Prediction of battery storage ageing and solid electrolyte interphase property estimation using an electrochemical model

Ashwin, T.R.; Barai, Anup; Uddin, Kotub; Somerville, Limhi; McGordon, Andrew; Marco, James

doi:10.1016/j.jpowsour.2018.03.010

Cited by 37 publications

(33 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In contrast to other research studies [22][23][24][25][26], we used the data-driven PBSID identification algorithm instead of physicochemical parameter measurements to obtain the model parameters. This difference mainly relates to the time-effective and application-oriented advantages involved in the PBSID algorithm: it provides the researchers with the ability to identify battery model with multiple inputs and multiple outputs and does not require any physicochemical related information.…”

Section: Discussionmentioning

confidence: 99%

“…However, input/output data are sampled in discrete form in real applications. We can rewrite Equation (26) in the discrete form as:…”

Section: Model Discretizationmentioning

confidence: 99%

“…In addition, electrochemical models usually consist of a series of partial differential equations, (PDEs), which make them computationally expensive. Recently, many reduced-order electrochemical models [22][23][24][25] have been proposed for different purpose, including State-of-Charge estimation [25], aging prediction [26,27], optimal charging control [28,29], and the optimization of battery design [30]. Compared to the empirical models and ECMs, the electrochemical models can achieve higher accuracy with clear physical meanings.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multi-Level Model Reduction and Data-Driven Identification of the Lithium-Ion Battery

Yang

Liu

et al. 2020

Energies

View full text Add to dashboard Cite

The lithium-ion battery is a complicated non-linear system with multi electrochemical processes including mass and charge conservations as well as electrochemical kinetics. The calculation process of the electrochemical model depends on an in-depth understanding of the physicochemical characteristics and parameters, which can be costly and time-consuming. We investigated the electrochemical modeling, reduction, and identification methods of the lithium-ion battery from the electrode-level to the system-level. A reduced 9th order linear model was proposed using electrode-level physicochemical modeling and the cell-level mathematical reduction method. The data-driven predictor-based subspace identification algorithm was presented for the estimation of lithium-ion battery model in the system-level. The effectiveness of the proposed modeling and identification methods was validated in an experimental study based on LiFePO4 cells. The accuracy and dynamic characteristics of the identified model were found to be much more likely related to the operating State of Charge (SOC) range. Experimental results showed that the proposed methods perform well with high precision and good robustness in the SOC range of 90% to 10%, and the tracking error increases significantly within higher (100–90%) or lower (10–0%) SOC ranges. Moreover, to achieve an optimal balance between high-precision and low complexity, statistical analysis revealed that the 6th, 3rd, and 5th order battery model is the optimal choice in the SOC range of 90% to 100%, 90% to 10%, and 10% to 0%, respectively.

show abstract

Section: Discussionmentioning

confidence: 99%

“…However, input/output data are sampled in discrete form in real applications. We can rewrite Equation (26) in the discrete form as:…”

Section: Model Discretizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multi-Level Model Reduction and Data-Driven Identification of the Lithium-Ion Battery

Yang

Liu

et al. 2020

Energies

View full text Add to dashboard Cite

show abstract

“…weight and conductivity. These parameters are very difficult to measure with the state of the art technology [2]. Initial results of the parameterisation is given in Ashwin et al [23], where the authors aimed to predict the SEI properties like density, molecular mass and conductivity by observing the trend of degradation for a storage only condition.…”

Section: Implementation Of Error Into Electrochemical Modellingmentioning

confidence: 99%

“…In order to find an optimal li-ion battery for an application, OEMs utilize different battery models, e.g. electrochemical models [2][3][4], equivalent circuit models [5,6], degradation models [7,8]. These battery models also underpin the design of the key functions within the battery management system (BMS) control functions such as the, such as state of charge (SoC) and state of health (SoH) estimation and the design if the thermal management system (TMS).…”

Section: Introductionmentioning

confidence: 99%

An insight into the errors and uncertainty of the lithium-ion battery characterisation experiments

Taylor

Barai

Ashwin

et al. 2019

Journal of Energy Storage

Self Cite

View full text Add to dashboard Cite

show abstract

Role of SEI layer growth in fracture probability in lithium‐ion battery electrodes

Ali

Iqbal

Lee

2020

Intl J of Energy Research

View full text Add to dashboard Cite

Understanding of degradation mechanisms in batteries is essential for the widespread use of eco-friendly vehicles. Degradation mechanisms affect battery performance not only individually but also in a coupled manner. Solid electrolyte interface (SEI) formation deteriorates battery capacity through consuming available lithium ions. On the other hand, as the SEI layer grows over multiple cycles, the level of mechanical constraints is changed, which can affect the fracture behavior of the active particles. We investigate the effect of the SEI layer growth on the fracture probability of the electrode particles. The simulations show that as the SEI layer grows, tensile stress inside the active particles turns into compressive stress, reducing the probability of particle fracture. Once the SEI layer is fractured, the particle fracture is sequentially more likely to happen because the SEI constraint is removed. The study emphasizes that the stability of SEI layers is important because it helps in alleviating electrochemical performance fade as well as mechanical failure probability. In addition, the SEI layer on small particles tends to be more fractured than that on large particles, suggesting that the particle size uniformity is essential for reducing the fracture probability of the SEI layers at the electrode.

show abstract

Prediction of battery storage ageing and solid electrolyte interphase property estimation using an electrochemical model

Cited by 37 publications

References 18 publications

Multi-Level Model Reduction and Data-Driven Identification of the Lithium-Ion Battery

Multi-Level Model Reduction and Data-Driven Identification of the Lithium-Ion Battery

An insight into the errors and uncertainty of the lithium-ion battery characterisation experiments

Role of SEI layer growth in fracture probability in lithium‐ion battery electrodes

Contact Info

Product

Resources

About