Results of Screening over 200 Pristine Lithium-Ion Cells

Barreras, Jorge Varela; Raj, Trishna; Howey, David A.; Schaltz, Erik

doi:10.1109/vppc.2017.8331060

Cited by 4 publications

(5 citation statements)

References 15 publications

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“…Apart from that, different sets of parameters are generated to create different scenarios for cell-to-cell variations. Experimental data from single-cell characterization tests are combined with statistical data from screening tests on more than 200 pristine cells from Barreras et al [28] and data from the literature to parameterize the models.…”

Section: Electro-thermal Battery Modelmentioning

confidence: 99%

“…Moreover, just two of them present experimental results for large-capacity cells, as summarized in Table 1. In this study, statistical data obtained by Barreras et al [28] through screening tests conducted on 208 Kokam SLPB 53 Ah cells tested at Beginning-of-Life (BOL) is used to parametrize the statistical model of cell-to-cell variation at BOL. The cell capacities are derived from the discharge capacities measured during a 1C full discharge at constant current and room temperature.…”

Section: Statistical Model Of Cell-to-cell Variationsmentioning

confidence: 99%

“…The two histograms of the relative frequency of discharge capacity and the DCR resistance at BOL are shown in Figure 10. These histograms are fitted to normal distributions, which are the de facto statistical models of cell-to-cell variations at BOL [28]. On the other hand, in the absence of more statistical data from large format cells at other aging states in the literature, the Middle-of-Life (MOL) and End-of-Life (EOL) parameter distributions are calculated through certain manipulation of the BOL distributions based on insights from the literature, following the same approach of Pinto et al [14].…”

Section: Statistical Model Of Cell-to-cell Variationsmentioning

confidence: 99%

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A Consensus Algorithm for Multi-Objective Battery Balancing

et al. 2021

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Batteries stacks are made of cells in certain series-parallel arrangements. Unfortunately, cell performance degrades over time in terms of capacity, internal resistance, or self-discharge rate. In addition, degradation rates are heterogeneous, leading to cell-to-cell variations. Balancing systems can be used to equalize those differences. Dissipative or non-dissipative systems, so-called passive or active balancing, can be used to equalize either voltage at end-of-charge, or state-of-charge (SOC) at all times. While passive balancing is broadly adopted by industry, active balancing has been mostly studied in academia. Beyond that, an emerging research field is multi-functional balancing, i.e., active balancing systems that pursue additional goals on top of SOC equalization, such as equalization of temperature, power capability, degradation rates, or losses minimization. Regardless of their functionality, balancing circuits are based either on centralized or decentralized control systems. Centralized control entails difficult expandability and single point of failure issues, while decentralized control has severe controllability limitations. As a shift in this paradigm, here we present for the first time a distributed multi-objective control algorithm, based on a multi-agent consensus algorithm. We implement and validate the control in simulations, considering an electro-thermal lithium-ion battery model and an electric vehicle model parameterized with experimental data. Our results show that our novel multi-functional balancing can enhance the performance of batteries with substantial cell-to-cell differences under the most demanding operating conditions, i.e., aggressive driving and DC fast charging (2C). Driving times are extended (>10%), charging times are reduced (>20%), maximum cell temperatures are decreased (>10 °C), temperature differences are lowered (~3 °C rms), and the occurrence of low voltage violations during driving is reduced (>5×), minimizing the need for power derating and enhancing the user experience. The algorithm is effective, scalable, flexible, and requires low implementation and tuning effort, resulting in an ideal candidate for industry adoption.

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Section: Electro-thermal Battery Modelmentioning

confidence: 99%

Section: Statistical Model Of Cell-to-cell Variationsmentioning

confidence: 99%

Section: Statistical Model Of Cell-to-cell Variationsmentioning

confidence: 99%

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A Consensus Algorithm for Multi-Objective Battery Balancing

et al. 2021

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“…Experiments and statistical analysis on CtCV have been widely conducted on cells with different sample sizes, from dozens [9] to hundreds [10] and even thousands [11,12]. Studied sources of CtCV normally include operating temperature, capacity, weight, DC resistance, AC impedances, self-discharge and calendar ageing.…”

Section: Introductionmentioning

confidence: 99%

A Monte Carlo simulator to investigate cell‐to‐cell deviation in a grid‐tied battery pack

Wang

Davidson

et al. 2022

IET Power Electronics

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Battery energy storage systems (BESSs) are commonly used in smart grids. Voltage deviation or imbalance among cells generally exists in multi-cell battery packs. This work presents a study of the voltage deviation-related phenomena observed during the operation of a grid-tied BESS, Willenhall Energy Storage System (WESS), including the voltage deviation changes during full range cycle and the cut-off mechanism activated by it. Electroimpedance spectroscopy measurements and equivalent circuit modelling were conducted on the same type of cell as that used in WESS to obtain cell-equivalent circuit parameter distributions (the standard deviation and mean). Cell voltage deviation in a WESS-sized battery pack (> 21k cells) was studied using Monte Carlo simulation through a proposed cell level battery simulator. Both experiments and simulations reveal that high cell voltage deviation emerges at the low and high state-of-charge zones where the cell internal resistance has a large value and large extent of deviation.

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“…Manufacturing tolerances often result in initial cell-to-cell variations in the capacity and the internal resistance of lithium-ion cells. [1][2][3][4][5][6][7][8][9][10][11][12] Cell-to-cell parameter variations are found both within a production batch and between different production batches over the time span of the production process. 1,7 Despite the strong possibility that battery modules would contain cells from only one batch Dubarry et al 13 point out that every battery module is unique due to the unavoidable cell-to-cell parameter variations previously described.…”

mentioning

confidence: 99%

Analyzing the Aging Behavior of Lithium-Ion Cells Connected in Parallel Considering Varying Charging Profiles and Initial Cell-to-Cell Variations

Schindler

Jocher

Durdel

et al. 2021

J. Electrochem. Soc.

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Aging behavior and long-term cell-to-cell variations have been much more frequently investigated in single-cells than cells connected in parallel. In particular, the literature lacks a study investigating the aging behavior of cells in parallel that is based on defined cell-to-cell variations and on the results of a previous single-cell aging study. Moreover, present studies are unable to exclude the impacts of measurement systems on their final results. To counter this deficiency, a novel 4-wire measurement technique is used which does not influence the current distribution but allows both single and parallel measurements to be recorded without changing the measurement configuration. Cells in parallel generally displayed improved aging behaviors in comparison to those seen in the single-cell aging study and the positive influence of extended CV-charging was evident, as long as the CV-charging phase was limited in length. It was also observed that the exclusion of critical voltage ranges exerted the most significant influence on the aging rate and dominates the influence of initial cell-to-cell variations in the long-term. As a result, it is recommended that module manufacturers reduce the effort spent on initial cell matching strategies for cells in parallel in favor of developing cell-specific charging profiles.

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Results of Screening over 200 Pristine Lithium-Ion Cells

Cited by 4 publications

References 15 publications

A Consensus Algorithm for Multi-Objective Battery Balancing

A Consensus Algorithm for Multi-Objective Battery Balancing

A Monte Carlo simulator to investigate cell‐to‐cell deviation in a grid‐tied battery pack

Analyzing the Aging Behavior of Lithium-Ion Cells Connected in Parallel Considering Varying Charging Profiles and Initial Cell-to-Cell Variations

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