4.7 Article

A rapid estimation and sensitivity analysis of parameters describing the behavior of commercial Li-ion batteries including thermal analysis

Journal

ENERGY CONVERSION AND MANAGEMENT
Volume 87, Issue -, Pages 472-482

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.enconman.2014.06.076

Keywords

Commercial Li-ion batteries; First-principle modeling; Parameters; Sensitivity analysis

Funding

  1. CONACYT [2012-183230]
  2. NSERC - Automotive Partnership Canada

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In this work, a methodology based on rigorous model fitting and sensitivity analysis is presented to determine the parameters describing the physicochemical behavior of commercial pouch Li-ion batteries of high-capacity (16 A h), utilized in electric vehicles. It is intended for a rapid estimation of the kinetic and transport parameters, state of charge and health of a Li-ion battery when chemical information is not available, or for a brand new system. A pseudo 2-D model comprised of different contributions reported in the literature is utilized to describe the mass, charge and thermal balances of the cell and porous electrodes; and adapted to the battery chemistry under study. The sensitivity analysis of key model parameters is conducted to determine confidence intervals, using Analysis of Variance (ANOVA) for non-linear models. Also individual multi-parametric sensitivity analysis is conducted to assess the impact of the model parameters on battery voltage. The battery is comprised of multiple cells in parallel containing carbon anodes and LiNi1/3Co1/3Mn1/3O2 (NMC) cathodes with maximum and cut-off voltages of 4.2 and 2.7 V, respectively. Mass and charge transfer limitations during the discharge/charge of the battery are discussed as a function of State of Charge (SOC). A thermal analysis is also conducted to estimate the temperature rise on the surface of the battery. This modeling methodology can be extended to the analysis of other chemistry types of Li-ion batteries, as well as the evaluation of other material phenomena including capacity fade. (C) 2014 Elsevier Ltd. All rights reserved.

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