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Abstract
Traditional lithium-ion battery (LiB) modeling does not provide sufficient information to accurately verify battery performance under real-time dynamic operating conditions, particularly when considering various aging modes and mechanisms. This paper proposes a lithium-ion battery digital twin that can capture real-time data and integrate the strong coupling between SEI layer growth, anode crack propagation, drying up of Electrolytes, and lithium plating. It can be utilized to predict the degradation behavior, voltage-current profiles in dynamic aging conditions, and analyze the lithium inventory loss (LLI) of Nickel-Manganese-Cobalt-Oxide (NMC) lithium-ion batteries.
Commercially available 18650 cylindrical NMC Samsung battery cells were investigated. The cathode and anode materials are LiNi0.5Co0.2Mn0.3O2 and synthetic graphite, respectively. Four dynamic aging test cases using 1C, 1.3C, MC (multistep charging), and 2C, were designed and performed at 25 °C. The reference performance test (RPT) was repeated every 100 full equivalent cycles (FECs) until the cells reached 20 % capacity fade. Figure 1 shows the real-time voltage and current profile captured and predicted by the digital twin.
The model takes into account LLI induced by SEI growth and lithium plating, aligning with the LLI calibrated from the differential voltage (DV) endpoint shift (Figure 1h). Based on the model results, SEI growth is found to be the primary contributor to capacity loss before 500 FECs. SEI-induced degradation is minimal under the MC protocol, with the capacity loss varying by less than 5.22% across four test cases (Figure 1e). After 500 FECs, anode cracking becomes a non-linear accelerated aging factor. The crack propagation stabilizes in the initial 200 FECs, followed by unstable accelerated growth leading to capacity loss. Subsequently, a rapid acceleration in capacity loss occurs after 700 FECs (Figure 1f). The growth of anode cracks under the MC aging protocol has a much slower effect on capacity loss compared to the 2C and 1.3C before 500 FECs, as strongly confirmed by the post-mortem analysis. In addition, the lithium plating phenomenon triggers two orders of magnitude less capacity loss than SEI growth and cracking at 25 °C. The MC protocol demonstrates an effective reduction in lithium plating capacity loss compared to the other three traditional charging protocols (Figure 1g). This digital twin can represent a firm physical foundation for future physics-informed machine learning development to predict LiBs degradation behavior.
Commercially available 18650 cylindrical NMC Samsung battery cells were investigated. The cathode and anode materials are LiNi0.5Co0.2Mn0.3O2 and synthetic graphite, respectively. Four dynamic aging test cases using 1C, 1.3C, MC (multistep charging), and 2C, were designed and performed at 25 °C. The reference performance test (RPT) was repeated every 100 full equivalent cycles (FECs) until the cells reached 20 % capacity fade. Figure 1 shows the real-time voltage and current profile captured and predicted by the digital twin.
The model takes into account LLI induced by SEI growth and lithium plating, aligning with the LLI calibrated from the differential voltage (DV) endpoint shift (Figure 1h). Based on the model results, SEI growth is found to be the primary contributor to capacity loss before 500 FECs. SEI-induced degradation is minimal under the MC protocol, with the capacity loss varying by less than 5.22% across four test cases (Figure 1e). After 500 FECs, anode cracking becomes a non-linear accelerated aging factor. The crack propagation stabilizes in the initial 200 FECs, followed by unstable accelerated growth leading to capacity loss. Subsequently, a rapid acceleration in capacity loss occurs after 700 FECs (Figure 1f). The growth of anode cracks under the MC aging protocol has a much slower effect on capacity loss compared to the 2C and 1.3C before 500 FECs, as strongly confirmed by the post-mortem analysis. In addition, the lithium plating phenomenon triggers two orders of magnitude less capacity loss than SEI growth and cracking at 25 °C. The MC protocol demonstrates an effective reduction in lithium plating capacity loss compared to the other three traditional charging protocols (Figure 1g). This digital twin can represent a firm physical foundation for future physics-informed machine learning development to predict LiBs degradation behavior.
Originalsprog | Engelsk |
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Publikationsdato | 2024 |
DOI | |
Status | Udgivet - 2024 |
Begivenhed | 245th ECS Meeting - San Francisco, USA Varighed: 26 maj 2024 → 30 maj 2024 https://www.electrochem.org/245 |
Konference
Konference | 245th ECS Meeting |
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Land/Område | USA |
By | San Francisco |
Periode | 26/05/2024 → 30/05/2024 |
Internetadresse |
Fingeraftryk
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Physics informed machine learning for LiBs' aging behavior estimation and prediction
Guo, W. (PI (principal investigator)), Stroe, D.-I. (Supervisor) & Vilsen, S. B. (Supervisor)
01/12/2021 → 30/11/2024
Projekter: Projekt › Ph.d.-projekt