actions are taken. Battery design parameters are treated as constant values in many electrochemical battery models. However, the uncertainty of these parameters has an adverse effect on the longevity and safety of batteries. To prevent any catastrophic failure, a battery model is needed to predict any potential over-charge/over-discharge and rapid heat generation caused by internal uncertainty. In this work, a new method is developed to simulate random local events and design new control strategies to increase longevity and safety. An electrochemical-based battery degradation model is integrated with Monte Carlo simulation to capture the unknown random design parameters within a cell. Random samples are taken to represent the complex microstructure. The battery model can simulate major side reactions, lithium plating, state of health, and heat generation. This process opens the possibility to predict the uncertainties of batteries without detailed 3D modeling and can also be used for battery design optimization, battery management systems, and state estimations due to its flexibility and reasonable computational cost. The simulation results show that under the normal cutoff voltage range, local areas experience over-charge/over-discharge and high heat generation, which can damage the overall safety and longevity of batteries.
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Liu, C., "Revealing Lithium-Ion Battery Internal Uncertainty through a Combined Electrochemical Based Degradation Battery Model with a Markov Chain Monte Carlo Approach," SAE Technical Paper 2020-01-0450, 2020, https://doi.org/10.4271/2020-01-0450.Data Sets - Support Documents
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