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In this paper, a reduced-order, Multi-Scale, Multi-Dimensional (MSMD) model is developed to achieve accurate and fast simulation of the 2D distribution of thermal and electrochemical properties across the surface of a large-format Li-ion battery cell. 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. The model is then verified against experimental data and a high-fidelity numerical model at various input current conditions. Results show that the computational complexity of the MSMD model is significantly reduced without sacrificing the accuracy in characterizing the distribution of the electrochemical and thermal properties. Finally, the impact of the applied current and thermal boundary conditions on the distribution of transverse current density is also evaluated. [ABSTRACT FROM AUTHOR]

Increased demand for hybrid and electric vehicles has motivated research to improve onboard state of charge (SOC) and state of health estimation (SOH). In particular, batteries with composite electrodes have become popular for automotive applications due to their ability to balance energy density, power density, and cost by adjusting the amount of each material within the electrode. SOH algorithms that do not use electrochemical-based models may have more difficulty maintaining an accurate battery model as the cell ages under varying degradation modes, such as lithium consumption at the solid-electrolyte interface or active material dissolution. Furthermore, efforts to validate electrochemical model-based state estimation algorithms with experimental aging data are limited, particularly for composite electrode cells. In this paper, we first present a reduced-order electrochemical model for a composite LiMn2O4-LiNi1/3Mn1/3Co1/3O2 electrode battery that predicts the surface and bulk lithium concentration of each material in the composite electrode, as well as the current split between each material. The model is then used in dual-nonlinear observers to estimate the cell SOC and loss of cyclable lithium over time. Three different observer types are compared: 1) the extended Kalman filter; 2) fixed interval Kalman smoother; and 3) particle filter. Finally, an experimental aging campaign is used to compare the estimated capacities for five different cells with the measured capacities over time. [ABSTRACT FROM PUBLISHER]

Abstract: The design of predictive, electrochemical, Li-ion battery models and the use of model-order reduction techniques to extract low-order models enables researchers to address problems related to model-based control, estimation, aging, and life-cycle prediction. This paper presents a novel approach to the design and parametrization of a reduced-order model characterizing the dynamic voltage response of a Li-ion cell. The model is based on the assumption of uniform active material utilization and includes prediction of the ionic concentration and potential dynamics in the liquid phase, which significantly affects the voltage response for high currents. A model-order reduction procedure based on the Pade approximation method is used to reduce the partial differential equation model to a low-order system of ordinary differential equations. Systematic methods are proposed to identify the electrochemical parameters that govern power and capacity prediction, as well as their temperature dependence. The procedure is based on carefully designed experiments that isolate the influence of small groups of parameters on the voltage output, utilizing complete cell data for model identification almost entirely in place of half-cell characterization. Finally, an extensive validation with experimental data illustrates the ability of the model to accurately predict the cell voltage. [Copyright &y& Elsevier]