Incremental Capacity Analysis of a Lithium-Ion Battery Pack for Different Charging Rates

Kalogiannis, Theodoros; Stroe, Daniel‐Ioan; Nyborg, Jonas; Nørregaard, Kjeld; Christensen, Andreas Elkjær; Schaltz, Erik

doi:10.1149/07711.0403ecst

Cited by 27 publications

(6 citation statements)

References 20 publications

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“…Furthermore, it is not always possible to access this data, e.g., in third-party second life modules. Kalogiannis et al [22] reported on a battery with cells connected in series. They observed that the scaled incremental capacity of a module consisting of series-connected cells with identical capacity corresponded to the average of the IC of the individual cells.…”

Section: Introductionmentioning

confidence: 99%

Incremental Capacity Analysis as a State of Health Estimation Method for Lithium-Ion Battery Modules with Series-Connected Cells

Krupp¹,

Ferg

Schuldt³

et al. 2020

Batteries

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Incremental capacity analysis (ICA) has proven to be an effective tool for determining the state of health (SOH) of Li-ion cells under laboratory conditions. This paper deals with an outstanding challenge of applying ICA in practice: the evaluation of battery series connections. The study uses experimental aging and characterization data of lithium iron phosphate (LFP) cells down to 53% SOH. The evaluability of battery series connections using ICA is confirmed by analytical and experimental considerations for cells of the same SOH. For cells of different SOH, a method for identifying non-uniform aging states on the modules’ IC curve is presented. The findings enable the classification of battery modules with series and parallel connections based on partial terminal data.

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Section: Introductionmentioning

confidence: 99%

Incremental Capacity Analysis as a State of Health Estimation Method for Lithium-Ion Battery Modules with Series-Connected Cells

Krupp¹,

Ferg

Schuldt³

et al. 2020

Batteries

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show abstract

“…Turning to the prospects for long-term tracking, the evolution of quantified dQ/dV peak characteristics such as intensity and position has been widely employed to monitor cell status during cycle aging. 53 In electrochemical terms, the intensity of the dQ/dV peaks is directly proportional to the contribution of the respective reactions to the cell's overall capacity, but this, of course, depends on contributions from the anode and cathode half-cell reactions. An unbalanced loss of active material (anode or cathode) will lead to changes in the peak positions and intensities, commonly referred to as slippage.…”

Section: Sohmentioning

confidence: 99%

Towards Long-Term Monitoring of Commercial Lithium-Ion Batteries Enabled by Externally Affixed Fiber Sensors and Strain-Based Prognostic Strategies

Ghashghaie,

Bonefacino,

Cheung

et al. 2024

J. Electrochem. Soc.

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Real-time monitoring of both continuous and spontaneous degradation in lithium-ion batteries is challenging due to the limited number of quantitative metrics available during cycling. In this regard, improved sensing approaches enabled by sensors of high accuracy, precision, and durability are key to achieving comprehensive state estimation and meeting rigorous safety standards. In this work, external temperature and strain monitoring in commercial Li-ion button cells was carried out using tandem pairs of polymer-based and silica-based optical fiber Bragg grating sensors. The decoupled data revealed that the sensors can reliably track strain and temperature evolution for over 500 cycles, as evidenced by periodic patterns with no sign of sensor degradation or loss of fidelity. Moreover, monitoring the strain signal enabled early detection of an anomalous cell over ~60 cycles ahead of an electrochemical signature and abrupt drop in capacity, suggesting that mechanical sensing data may offer unique benefits in some cases. Detailed mechanical monitoring via incremental strain analysis suggests a parallel path toward understanding cell degradation mechanisms, regardless of whether they are continuous or discrete in nature. The accuracy and durability of such a package-level optical fiber sensing platform offer a promising pathway for developing robust real-time battery health monitoring techniques and prognostic strategies

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“…Through this, the change in charge is observed. The state and progress of internal chemical reactions can be estimated [32]. Through this, the concept of capacity loss in Li-ion batteries can be analyzed by dividing it into the loss of active material (LAM) and the loss of lithium inventory (LLI) [33].…”

Section: Incremental Capacity Analysismentioning

confidence: 99%

Rapid Estimation of Static Capacity Based on Machine Learning: A Time-Efficient Approach

Son,

Choi

2024

Batteries

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With the global surge in electric vehicle (EV) deployment, driven by enhanced environmental regulations and efforts to reduce transportation-related greenhouse gas emissions, managing the life cycle of Li-ion batteries becomes more critical than ever. A crucial step for battery reuse or recycling is the precise estimation of static capacity at retirement. Traditional methods are time-consuming, often taking several hours. To address this issue, a machine learning-based approach is introduced to estimate the static capacity of retired batteries rapidly and accurately. Partial discharge data at a 1C rate over durations of 6, 3, and 1 min were analyzed using a machine learning algorithm that effectively handles temporally evolving data. The estimation performance of the methodology was evaluated using the mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE). The results showed reliable and fairly accurate estimation performance, even with data from shorter partial discharge durations. For the one-minute discharge data, the maximum RMSE was 2.525%, the minimum was 1.239%, and the average error was 1.661%. These findings indicate the successful implementation of rapidly assessing the static capacity of EV batteries with minimal error, potentially revitalizing the retired battery recycling industry.

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Incremental Capacity Analysis of a Lithium-Ion Battery Pack for Different Charging Rates

Cited by 27 publications

References 20 publications

Incremental Capacity Analysis as a State of Health Estimation Method for Lithium-Ion Battery Modules with Series-Connected Cells

Incremental Capacity Analysis as a State of Health Estimation Method for Lithium-Ion Battery Modules with Series-Connected Cells

Towards Long-Term Monitoring of Commercial Lithium-Ion Batteries Enabled by Externally Affixed Fiber Sensors and Strain-Based Prognostic Strategies

Rapid Estimation of Static Capacity Based on Machine Learning: A Time-Efficient Approach

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