Short communicationModeling capacity fade in lithium-ion cells
Introduction
Accurate prediction of a battery's life, either the calendar or the cycle life, is a great technical challenge to any battery application. Battery life is an important issue for both traction and stationary applications because it is critically related to battery reliability and dependability, which in turn determines a power source's quality and eventual life-cycle cost. A battery's actual service life, however, depends on its history, during both the storage/standby and mission/duty periods experienced by the battery through its lifetime. Any sensible approach to predict battery life therefore has to address impacts from both the storage/standby and mission/duty periods.
In the past decade, due to substantial improvements in computer computation power and software capability, battery modeling and simulation [1], [2], [3], [4], [5], [6], [7], [8] has enjoyed significant advancements. At the same time, experimental techniques that allow detailed investigation of interfacial and bulk properties of electrode materials have contributed to a better understanding of cell performance and degradation [9]. It is feasible to develop an integrated battery testing and simulation capability to assist battery R&D and operation [5], [6].
There are a few attempts in the past to predict lithium-ion battery capacity. For example, Fuller et al. [10] used a ‘first-principles’ electrochemical model to estimate lithium-polymer cell capacity. Rakhmatov et al. [11] proposed an analytical model for lithium-ion cells used in portable electronic systems that can predict battery lifetime. Spotnitz [12] incorporated SEI growth into Fuller's model and began to look into the correlation of impedance change with capacity fade. Ramadass et al. [13] attempted to incorporate solvent reduction reaction into their first-principles electrochemical model to predict capacity fade.
We are much interested in taking a comprehensive, concurrent and hybrid approach [7], [8] to develop tools and strategies that can predict the end-of-life (EOL) of a battery. Determination of the EOL of a battery system in service is very difficult, expensive, and often destructive to the system. Therefore, non-invasive, non-destructive techniques that can determine the EOL without removing the system from service are highly desired.
Sandia has been involved in studying the performance of a group of 18650-size lithium-ion batteries (LIB) that was fabricated for the US Department of Energy (USDOE) Advanced Technology Development (ATD) Program. These batteries contain a high-power chemistry [9], [14], [15], [16]. One of the objectives of the ATD Program is to develop procedures to make rapid comparisons of performance–degradation rates and predictions of battery life. This paper will discuss a simple LIB modeling approach that uses an equivalent-circuit model (ECM) to simulate cell performance, particularly the capacity fade phenomenon. This example shows that we can simulate battery performance changes due to thermal aging, which is one of the most influential factors impacting battery calendar life during storage, standby or operation periods.
Section snippets
The equivalent-circuit model (ECM)
There are several ECM approaches of different nature and flavor reported by others (e.g., [17], [18], [19]) for simulating LIB performance. A schematic of the ECM used in this work is shown in Fig. 1. The model resembles to that used by Verbrugge and Conell [19] for Ni-MH cells. We favor this model due to its simplicity, yet flexibility, in describing an electrochemical system via the separation of all ohmic resistance components from all faradic non-linear components, as they are lumped into R1
Experimental
Quallion LLC (Sylmar, CA) provided test cells as part of the USDOE ATD Program efforts, and Sandia along with four other National Labs was commissioned to evaluate the cell performance for hybrid electric vehicle applications. Detailed descriptions of the chemistry, cell configuration, test protocols and procedures, and test results can be found in [9], [16], [20]. In short, this high-power cell chemistry uses a cathode consisting of LiNi0.8Co0.15Al0.05O2, an anode fabricated with MAG-10
Results and discussion
Fig. 4 shows a series of discharge curves at various rates, from C/25 to 10C, simulated by the ECM using Eq. (4). Also shown are two sets of experimental data obtained at C/25 and C/1 rate. The very high degree of agreement between the actual data and the simulated results gives us a high level of confidence about the resistance values that we used in the model. It is worth mentioning that the fit of the parameters is also demonstrated by the ability to capture the essence of the shape of the
Conclusion
We have demonstrated that high-fidelity simulation of battery performance and capacity fade can be achieved with a simple equivalent-circuit model using a consistent set of parameters that reflect thermal aging. This high-performance simulation can assist us to predict the battery life under thermal-aging conditions, which are the most critical factors in determining the battery calendar life. With increasing accuracy in predicting the battery performance and degradation, we can use this
Acknowledgments
Sandia National Laboratories is a multi-program laboratory operated by Sandia Corporation, a Lockheed-Martin Company, for the United States Department of Energy's National Nuclear Security Administration under Contract DE-ac04-94AL85000. The USDOE FreedomCAR and Vehicle Technology Office through the ATD High-Power Battery Program funded the lithium-ion battery testing and data collection. The battery-life prediction modeling was funded by USDOE under the Energy Storage Program.
References (20)
- et al.
J. Power Sources
(2001) - et al.
J. Power Sources
(2002) - et al.
Solid State Ionics
(2002) - R. G. Jungst, D. H. Doughty, B. Y. Liaw, G. Nagasubramanian, H. L. Case, E. V. Thomas, Proceedings of the 40th Power...
- et al.
J. Power Sources
(1999) - et al.
Electrochim. Acta
(2002) - et al.
J. Electrochem. Soc.
(2002) - PNGV Test Plan for Advanced Technology Development Gen 2 Lithium-Ion Cells, EHV-TP-121, Revisions 1 through 6, US...
Electrochemical Systems
(1991)- et al.
J. Electrochem. Soc.
(1998)
Cited by (186)
Parameter sensitivity analysis of a multi-physics coupling aging model of lithium-ion batteries
2024, Electrochimica ActaA review of research in the Li-ion battery production and reverse supply chains
2023, Journal of Energy StorageDeep feature extraction in lifetime prognostics of lithium-ion batteries: Advances, challenges and perspectives
2023, Renewable and Sustainable Energy ReviewsEmpirical calendar ageing model for electric vehicles and energy storage systems batteries
2022, Journal of Energy StorageA PV ramp-rate control strategy to extend battery lifespan using forecasting
2022, Applied Energy