Development of a lifetime prediction model for lithium thionyl chloride batteries based on an accelerated degradation test

Cheng, Shiduan; Li, Bimei; Yuan, Zhongzhi; Zhang, Fuyi; Liu, Jincheng

doi:10.1016/j.microrel.2016.07.152

Cited by 30 publications

(12 citation statements)

References 10 publications

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“…Based on the predicted R , the Meta model was developed with the optimization software package 1stOpt based on the universal global optimization algorithm (http://www.7d-soft.com/). 23,24 The constants in equation (17) are determined in Table 3.…”

Section: Resultsmentioning

confidence: 99%

Modeling the motion of small unmanned aerial system (sUAS) due to ground collision

Dori

Divo

et al. 2017

Proceedings of the Institution of Mechanical Engineers, Part G:

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The aim of this paper is to develop a modeling method in order to predict the rebounce distance of a small size unmanned aerial system (sUAS) after collision with the ground. This distance would be useful to determine the safe range on the ground when operating the UAS. A two-step strategy is developed to model this procedure. In step one, numerical simulations are performed to model the first impact and predict the rebounce angle and impact velocity. Based on the information obtained, in step two, an analytical model is employed to calculate the rebounce distance without running numerical simulations, to save the computational time. Based on the model predictions, a non-linear function (known as a Meta model) is established to describe the rebounce behavior as a function of impact parameters, i.e. impact velocity, angle, and friction coefficient between the vehicle and ground. A correlation analysis is further performed to analyze the effect of these parameters on the response. The results show that the maximum rebounce range could be greater than 10 m. Compared to impact angle and friction coefficient, impact velocity is the most significant factor influencing the rebounce range. The larger velocity, smaller impact angle, and smaller ground friction coefficient would lead to the large rebounce range. The established Meta model would be useful to estimate safety range when operating sUAS platforms.

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Section: Resultsmentioning

confidence: 99%

Modeling the motion of small unmanned aerial system (sUAS) due to ground collision

Dori

Divo

et al. 2017

Proceedings of the Institution of Mechanical Engineers, Part G:

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“…For the batteries, many factors are known that they effect the battery health and cause degradation to it, such as DoD, presence of air, imperfect charging algorithm, intensive use, and high external temperature . Some researchers proposed lifetime functions taking into consideration number of factors .…”

Section: Fuzzy Logic‐based Energy Managementmentioning

confidence: 99%

“…Some researchers proposed lifetime functions taking into consideration number of factors . Others, used experimental data to drive empirical functions to estimate the lifetime of the batteries .…”

Section: Fuzzy Logic‐based Energy Managementmentioning

confidence: 99%

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Fuzzy energy management of hybrid renewable power system with the aim to extend component lifetime

Ameur

Hadjaissa

Cheikh

et al. 2017

Int. J. Energy Res.

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Summary In this paper, a fuzzy energy management algorithm for a hybrid renewable power system based on lifetime extending is presented. When the system contains two storage elements or more, the selection of the suitable element to be charged or discharged becomes of paramount importance. When the storage elements are of different types, the decision will be difficult. Conventional algorithms that make series of tests to select the storage element choose always the first available element. This way of testing affects badly the most used element and may affect the other storage elements too as they rarely operate under hard load scenarios. In this study, and in order to solve this problem, two fuzzy controllers have been used to manage the energy flow for a hybrid renewable power system. It is composed of: a photovoltaic generator as a main source, a fuel cell and batteries as a storage elements. The controllers operate as master and slave. The master controller gives orders to all the system power converters and to the slave controller as well. The latter is activated only when the storage elements are at the same state of charge. It is charged, instead of the master's, to select the suitable element to be charged or discharged. Its orders are given based on lifetime functions for each element. To examine the proposed algorithm, simulations have been performed under Matlab/Simulink (The MathWorks, Inc., Massachusetts, USA). Comparison and statistics have been carried out to give the percentage of the worked hours for each element in each operating mode. The obtained results show the high performance of the proposed algorithm. Copyright © 2017 John Wiley & Sons, Ltd.

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“…The parametric methods mentioned above all require the specification of a product's lifetime‐stress acceleration model. However, when the product's degradation mechanism becomes quite complicated, developing an accurate acceleration model is nearly impossible . On the other hand, machine learning methods, which purely rely on observed data, can avoid such restriction.…”

Section: Introductionmentioning

confidence: 99%

Ensemble learning for predicting degradation under time‐varying environment

Wang

Lu²,

Wang

et al. 2020

Quality & Reliability Eng

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Product lifetime prediction is challenging when the product is subject to a time-varying operational environment. Most of the existing studies use some functions to explicitly specify the relationship between degradation parameters and environmental conditions so as to reveal how the degradation process evolves over time. However, in many applications, the assumptions needed for establishing these functions cannot be validated in engineering practice or they cannot accurately model the entire underlying degradation mechanism. In contrast to previous work, the focus of our study is placed on product degradation prognosis by implementing an ensemble learning method. This method combines the stochastic process modeling approach and the machine learning approach, taking advantage of these approaches to gain a more accurate and stable degradation prediction. The proposed method is demonstrated by some simulation examples and by a case study of lithium-ion battery accelerated degradation test. Both the simulation study and the real case verify the superiority of the proposed method. The case study indicates that the ensemble learning method can further help to effectively manage the energy storage and energy distribution of battery packs. K E Y W O R D Sdegradation process, ensemble model, Li-ion battery, machine learning, stochastic process

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Development of a lifetime prediction model for lithium thionyl chloride batteries based on an accelerated degradation test

Cited by 30 publications

References 10 publications

Modeling the motion of small unmanned aerial system (sUAS) due to ground collision

Modeling the motion of small unmanned aerial system (sUAS) due to ground collision

Fuzzy energy management of hybrid renewable power system with the aim to extend component lifetime

Ensemble learning for predicting degradation under time‐varying environment

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