Development of a lifetime prediction model for lithium-ion batteries based on extended accelerated aging test data

By Madeleine Ecker, Jochen Bernhard Gerschler, J. Vogel, S. Käbitz, Friedrich Hust, Philipp Dechent, Dirk Uwe Sauer. 2012

604 citations recorded by the source corpus.

Abstract Battery lifetime prognosis is a key requirement for successful market introduction of electric and hybrid vehicles. This work aims at the development of a lifetime prediction approach based on an aging model for lithium-ion batteries. A multivariable analysis of a detailed series of accelerated lifetime experiments representing typical operating conditions in hybrid electric vehicle is presented. The impact of temperature and state of charge on impedance rise and capacity loss is quantified. The investigations are based on a high-power NMC/graphite lithium-ion battery with good cycle lifetime. The resulting mathematical functions are physically motivated by the occurring aging effects and are used for the parameterization of a semi-empirical aging model. An impedance-based electric-thermal model is coupled to the aging model to simulate the dynamic interaction between aging of the battery and the thermal as well as electric behavior. Based on these models different drive cycles and management strategies can be analyzed with regard to their impact on lifetime. It is an important tool for vehicle designers and for the implementation of business models. A key contribution of the paper is the parameterization of the aging model by experimental data, while aging simulation in the literature usually lacks a robust empirical foundation.

Subfields: Automotive Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/eba60a2a7119bc1d82e66286f4b73c8a64c0ac4b

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Citations in the corpus

A control-oriented cycle-life model for hybrid electric vehicle lithium- ion batteries (2016, background): “[18], a lifetime prediction model for lithium-ion batteries is validated on profiles defined by the VDA (German association of the automotive industry).”
A review on fault mechanism and diagnosis approach for Li-Ion batteries (2015, background): “The temperature varies with climate and thermal management; load profile lies on the driving mode [40], and voltages of individual cells depend on the balancing protocol.”
Cycle aging of commercial NMC/graphite pouch cells at different temperatures (2015, background): “At temperatures of +60 C and above, the aging mechanisms at graphite negative electrodes have been observed to change when compared to lower temperatures [22,23].”
Impact of the electricity mix and use profile in the life-cycle assessment of electric vehicles (2013, background): “Overcharge, over-discharge, high Depth of Discharge (DOD)s and high temperatures also influence the fast decay of the battery life and low temperatures can also have a negative impact, mainly during the charging phase [46,51].”
A Review on Temperature-Dependent Electrochemical Properties, Aging, and Performance of Lithium-Ion Cells (2020, background): “Therefore, in order to comply with the life and safety requirements, the cells of a pack must remain within a well-defined temperature range, this range for the most common type of LIBs is between 10–50 °C [10,13,23].”
Gaussian Process Regression With Automatic Relevance Determination Kernel for Calendar Aging Prediction of Lithium-Ion Batteries (2020, background): “case is another promising research topic [32], [36].”
A brief review on key technologies in the battery management system of electric vehicles (2018, methodology): “[73], a cycle-life model was validated for the Li-ion battery based on the profiles proposed by the VDA (German association of the automotive industry).”