Local degradation and differential voltage analysis of aged lithium-ion pouch cells

Summary To accurately evaluate the state of charge (SOC) and state of health (SOH) of Li‐ion battery, the second‐order RC equivalent‐circuit model is used to characterize the battery performance, a novel dual adaptive Kalman filtering algorithm is presented by adding double cycles and noise adaptive steps to realize the joint estimation of the SOC and internal resistance. The state variables can be corrected with each other as go through the cycle under three operating conditions. The accuracy of the SOC estimation method proposed in this paper is significantly improved compared with the extended Kalman filtering and the unscented Kalman filtering algorithm. Under three operating conditions, the average error and the maximum error decreased obviously. An equation for calculating the SOH in terms of internal resistance increase was built. The estimation result of the SOH effectively simulated the actual situation, compared with the actual result, the maximum error under the three operating conditions are within a lower level than the improved unscented Kalman filtering algorithm. The convergence effect of the algorithm has obvious advantages over that of the algorithm used for comparison, which could effectively track the state change of the battery.

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“…It is generally believed that when SOH drops to 80%, the battery reaches the end of life state, at which point it needs to be scrapped. 44…”

Section: The Soc and Soh Of Li-ion Batteries Studymentioning

confidence: 99%

“…R refers to the internal resistance of the battery at the current time, and R BOL represents the new battery's internal resistance. It is generally believed that when SOH drops to 80%, the battery reaches the end of life state, at which point it needs to be scrapped 44 …”

Section: Mathematical Analysismentioning

confidence: 99%

Fusion estimation strategy based on dual adaptive Kalman filtering algorithm for the state of charge and state of health of hybrid electric vehicle Li‐ion batteries

Ren

Wang

Chen

et al. 2022

Intl J of Energy Research

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“…stemming from SEI growth or lithium plating, shifts the anode's vs. the cathode's potential curve [37,38,40] . Additionally, the algorithm captures inhomogeneous aging by fitting a linear distribution of seven differently aged segments to the overall DVA curve [41,42] . Figure 1 b) shows an exemplary fit of the experimental DVA-curve (green) with seven differently aged DVAcurves (orange).…”

Section: Isolating Active Lithium Lossmentioning

confidence: 99%

“…As first step of our post mortem analysis, we uniformly discharge the cells to 3 V. Then, we open the cells in a glovebox with argon atmosphere and H 2 O and O 2 levels below 0.1 ppm following the procedure described by Sieg et al [42] . To remove residues of conducting salt, we wash anode and cathode layers from the middle of the stack in a bath of dimethyl carbonate (DMC) for at least three minutes.…”

Section: Post Mortem Analysismentioning

confidence: 99%

A four parameter model for the solid-electrolyte interphase to predict battery aging during operation

Kolzenberg¹,

Stadler²,

Fath³

et al. 2021

Preprint

Self Cite

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Accurately predicting aging of lithium-ion batteries would help to prolong their lifespan, but remains a challenge owing to the complexity and interrelation of different aging mechanisms. As a result, aging prediction often relies on empirical or data-driven approaches, which obtain their performance from analyzing large datasets. However, these datasets are expensive to generate and the models are agnostic of the underlying physics and thus difficult to extrapolate to new conditions. In this article, a physical model is used to predict capacity fade caused by solid-electrolyte interphase (SEI) growth in 62 automotive cells, aged with 28 different protocols. Three protocols parametrize the time, current and temperature dependence of the model, the state of charge dependence results from the anode's open circuit voltage curve. The model validation with the remaining 25 protocols shows a high predictivity with a root-mean squared error of 1.28%. A case study with the so-validated model shows that the operating window, i.e. maximum and minimum state of charge, has the largest impact on SEI growth, while the influence of the applied current is almost negligible. Thereby the presented model is a promising approach to better understand, quantify and predict aging of lithium-ion batteries.

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“…25 Whole cell parameter estimation using differential voltage and differential capacity data from aging cells helps understand inhomogeneous material degradation and slippage. [26][27][28] Open software used for the analysis of engineered whole-cells is provided as a Jupyter notebook.…”

Section: Introductionmentioning

confidence: 99%

Low-Error Estimation of Half-Cell Thermodynamic Parameters from Whole-Cell Li-Ion Battery Experiments: Physics-Based Model Formulation, Experimental Demonstration, and an Open Software Tool

Schwartz

2022

Meet. Abstr.

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Low C-rate charge and discharge experiments, plus complementary differential voltage or differential capacity analysis, are among the most common battery characterization methods. Here, we adapt the multi-species, multi-reaction (MSMR) half-cell thermodynamic model to low C-rate cycling of whole-cell Li-ion batteries. MSMR models for the anode and cathode are coupled through whole-cell charge balances and cell-cycling voltage constraint equations, forming the basis for model-based estimation of MSMR half-cell parameters from whole-cell experimental data. Emergent properties of the whole-cell, like slippage of the anode and cathode lithiation windows, are also computed as cells cycle and degrade. A sequential least-square optimization scheme is used for parameter estimation from low-C cycling data of Samsung 18650 NMC|C cells. Low-error fits of the open-circuit cell voltage (e.g., under 5 mV mean absolute error for charge or discharge curves) and differential voltage curves for fresh and aging cells are achieved. We explore the features (and limitations) of using literature reference values for the MSMR half-cell thermodynamic parameters (reducing our whole-cell formulation to a 1-degree-of-freedom fit), and demonstrate the benefits of opening up the degrees of freedom by letting the MSMR parameters be tailored to the cell under test, within constrained bounds near the half-cell reference values. Bootstrap analysis is performed on each dataset to show the robustness of our fitting to experimental noise and data sampling over the course of 600 cell cycles. The results show which specific MSMR insertion reactions are most responsible for capacity loss in each half-cell and the collective interactions that lead to whole-cell slippage and changes in useable capacity. Open-source software is made available to easily extend this model-based analysis to other labs and battery chemistries.

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Local degradation and differential voltage analysis of aged lithium-ion pouch cells

Cited by 56 publications

References 73 publications

Fusion estimation strategy based on dual adaptive Kalman filtering algorithm for the state of charge and state of health of hybrid electric vehicle Li‐ion batteries

Fusion estimation strategy based on dual adaptive Kalman filtering algorithm for the state of charge and state of health of hybrid electric vehicle Li‐ion batteries

A four parameter model for the solid-electrolyte interphase to predict battery aging during operation

Low-Error Estimation of Half-Cell Thermodynamic Parameters from Whole-Cell Li-Ion Battery Experiments: Physics-Based Model Formulation, Experimental Demonstration, and an Open Software Tool

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