Lithium-ion cell equivalent circuit model identification by galvanostatic intermittent titration technique

LeBel, Felix-A.; Messier, Pascal; Sarı, Ali; Trovão, João P.

doi:10.1016/j.est.2022.105303

Cited by 17 publications

(5 citation statements)

References 67 publications

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“…Through Ohm’s law, the real-time cell resistance can be extracted directly ( Kim et al, 2022 ). Galvanostatic intermittent titration techniques provide good insight of the total battery resistance, as well as measuring the complex impedance, if used alongside model-based methods where the voltage response is fitted to equivalent circuit models (ECM) ( LeBel et al, 2022 ). However, galvanostatic intermittent titration cannot isolate the constituent components of resistance, and therefore cannot relate power fade back to degradation mechanisms.…”

Section: Second Life: Opportunities and Pathwaysmentioning

confidence: 99%

Lithium-ion battery second life: pathways, challenges and outlook

Patel,

Lander,

Ahuja

et al. 2024

Front. Chem.

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Net zero targets have resulted in a drive to decarbonise the transport sector worldwide through electrification. This has, in turn, led to an exponentially growing battery market and, conversely, increasing attention on how we can reduce the environmental impact of batteries and promote a more efficient circular economy to achieve real net zero. As these batteries reach the end of their first life, challenges arise as to how to collect and process them, in order to maximise their economical use before finally being recycled. Despite the growing body of work around this topic, the decision-making process on which pathways batteries could take is not yet well understood, and clear policies and standards to support implementation of processes and infrastructure are still lacking. Requirements and challenges behind recycling and second life applications are complex and continue being defined in industry and academia. Both pathways rely on cell collection, selection and processing, and are confronted with the complexities of pack disassembly, as well as a diversity of cell chemistries, state-of-health, size, and form factor. There are several opportunities to address these barriers, such as standardisation of battery design and reviewing the criteria for a battery’s end-of-life. These revisions could potentially improve the overall sustainability of batteries, but may require policies to drive such transformation across the industry. The influence of policies in triggering a pattern of behaviour that favours one pathway over another are examined and suggestions are made for policy amendments that could support a second life pipeline, while encouraging the development of an efficient recycling industry. This review explains the different pathways that end-of-life EV batteries could follow, either immediate recycling or service in one of a variety of second life applications, before eventual recycling. The challenges and barriers to each pathway are discussed, taking into account their relative environmental and economic feasibility and competing advantages and disadvantages of each. The review identifies key areas where processes need to be simplified and decision criteria clearly defined, so that optimal pathways can be rapidly determined for each end-of-life battery.

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Section: Second Life: Opportunities and Pathwaysmentioning

confidence: 99%

Lithium-ion battery second life: pathways, challenges and outlook

Patel,

Lander,

Ahuja

et al. 2024

Front. Chem.

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show abstract

“…All experiments were conducted on 5 Ah LG Chem M50LT 21700 cylindrical lithium-ion cells, which comprise an NMC cathode and graphite-silicon anode [20,21] and have previously been used in relevant publications related to EV BMSs [22,23]. Three cells were used for the tests, with each cell subjected to all procedures, The stabilisation rig maintained uniform and repeatable pressure applied from the electrical connections onto the cell, ensuring traceability across each test setup and each cell.…”

Section: Methodsmentioning

confidence: 99%

“…For given RC time-constants, parameters R 0 , R 1 , R 2 , C 1 and C 2 of the standard Thévenin model (without hysteresis) may be identified from GITT data with ordinary least-squares, using established methodologies from the literature [22,29,30,31]. Hysteresis parameters cannot be identified in the same way, and must instead be determined through nonlinear optimisation.…”

Section: Parameter Identificationmentioning

confidence: 99%

Advancing state estimation for lithium-ion batteries with hysteresis: systematic extended Kalman filter tuning

Knox,

Blyth,

Hales

2024

Preprint

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Knowledge of remaining battery charge is fundamental to electric vehicle deployment. Accurate measurements of state-of-charge (SOC) cannot be directly obtained, and estimation methods must be used instead. This requires both a good model of a battery and a well-designed state estimator. Here, hysteretic reduced-order battery models and adaptive extended Kalman filter estimators are shown to be highly effective, accurate SOC estimators. A battery model parameterisation framework is proposed, which enhances standardised methods to capture hysteresis effects. The hysteretic model is parameterised for three independent NMC811 lithium-ion cells and is shown to reduce voltage RMS error by 50% across 18-hour automotive drive-cycles. Parameterised models are used alongside an extended Kalman filter, which demonstrates the value of adaptive filter parameterisation schemes. When used alongside an extended Kalman filter, adaptive covariance matrices yield highly accurate SOC estimates, reducing SOC estimation error by 85%, compared to the industry standard battery model.

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“…All experiments were conducted on 5 Ah LG Chem M50LT 21700 cylindrical lithium-ion cells, which comprise an NMC cathode and graphite-silicon anode 28,29 and have previously been used in relevant publications related to EV BMSs 30,31 . Three cells were used for the tests, with each cell subjected to all procedures, resulting in three complete datasets.…”

Section: Methodsmentioning

confidence: 99%

“…For given RC time-constants, parameters R 0 , R 1 , R 2 , C 1 and C 2 of the standard Thévenin model (without hysteresis) may be identified from GITT data with ordinary least-squares, using established methodologies from the literature 30,[37][38][39] . Hysteresis parameters cannot be identified in the same way, and must instead be determined through nonlinear optimisation.…”

Section: Parameter Identificationmentioning

confidence: 99%

Advancing state estimation for lithium-ion batteries with hysteresis through systematic extended Kalman filter tuning

Knox,

Blyth,

Hales

2024

Sci Rep

View full text Add to dashboard Cite

Knowledge of remaining battery charge is fundamental to electric vehicle deployment. Accurate measurements of state-of-charge (SOC) cannot be obtained directly and estimation methods must be used instead. This requires both a good model of a battery and a well-designed state estimator. Here, hysteretic reduced-order battery models and adaptive extended Kalman filter estimators are shown to be highly effective, accurate predictors of SOC. A battery model parameterisation framework is proposed, which enhances standardised methods to capture hysteresis effects. The hysteretic model is parameterised for three independent NMC811 lithium-ion cells and is shown to reduce voltage RMS error by 50% across 18 h automotive drive-cycles. Parameterised models are used alongside an extended Kalman filter, which demonstrates the value of adaptive filter parameterisation schemes. When used alongside an extended Kalman filter, adaptive covariance matrices yield highly accurate SOC estimates, reducing SOC estimation error by 85%, compared to the industry standard battery model.

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Lithium-ion cell equivalent circuit model identification by galvanostatic intermittent titration technique

Cited by 17 publications

References 67 publications

Lithium-ion battery second life: pathways, challenges and outlook

Lithium-ion battery second life: pathways, challenges and outlook

Advancing state estimation for lithium-ion batteries with hysteresis: systematic extended Kalman filter tuning

Advancing state estimation for lithium-ion batteries with hysteresis through systematic extended Kalman filter tuning

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