Realistic lifetime prediction approach for Li-ion batteries

Sarasketa-Zabala, E.; Martinez-Laserna, Egoitz; Berecibar, Maitane; Gandiaga, I.; Rodrı́guez-Martı́nez, Lide M.; Villarreal, I.

doi:10.1016/j.apenergy.2015.10.115

Cited by 169 publications

(85 citation statements)

References 28 publications

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“…Despite being studied for many years with continued effort, understanding and modeling the lifetime of LIB is a field of continued research [43,44]. Comprehensive overview and description of aging phenomena are provided in [23].…”

Section: Aging Of Lithium-ion Batteriesmentioning

confidence: 99%

Lithium-Ion Battery Storage for the Grid—A Review of Stationary Battery Storage System Design Tailored for Applications in Modern Power Grids

et al. 2017

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Battery energy storage systems have gained increasing interest for serving grid support in various application tasks. In particular, systems based on lithium-ion batteries have evolved rapidly with a wide range of cell technologies and system architectures available on the market. On the application side, different tasks for storage deployment demand distinct properties of the storage system. This review aims to serve as a guideline for best choice of battery technology, system design and operation for lithium-ion based storage systems to match a specific system application. Starting with an overview to lithium-ion battery technologies and their characteristics with respect to performance and aging, the storage system design is analyzed in detail based on an evaluation of real-world projects. Typical storage system applications are grouped and classified with respect to the challenges posed to the battery system. Publicly available modeling tools for technical and economic analysis are presented. A brief analysis of optimization approaches aims to point out challenges and potential solution techniques for system sizing, positioning and dispatch operation. For all areas reviewed herein, expected improvements and possible future developments are highlighted. In order to extract the full potential of stationary battery storage systems and to enable increased profitability of systems, future research should aim to a holistic system level approach combining not only performance tuning on a battery cell level and careful analysis of the application requirements, but also consider a proper selection of storage sub-components as well as an optimized system operation strategy.

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Section: Aging Of Lithium-ion Batteriesmentioning

confidence: 99%

Lithium-Ion Battery Storage for the Grid—A Review of Stationary Battery Storage System Design Tailored for Applications in Modern Power Grids

et al. 2017

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“…Several purely empirical studies capture calendar aging 5,6 or cycle aging 7,8 without evaluating interdependencies. Through superposition, some empirical model approaches combine calendar and cycle aging [9][10][11][12] but tend to neglect the temperature dependence of the cycle aging mechanisms and are prone to extrapolation errors due to the utilized mathematical functions.…”

mentioning

confidence: 99%

Comprehensive Modeling of Temperature-Dependent Degradation Mechanisms in Lithium Iron Phosphate Batteries

Schimpe

Kuepach

Naumann

et al. 2018

J. Electrochem. Soc.

170

107

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For reliable lifetime predictions of lithium-ion batteries, models for cell degradation are required. A comprehensive semi-empirical model based on a reduced set of internal cell parameters and physically justified degradation functions for the capacity loss is developed and presented for a commercial lithium iron phosphate/graphite cell. One calendar and several cycle aging effects are modeled separately. Emphasis is placed on the varying degradation at different temperatures. Degradation mechanisms for cycle aging at high and low temperatures as well as the increased cycling degradation at high state of charge are calculated separately. For parameterization, a lifetime test study is conducted including storage and cycle tests. Additionally, the model is validated through a dynamic current profile based on real-world application in a stationary energy storage system revealing the accuracy. Tests for validation are continued for up to 114 days after the longest parametrization tests. The model error for the cell capacity loss in the application-based tests is at the end of testing below 1% of the original cell capacity and the maximum relative model error is below 21%. Today, stationary energy storage systems utilizing lithium-ion batteries account for the majority of new storage capacity installed. 1In order to meet technical and economic requirements, the specified system lifetime has to be ensured.For reliable lifetime predictions, cell degradation models are necessary. Physicochemical models that include aging mechanisms are based on a detailed set of parameters which are often not readily available, computationally costly and require experimental parameterization of degradation rates.2-4 Instead, purely empirical models can be parameterized without knowledge of internal cell setup through extensive testing. Several purely empirical studies capture calendar aging 5,6 or cycle aging 7,8 without evaluating interdependencies. Through superposition, some empirical model approaches combine calendar and cycle aging 9-12 but tend to neglect the temperature dependence of the cycle aging mechanisms and are prone to extrapolation errors due to the utilized mathematical functions.Due to the limited knowledge about degradation mechanisms, empirically based models conventionally lump multiple degradation effects into single functions. This leads to the aforementioned prediction errors when deviating from the parameterization test conditions. E.g. for cycle aging, Waldmann et al. reported a transition of dominating aging mechanisms at 25• C. 13 The aging for temperatures above 25• C was attributed to the solid-electrolyte interface (SEI) growth and cathode degradation, while below 25• C the aging was attributed to lithium plating. In fact, for an improved understanding of cell internal degradation, model development should aim for a separation of the degradation mechanisms wherever possible. The respective mechanisms can then be modeled through functions that are suitable for the degradation driving factors.In this work, ...

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“…Lithium ion batteries (LIBs) can be used both on static energy storage and transportation energy storage [4,5] thanks to their remarkable advantages such as high specific energy, high efficiency, and long life [6,7]. The two key tasks in battery management system (BMS) are to estimate state of charge (SOC) [8,9] and state of health (SOH) [10][11][12] to avoid battery abuse and prolong its life span [13]. SOC is the ratio of the remaining capacity to the nominal capacity of the battery [14,15], and SOH reflects the ability of the battery to deliver the peak power and energy compared with the initial state [16].…”

Section: Introductionmentioning

confidence: 99%

A capacity model based on charging process for state of health estimation of lithium ion batteries

et al. 2016

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Realistic lifetime prediction approach for Li-ion batteries

Cited by 169 publications

References 28 publications

Lithium-Ion Battery Storage for the Grid—A Review of Stationary Battery Storage System Design Tailored for Applications in Modern Power Grids

Lithium-Ion Battery Storage for the Grid—A Review of Stationary Battery Storage System Design Tailored for Applications in Modern Power Grids

Comprehensive Modeling of Temperature-Dependent Degradation Mechanisms in Lithium Iron Phosphate Batteries

A capacity model based on charging process for state of health estimation of lithium ion batteries

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