Electrochemical Impedance Spectroscopy Based Power-Mix Control Strategy for Improved Lifetime Performance in Second-Life Battery Systems

Lamoureux, Carl; Gong, Zhe; Nasr, Miad; Assadi, Seyed Amir; Gupta, Kshitij; Galatro, Daniela; Tayyara, Omri; Silva, C. da; Amón, Cristina H.; Trescases, Olivier

doi:10.1109/apec39645.2020.9124230

Cited by 6 publications

(3 citation statements)

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“…This section builds on the work of Lamoureux et al, [ 23 ] who developed a power‐mix control strategy to increase the added value of second‐life LIBs used in off‐grid stationary storage applications. For this, the battery modelling was carried out using electrochemical impedance spestroscopy (EIS) data, in combination with a strategy to control the power flow between two 3.85 kWh battery modules at different SOHs in an off‐grid application, aiming to reduce the total battery degradation rate.…”

Section: Methodsmentioning

confidence: 99%

“…For this, the battery modelling was carried out using electrochemical impedance spestroscopy (EIS) data, in combination with a strategy to control the power flow between two 3.85 kWh battery modules at different SOHs in an off‐grid application, aiming to reduce the total battery degradation rate. The battery aging model presented by Lamoureux et al [ 23 ] is based on measured degradation data for NMC cells. The capacity loss is an Arrhenius‐based equation with adjusted acceleration factors.…”

Section: Methodsmentioning

confidence: 99%

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Impact of cell spreading on second‐life of lithium‐ion batteries

et al. 2022

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The evolution of the degradation paths of the cells in battery packs is shaped by both intrinsic cell‐to‐cell variations, also known as cell spreading, and spatial–temporal cell‐to‐cell differences in temperature and other stress factors. To account for these variations and differences in degradation, we propose a statistical approach for modelling the degradation of lithium‐ion batteries (LIBs) that utilizes a three‐parameter non‐homogeneous gamma process, allowing for the prediction of the capacity fade or time‐to‐failure for any LIB architecture. This degradation modelling approach has been integrated into a cost model to investigate the sensitivity of the battery lifetime's economic outcome, aiming to maximize the added value of stationary battery storage composed of degraded electric vehicle batteries. Thus, the impact of cell spreading was quantified for three simulation scenarios on second‐life applications: (i) performing a simplified cost analysis to evaluate the business case for second‐life, (ii) performing an economic analysis to visualize the impact of spreading, and (iii) evaluating an existing power flow control strategy between LIB modules. The information derived from the spreading‐cost integration approach is valuable to support the technical and economic analyses in the decision‐making process of designing, installing, and running efficiently second‐life applications.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Impact of cell spreading on second‐life of lithium‐ion batteries

et al. 2022

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“…Cell‐to‐cell variation, although statistically challenging to account for, should be considered by including more cells in those tests whose conditions promote dominant degradation mechanisms, such as tests at 45°C and −15°C. When running tests, we also recommend periodic cell characterizations every 100 days, including electrochemical impedance spectroscopy (EIS), which is proven as an efficient aging diagnostic tool that could help to identify degradation modes . Finally, tests at sub‐zero temperatures become optional in those geographies where these ambient temperatures are not expected.…”

Section: Guidelines For Dbm‐ats In Libsmentioning

confidence: 99%

Challenges in data‐based degradation models for lithium‐ion batteries

et al. 2020

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Summary This work summarizes the findings resulting from applying an aging modeling approach to four different capacity loss experimental datasets of lithium‐ion batteries (LIBs). This approach assumes that the degradation trajectory of the capacity is a function of three variables: time, kinetic constant, and time‐dependent factor. The analysis shows that the time‐dependent factor α is cell‐chemistry dependent and cannot be averaged for calendar and cycling modes and combined modes. This factor was also found to be a function of the stress factors. A quadratic model was used to obtain the kinetic constants per test, and statistical metrics were provided to evaluate the quality of the fitting, which was significantly affected when using averaged values of α and refitted kinetic constants. A set of test matrices is proposed for calendar, cycling, and mixed aging modes to overcome the challenges of data‐based models developed from accelerated test approaches for modeling aging in LIBs. This work also proposes a methodology to develop these data‐based aging models.

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