A real-time quantification of Li transport using a nondestructive neutron method to measure the Li distribution upon charge and discharge in a Li-ion cell is reported. By using in situ neutron depth profiling (NDP), we probed the onset of lithiation in a high-capacity Sn anode and visualized the enrichment of Li atoms on the surface followed by their propagation into the bulk. The delithiation process shows the removal of Li near the surface, which leads to a decreased coulombic efficiency, likely because of trapped Li within the intermetallic material. The developed in situ NDP provides exceptional sensitivity in the temporal and spatial measurement of Li transport within the battery material. This diagnostic tool opens up possibilities to understand rates of Li transport and their distribution to guide materials development for efficient storage mechanisms. Our observations provide important mechanistic insights for the design of advanced battery materials.
Evaluation of Li-ion cell performance and life requires the ability to predict behavior at extreme conditions, such as low temperatures and high C-rates. Most electrochemical models assuming constant electrolyte diffusion properties fail to accurately predict the electrode and electrolyte potential at such conditions. This study presents a physics-based Extended Single Particle Model (ESPM) designed specifically for accurately predicting the behavior of a Li-ion cell at extreme conditions, incorporating concentrationdependent properties in the electrolyte diffusion dynamics. Since the proposed model aims at supporting long-term simulation, virtual design and optimization studies, minimization of the computational complexity is achieved through analytical Model Order Reduction (MOR) based on a Galerkin projection method. Results show that the implementation of the concentration-dependent diffusion properties leads to significant improvement of model accuracy at extreme conditions. The Reduced Order Model (ROM) can be simulated significantly faster than numerical methods with no loss of accuracy, supporting simulation of long-term usage cycles (10-year) and remaining useful life calculations. Lithium ion batteries are considered the state of the art for energy storage in electric and hybrid vehicles. When batteries operates at low temperature conditions, for instance during a cold start of an electric vehicle, the highly reduced ionic conductivity and diffusivity lead to the formation of large gradients in the electrolyte concentration within the cell domain.1,2 Large concentration gradients can also be established at high C-rate conditions, which typically occur when the battery is subject to fast charge/discharge current loads in cold weather. Therefore, it is important to accurately predict the cell electrochemical behavior and terminal voltage at such conditions, as performing fast charging procedures at low temperatures could severely shorten the cycle life due to lithium deposition on the anode. 1,3,4 Electrochemical battery models based on first principles have shown the ability to accurately predict the concentration dynamics and terminal voltage. [5][6][7][8][9][10][11] In particular, the pseudo two-dimensional model (or the P2D model) based on Porous Electrode Theory 5 and Single Particle Model 12 (SPM) have been widely used for modeling the dynamic response of Lithium ion cells to variable input current profiles. Recently, Extended Single Particle Models (ESPM) [13][14][15][16][17] incorporating the electrolyte dynamics have been developed to capture the cell behavior under high C-rates conditions, albeit with simpler mathematical structure than P2D models.While electrochemical models generally predict well the cell terminal voltage as function of current and temperature, they are characterized by the presence of coupled PDEs and nonlinear algebraic equations, increasing the mathematical complexity and leading to computational challenges when applied to fast simulation and to estimation or control desig...
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