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

doi:10.1016/j.jpowsour.2012.05.012

Journal of Power Sources

Volume 215, 1 October 2012, Pages 248-257

https://doi.org/10.1016/j.jpowsour.2012.05.012 Get rights and content

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.

Highlights

► Extended accelerated aging tests on lithium-ion batteries. ► Semi-empirical aging model based on extended calendar aging data. ► Impedance-based electro-thermal model coupled to aging model. ► Lifetime prediction under real application condition possible concerning capacity fade.

Introduction

Lifetime prediction for lithium-ion batteries under real operation conditions is a key issue for a reliable integration of the battery not only into the vehicle but also for stationary applications and for warranty issues. As aging tests using real operation conditions are very time and cost intensive, accelerated aging tests are discussed to be a powerful method. To extrapolate data obtained from accelerated aging test to real life conditions, aging models are required. So far simple model approaches for lifetime predictions have been reported in literature, like e.g. approaches based on neuronal networks [1]. These approaches usually lack the ability to make extrapolations to conditions that were not used in the learning test set. On the other hand, physic-chemical models have been developed, focusing on the description of single aging mechanisms like formation of solid electrolyte interphase (SEI) [2], [3], mechanical stresses [4], etc. These models have been used for parameter studies, helpful to understand the ongoing process. Nevertheless they are not appropriate for fast lifetime predictions, as they are difficult to parameterize and only describe single mechanisms. This work aims at a compromise between physico-chemical and simple neuronal network model approaches. A physical approach based on an impedance model, able to extrapolate the data from accelerated aging tests to get real life condition lifetime predictions is presented here. It is an important goal of this semi-empirical approach to derive a set of equations for describing the aging using mathematical expressions that are close to the main degradation mechanisms.

Aging in lithium-ion batteries leads to increase of inner resistance, capacity and power loss as well as to changes in impedance spectra due to electrochemical and mechanical processes. Aging strongly depends on temperature, SOC or rather electrode potential, cycling depth and charge throughput [5], [6], [7]. Few studies are reported in literature, investigating the calendar and cycle life of different cells using large test matrixes [7], [8], [9], [10]. These studies illuminate the aging characteristics of lithium-ion batteries. But so far, this knowledge has not been utilized to develop an aging model that is able to predict the lifetime cycle of real application. Wang et al. [10] made the attempt of a lifetime prediction model based on cycle aging results, but didn't include the strong influence of SOC on aging.

Aging models based on mathematical functions obtained from extended aging tests can be directly linked to impedance-based models, which determine electrical and thermal behavior of the battery [11], [12]. Coupling of impedance-based thermo-electrical battery models with aging models enables investigation of the dynamical interaction between thermal, electrical and aging behavior of the battery. A higher temperature for example causes a faster aging and therefore a faster increase in the inner resistance, affecting the electrical performance of the battery. These relations have been investigated in [13] but lacking a profound parameterization of the developed model using aging test results. This work will focus on the parameterization of the aging model by experimental data using extended aging test results.

Section snippets

Experimental

To parameterize impedance-based aging models, extensive aging tests are necessary. In this work a lithium-ion high power pouch cell with a nominal capacity of 6 Ah and a nominal voltage of 3.6 V was used. The anode consists of hard carbon, the cathode of LiNi_1/3Mn_1/3Co_1/3O₂ (NMC) as active material. Cells with similar characteristics are typically used in hybrid electric vehicle (HEV) applications.

Extended accelerated calendar aging tests have been performed by storing batteries at constant

Calendar and cycle aging results

In order to develop and parameterize an aging model, the calendar aging tests were evaluated. In this section the most important results of the aging data are discussed and summarized to support the assumptions made for the setup of the model.

It is widely known from literature, that electrolyte decomposition and the corresponding formation of solid electrolyte interphase (SEI), is the dominant aging process in most graphite-based lithium-ion batteries during storage leading to capacity decline

Mathematical description of aging behavior

Based on the considerations of the previous section, a lifetime model following a semi-empirical approach can be developed. It has been shown from the aging tests, that the following simplifications and assumptions can be used:

•
As the cycle life of the batteries exceeds by far the requirements of application, cycle aging is neglected in the following.
•
Different definitions of resistances have been evaluated over aging. It has been found that they evolve in a similar way over aging. Exemplarily,

Development of lifetime model

The fitting results of the calendar aging data can be used to develop a model to predict lifetimes under different operation conditions by combining an impedance-based electro-thermal model with the mathematical expressions obtained from the test results. Such a semi-empirical model approach for lifetime predictions has the advantage of an easy parameterization based on accelerated calendar life tests and an acceptable computing time. Moreover the approach enables extrapolations to different

Simulation result

For the simulation a realistic current profile for HEV according to VDA was used to cycle the battery and to investigate the aging under realistic operation conditions. Using the profile shown in Fig. 2 the batteries were cycled at 40 °C between 60% and 80%, 45%–65% and 30%–50% SOC. The profile was also applied to cells during aging tests as described in section 3. For the comparison with the cycle data from the test results no temperature model was used in the following simulation. The cell

Conclusion

In this work a simulation model was presented to predict lifetime of a high power lithium-ion battery under realistic operation condition. The model approach couples an impedance-based electric-thermal model to a semi-empirical aging model, to account for the impact of aging on the dynamic behavior of the battery. The aging model is based on results obtained from extended accelerated aging tests, which were used to parameterize the model. From the results of the aging test, simplifications for

Acknowledgment

This work has been done in the framework of the research initiative “KVN” funded by the German Federal Ministry for Education and Research, funding number 13N9973. Responsibility for the content of this publication lies with the authors.

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