This paper presents a numerical framework for automating the design of lithium-ion cells to maximize cell energy density while meeting specific power density requirements. The battery is modeled using a physics-based electrochemistry model that considers ion transport and includes mass balance and interfacial reactions [6 -7]. The design is automatically performed by coupling the battery model with a gradient-based optimization algorithm. We demonstrate the potential for gradient-based optimization by applying this framework to optimize the design of a lithium-ion cell with spinel manganese dioxide cathode and meso-carbon micro beads (MCMB) anode for a range of power requirements. Results indicate that variations in the electrode thickness and porosity at optimal cell designs can be quantified via active mass ratios and it is found that the active mass ratios for optimal cell designs are independent of discharge rate.
Lithium-ion batteries are now used in a wide range of applications, and much knowledge has been accumulated in the relevant physical phenomena. However, the application of this knowledge in battery design still relies on an inefficient manual process, in large part due to limitations in existing computational models. To address this, a multi-scale model is developed that incorporates microscopic simulation data for effective ion diffusivity and electronic conductivity, and interfacial electrochemical kinetics, into a macroscopic homogeneous model at the cell scale. Microscopic physics-based models are applied to 3D microstructures, and automated simulations are performed for statistically significant averaging of the results. A surrogate model couples the length scales by precomputing solutions based on a design of experiments. Results for the porosity-tortuosity relationship are compared to experimental data in the literature, and global sensitivity analysis is performed to quantify the relative impact of ion concentration and electric potential distribution on the electrochemical kinetics profile. The resulting multi-scale model successfully reproduces the microscopic solution while retaining the computational efficiency of the macroscopic homogeneous model. These attributes make it a suitable candidate for implementation in an automated simulation and optimization framework that may lead to a more efficient design process for high performance batteries.
A surrogate modeling framework is implemented to analyze the performance of a Li-ion cell with respect to four input variables: cycling rate, particle size, diffusivity, and electrical conductivity. Five different cathode materials (LiMn 2 O 4 , LiFePO 4 , LiCoO 2 , LiV 6 O 13 , and LiTiS 2) are modeled, and ranges for all material properties are selected based on reported data from the literature. The relative impact of the variables is quantified using global sensitivity analysis, and critical diffusivity and conductivity values are calculated. Two dimensionless parameters based on relative time and conductivity scales are defined and found to separate operating conditions into distinct regimes in which the cell performance is limited by diffusion or conduction. Combining the two dimensionless parameters into a single quantity and non-dimensionalizing the energy performance yields a Pareto-efficient set of solutions that are described well by the generalized logistic function, which can be considered a reduced-order model of battery performance with a global analytical solution.
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