Capacity and Internal Resistance of lithium-ion batteries: Full degradation curve prediction from Voltage response at constant Current at discharge

Ibraheem, Rasheed; Strange, Calum; Reis, Gonçalo dos

doi:10.1016/j.jpowsour.2022.232477

Cited by 29 publications

(12 citation statements)

References 29 publications

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“…Overall, the model uses the voltage and current curves from a single cycle (any cycle) as input to predict full capacity/IR trajectory (including RUL) via certain cogent points on the capacity/IR curve. Concretely, this model was trained to predict the time to knee-onset (ttk-o), time to knee-point (ttk-p), remaining useful life (RUL), capacity at knee-onset (Q@k-o), and capacity at knee-point (Q@k-p)-the methodology is described in [20,22]. In addition, for the Baumhöfer dataset, we predict the time to elbow-onset (tte-o), time to elbow-point (tte-p), and their corresponding IR values (IR@e-o and IR@e-p); see Table 1 for a summary.…”

Section: Model From In-cycle Variabilitymentioning

confidence: 99%

“…For the Baumhöfer dataset model training, the model is directly redeployed from [20], and the parameters (number of filters, training epochs, dropout rate) were increased in an unoptimised way-we recall that the goal of this work not an in-depth exploration of the one-cycle model from machine learning parameter optimality. [20] (also [22]).…”

Section: Model From In-cycle Variabilitymentioning

confidence: 99%

“…As for full trajectory prediction, from early life to end of life, for capacity degradation was considered in [17][18][19] where neural networks were used as the main building block. In addition, both capacity fade and IR rise curves have been jointly predicted using convolutional neural network (CNN) [20], deep recurrent neural network architecture [21], and extreme gradient boosting [22]. For instance, using CNNs for cycle-to-cycle prediction and requiring at least 100 cycles of data [17] builds a model able to predict the entire battery capacity fade curve, including the per-cycle fade rate and rollover cycle (knee point) of the capacity fade curve.…”

Section: Introductionmentioning

confidence: 99%

“…On longitudinal data, various machine learning algorithms taking the first 100 cycles of certain measured quantities (such as temperature, capacity, current, and voltage) have been developed to predict the end of life (EOL) [14,15,[30][31][32] and full capacity curve [30,33]. Then in [10,22], only the first 50 cycles were used for the prediction of various RUL and capacity/IR predictions, with [22] using only discharge voltage under constant-current condition (a reduced cross-sectional data). This reduction is pushed further in [14,20] to train a class of models referred to as one-cycle models (sometimes also called 'historyless').…”

Section: Introductionmentioning

confidence: 99%

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Online Lifetime Prediction for Lithium-Ion Batteries with Cycle-by-Cycle Updates, Variance Reduction, and Model Ensembling

2023

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Lithium-ion batteries have found applications in many parts of our daily lives. Predicting their remaining useful life (RUL) is thus essential for management and prognostics. Most approaches look at early life prediction of RUL in the context of designing charging profiles or optimising cell design. While critical, said approaches are not directly applicable to the regular testing of cells used in applications. This article focuses on a class of models called ‘one-cycle’ models which are suitable for this task and characterized by versatility (in terms of online prediction frameworks and model combinations), prediction from limited input, and cells’ history independence. Our contribution is fourfold. First, we show the wider deployability of the so-called one-cycle model for a different type of battery data, thus confirming its wider scope of use. Second, reflecting on how prediction models can be leveraged within battery management cloud solutions, we propose a universal Exponential-smoothing (e-forgetting) mechanism that leverages cycle-to-cycle prediction updates to reduce prediction variance. Third, we use this new model as a second-life assessment tool by proposing a knee region classifier. Last, using model ensembling, we build a “model of models”. We show that it outperforms each underpinning model (from in-cycle variability, cycle-to-cycle variability, and empirical models). This ‘ensembling’ strategy allows coupling explainable and black-box methods, thus giving the user extra control over the final model.

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Section: Model From In-cycle Variabilitymentioning

confidence: 99%

Section: Model From In-cycle Variabilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Online Lifetime Prediction for Lithium-Ion Batteries with Cycle-by-Cycle Updates, Variance Reduction, and Model Ensembling

2023

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“…The internal resistance of the battery is a parameter to measure the difficulty of carrier movement in the electrode. Numerous experiments show that the internal resistance is closely related to the battery residual capacity [8][9][10] . Generally, the discharge capacity of a battery with a small internal resistance is higher, while the discharge capacity of a battery with a large internal resistance is lower.…”

Section: Introductionmentioning

confidence: 99%

Estimation of the residue capacity of lithium iron phosphate battery based on the internal resistance obtained from charging voltage drop

Yin,

Yu,

Zhang

et al. 2024

J. Phys.: Conf. Ser.

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This study takes the 80 Ah lithium iron phosphate (LFP) prismatic battery that is from the vehicle and is in the middle or end of life as the research target, and the voltage-drop resistance (VDR), which is calculated through the voltage drop at the charging end, is used to for residue capacity estimation. The relationship between the VDR and the residue capacity of the battery is systematically studied, including factors such as charging cut-off voltage, charging current, charging end time, and charging current change mechanism. The results show that the residue capacity is a quadratic function relationship with the charging cut-off voltage and VDR. The VDR is affected by the charging cut-off voltage, charging cut-off current, and charging end time. There is no significant difference in the influence of VDR obtained by the charging modes of step charging and constant current charging. By testing the VDR under different charging conditions and different resting times, and standardizing the VDR, the residue capacity estimation error of 97% of the vehicle’s batteries is within ±5%, which meets the application requirements for residue capacity estimation.

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Advancements in Graphite Anodes for Lithium‐Ion and Sodium‐Ion Batteries: A Review

Xiong,

Qi,

Zhang

2024

Electroanalysis

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Amidst the escalating global energy demand and the rapid advancement of renewable energy technologies, battery technology plays an indispensable role in energy storage. As a crucial anode material, Graphite enhances performance with significant economic and environmental benefits. This review provides an overview of recent advancements in the modification techniques for graphite materials utilized in lithium‐ion and sodium‐ion batteries. This review initially presents various modification approaches for graphite materials in lithium‐ion batteries, such as electrolyte modification, interfacial engineering, purification and morphological modification, composite modification, surface modification, and structural modification, while also addressing the applications and challenges of graphite anode materials

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Capacity and Internal Resistance of lithium-ion batteries: Full degradation curve prediction from Voltage response at constant Current at discharge

Cited by 29 publications

References 29 publications

Online Lifetime Prediction for Lithium-Ion Batteries with Cycle-by-Cycle Updates, Variance Reduction, and Model Ensembling

Online Lifetime Prediction for Lithium-Ion Batteries with Cycle-by-Cycle Updates, Variance Reduction, and Model Ensembling

Estimation of the residue capacity of lithium iron phosphate battery based on the internal resistance obtained from charging voltage drop

Advancements in Graphite Anodes for Lithium‐Ion and Sodium‐Ion Batteries: A Review

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