Lithium-ion battery aging mechanisms and life model under different charging stresses

Gao, Yang; Jiang, Jiuchun; Zhang, Caiping; Zhang, Weige; Ma, Zeyu; Jiang, Yan

doi:10.1016/j.jpowsour.2017.04.084

Cited by 255 publications

(116 citation statements)

References 35 publications

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“…[75], according to a mechanistic and prognostic model, a dynamic cycle-life model was proposed to capture the capacity degradation dynamics under the conditions of varying load for large-format Liion batteries. Gao et al [76] analysed the battery aging under different charging conditions and concluded that charging current less than 1 C affects the active material loss, cut-off voltage less than 4.2 V affects the lithium loss respectively. Then an experiential life model was proposed to capture the relations of degraded battery capacity and charging stresses.…”

Section: Soh Estimationmentioning

confidence: 99%

A brief review on key technologies in the battery management system of electric vehicles

et al. 2018

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Batteries have been widely applied in many high-power applications, such as electric vehicles (EVs) and hybrid electric vehicles, where a suitable battery management system (BMS) is vital in ensuring safe and reliable operation of batteries. This paper aims to give a brief review on several key technologies of BMS, including battery modelling, state estimation and battery charging. First, popular battery types used in EVs are surveyed, followed by the introduction of key technologies used in BMS. Various battery models, including the electric model, thermal model and coupled electro-thermal model are reviewed. Then, battery state estimations for the state of charge, state of health and internal temperature are comprehensively surveyed. Finally, several key and traditional battery charging approaches with associated optimization methods are discussed.

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Section: Soh Estimationmentioning

confidence: 99%

A brief review on key technologies in the battery management system of electric vehicles

et al. 2018

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“…According to the prior data, which was tested by the same measurement method and the environment using the hybrid pulse power characterization (HPPC) test with an ambient temperature of 25 • C ± 3 • C around the kind of cells, the standard deviation of the resistance of the same batch battery is about 0.8 mΩ [25]. An estimation error of less than 1% is assumed.…”

Section: Determination Of Sample Sizementioning

confidence: 99%

Effects of Vibration on the Electrical Performance of Lithium-Ion Cells Based on Mathematical Statistics

Zhang

Ning

et al. 2017

Applied Sciences

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Lithium-ion batteries are increasingly used in mobile applications where mechanical vibrations and shocks are a constant companion. There is evidence both in the academic and industrial communities to suggest that the electrical performance and mechanical properties of the lithium-ion cells of an electric vehicle (EV) are affected by the road-induced vibration. However, only a few studies related to the effects of vibration on the degradation of electrical performance of lithium-ion batteries have been approached. Therefore, this paper aimed to investigate the effects of vibration on the DC resistance, 1C capacity and consistency of NCR18650BE lithium-ion cells. Based on mathematical statistics, the method changes of the DC resistance and the capacity of the cells both before and after the test were analyzed with a large sample size. The results identified that a significant increase in DC resistance was observed as a result of vibration at the 95% confidence level, while typically a reduction in 1C capacity was also noted. In addition, based on a multi-feature quantity, a clustering algorithm was adopted to analyze the effect of vibration on cell consistency; the results show that the cell consistency had deteriorated after the vibration test.

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“…Specifically, x represents charge-discharge cycles and the calibration results are shown in Table 4. In order to apply the model of battery decay and calibration parameters of [35] to the research in this study, the aging model has been further deduced, and (19) can be changed to:…”

Section: Attenuation Comparison Of Battery Capacitymentioning

confidence: 99%

Multi-Objective Optimal Charging Method for Lithium-Ion Batteries

Shi

2017

Energies

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Abstract:In order to optimize the charging of lithium-ion batteries, a multi-stage charging method that considers the charging time and energy loss as optimization targets has been proposed in this paper. First, a dynamic model based on a first-order circuit has been established, and the model parameters have been identified. Second, on the basis of the established model, we treat the objective function of the optimization problem as a weighted sum of charging time and energy loss. Finally, a dynamic programming algorithm (DP) has been used to calculate the charging current of the objective function. Simulation and experimental results show that the proposed charging method could effectively reduce the charging time and decrease the energy loss, compared with the constant-current constant-voltage charging method, under the premise of exerting little influence on the attenuation of battery capacity.

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Lithium-ion battery aging mechanisms and life model under different charging stresses

Cited by 255 publications

References 35 publications

A brief review on key technologies in the battery management system of electric vehicles

A brief review on key technologies in the battery management system of electric vehicles

Effects of Vibration on the Electrical Performance of Lithium-Ion Cells Based on Mathematical Statistics

Multi-Objective Optimal Charging Method for Lithium-Ion Batteries

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