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The successful electrification of the transportation and stationary power markets requires batteries with 10-30 years of lifetime. Validations of such long time scales can take several years and require significant confidence in future life predictions. Battery life predictions across such timescales can be highly sensitive to uncertainty in the collection of primary parameters such as current, voltage and time. A methodology to quantify how these primary parameters propagate into the derived parameters of interest such as capacity and coulombic efficiency is presented. The recent development of high precision battery testing and equipment has met the need for accurate and precise assessment of measurement errors. As an example of the derived quantity uncertainty propagation methodology and approach developed in this paper, a prototype high current, high precision tester is also evaluated. The aim of this paper is to describe a standardized method for the precise quantification of battery derived parameter uncertainty, apply the method to the performance of a prototype high current, high precision tester (HPT) as a case study, and then to model the sensitivity of these precision results across a population of possible test conditions. [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]