battery cell
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10.1142/12511 ◽  
2022 ◽  
Author(s):  
Kai Peter Birke ◽  
Max Weeber ◽  
Michael Oberle

2022 ◽  
Vol 12 (2) ◽  
pp. 885
Author(s):  
Ahmed Yahia Kallel ◽  
Viktor Petrychenko ◽  
Olfa Kanoun

Several studies show that impedance spectroscopy is a suitable method for online battery diagnosis and State-of-Health (SoH) estimation. However, the most common method is to model the acquired impedance spectrum with equivalent circuits and focus on the most sensitive parameters, namely the charge-transfer resistance. This paper introduces first a detailed model of a battery cell, which is then simplified and adapted to the observable spectrum behavior. Based on the physical meaning of the model parameters, we propose a novel approach for SoH assessment combining parameters of the impedance spectrum by building the ratio of the solid electrolyte interphase (SEI) resistance to the total resistance of SEI and the charge transfer. This ratio characterizes the charge-transfer efficiency at the electrodes’ surfaces and should decrease systematically with SoH. Four different cells of the same type were cycled 400 times for the method validation, and impedance spectroscopy was performed at every 50th cycle. The results show a systematic correlation between the proposed ratio and the number of cycles on individual cell parameters, which build the basis of a novel online method of SoH assessment.


2022 ◽  
Vol 35 (1) ◽  
Author(s):  
Yunhong Che ◽  
Zhongwei Deng ◽  
Xiaolin Tang ◽  
Xianke Lin ◽  
Xianghong Nie ◽  
...  

AbstractAging diagnosis of batteries is essential to ensure that the energy storage systems operate within a safe region. This paper proposes a novel cell to pack health and lifetime prognostics method based on the combination of transferred deep learning and Gaussian process regression. General health indicators are extracted from the partial discharge process. The sequential degradation model of the health indicator is developed based on a deep learning framework and is migrated for the battery pack degradation prediction. The future degraded capacities of both battery pack and each battery cell are probabilistically predicted to provide a comprehensive lifetime prognostic. Besides, only a few separate battery cells in the source domain and early data of battery packs in the target domain are needed for model construction. Experimental results show that the lifetime prediction errors are less than 25 cycles for the battery pack, even with only 50 cycles for model fine-tuning, which can save about 90% time for the aging experiment. Thus, it largely reduces the time and labor for battery pack investigation. The predicted capacity trends of the battery cells connected in the battery pack accurately reflect the actual degradation of each battery cell, which can reveal the weakest cell for maintenance in advance.


Batteries ◽  
2021 ◽  
Vol 7 (4) ◽  
pp. 88
Author(s):  
Natascia Andrenacci ◽  
Francesco Vellucci ◽  
Vincenzo Sglavo

The prediction of capacity degradation, and more generally of the behaviors related to battery aging, is useful in the design and use phases of a battery to help improve the efficiency and reliability of energy systems. In this paper, a stochastic model for the prediction of battery cell degradation is presented. The proposed model takes its cue from an approach based on Markov chains, although it is not comparable to a Markov process, as the transition probabilities vary with the number of cycles that the cell has performed. The proposed model can reproduce the abrupt decrease in the capacity that occurs near the end of life condition (80% of the nominal value of the capacity) for the cells analyzed. Furthermore, we illustrate the ability of this model to predict the capacity trend for a lithium-ion cell with nickel manganese cobalt (NMC) at the cathode and graphite at the anode, subjected to a life cycle in which there are different aging factors, using the results obtained for cells subjected to single aging factors.


Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8492
Author(s):  
Chao Li ◽  
Assimina A. Pelegri

Models that can predict battery cells’ thermal and electrical behaviors are necessary for real-time battery management systems to regulate the imbalance within battery cells. This work introduces a Gaussian Process Regression (GPR)-based data-driven framework that succeeds the Multi-Scale Multi-Dimensional (MSMD) modeling structure. The framework can make highly accurate predictions at the same level as full-order full-distribution simulations based on MSMD. A pseudo-2D model is used to generate training data and is combined with a process that shifts computation burdens from real-time battery management systems to lab data preparation. The testing results highlight the reliability of the GPR-based data-driven framework in terms of accuracy and stability under various operational conditions.


Batteries ◽  
2021 ◽  
Vol 7 (4) ◽  
pp. 81
Author(s):  
Yiqun Liu ◽  
Yitian Li ◽  
Y. Gene Liao ◽  
Ming-Chia Lai

The nail penetration test has been widely adopted as a battery safety test for reproducing internal short-circuits. In this paper, the effects of cell initial State-of-Charge (SOC) and penetration location on variations in cell temperature and terminal voltage during penetration tests are investigated. Three different initial SOCs (10%, 50%, and 90%) and three different penetration locations (one is at the center of the cell, the other two are close to the edge of the cell) are used in the tests. Once the steel cone starts to penetrate the cell, the cell terminal voltage starts to drop due to the internal short-circuit. The penetration tests with higher initial cell SOCs have larger cell surface temperature increases during the tests. Also, the penetration location always has the highest temperature increment during all penetration tests, which means the heat source is always at the penetration location. The absolute temperature increment at the penetration location is always higher when the penetration is close to the edge of the cell, compared to when the penetration is at the center of the cell. The heat generated at the edges of the cell is more difficult to dissipate. Additionally, a battery cell internal short-circuit model with different penetration locations is built in ANSYS Fluent, based on the specifications and experimental data of the tested battery cells. The model is validated with an acceptable discrepancy range by using the experimental data. Simulated data shows that the temperature gradually reduces from penetration locations to their surroundings. The gradients of the temperature distributions are much larger closer to the penetration locations. Overall, this paper provides detailed information on the temperature and terminal voltage variations of a lithium-ion polymer battery cell with large capacity and high power under penetration tests. The presented information can be used for assessing the safety of the onboard battery pack of electric vehicles.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Minsung Baek ◽  
Jinyoung Kim ◽  
Jaegyu Jin ◽  
Jang Wook Choi

AbstractExtremely fast charging (i.e. 80% of storage capacity within 15 min) is a pressing requirement for current lithium-ion battery technology and also affects the planning of charging infrastructure. Accelerating lithium ion transport through the solid-electrolyte interphase (SEI) is a major obstacle in boosting charging rate; in turn, limited kinetics at the SEI layer negatively affect the cycle life and battery safety as a result of lithium metal plating on the electrode surface. Here, we report a γ-ray-driven SEI layer that allows a battery cell to be charged to 80% capacity in 10.8 min as determined for a graphite full-cell with a capacity of 2.6 mAh cm−2. This exceptional charging performance is attributed to the lithium fluoride-rich SEI induced by salt-dominant decomposition via γ-ray irradiation. This study highlights the potential of non-electrochemical approaches to adjust the SEI composition toward fast charging and long-term stability, two parameters that are difficult to improve simultaneously in typical electrochemical processes owing to the trade-off relation.


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