Cycle life estimation of lithium-ion polymer batteries using artificial neural network and support vector machine with time-resolved thermography

Zhou, Xunfei; Hsieh, Sheng‐Jen; Peng, Bo; Hsieh, Daniel

doi:10.1016/j.microrel.2017.10.013

Cited by 34 publications

(11 citation statements)

References 39 publications

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“…It is shown that the error of RUL prediction is less than 5%. ANN (an SLFNN model) and SVM models are used to estimate the cycle life of lithium polymer batteries (Zhou et al, 2017). The input for the SLFNN and SVM (has a linear kernel function) is the thermal information (normalized de-trend surface temperature acquired from the infrared images taken in experiments) or the electrical information (the current or voltage).…”

Section: Ll Open Accessmentioning

confidence: 99%

Machine learning toward advanced energy storage devices and systems

Gao

2021

iScience

109

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Technology advancement demands energy storage devices (ESD) and systems (ESS) with better performance, longer life, higher reliability, and smarter management strategy. Designing such systems involve a trade-off among a large set of parameters, whereas advanced control strategies need to rely on the instantaneous status of many indicators. Machine learning can dramatically accelerate calculations, capture complex mechanisms to improve the prediction accuracy, and make optimized decisions based on comprehensive status information. The computational efficiency makes it applicable for real-time management. This paper reviews recent progresses in this emerging area, especially new concepts, approaches, and applications of machine learning technologies for commonly used energy storage devices (including batteries, capacitors/supercapacitors, fuel cells, other ESDs) and systems (including battery ESS, hybrid ESS, grid and microgrid-containing energy storage units, pumped-storage system, thermal ESS). The perspective on future directions is also discussed.

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Section: Ll Open Accessmentioning

confidence: 99%

Machine learning toward advanced energy storage devices and systems

Gao

2021

iScience

109

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“…In formula (10), S is the subset of training samples, p is the number of subsets, H is the total number of model parameters, ) Y is the model output obtained by using dropout technology, and  is the attenuation coefficient of the regularization technology.…”

Section: Let the Random Variablementioning

confidence: 99%

“…The Adam optimization method is often used to optimize the objective function (10).When the optimal approximate distribution of the posterior distribution of the model parameters is obtained, for the newly obtained input sample X  ,the distribution of the model RUL prediction result is:…”

Section: Let the Random Variablementioning

confidence: 99%

“…Reference [9] further considers the effects of discharge current and ambient temperature on capacity reduction during recycling of lithium batteries, and establishes a lithium battery capacity and RUL prediction model that takes into account the effects of external conditions. Zhou et al [10] used an artificial neural network and SVM to predict battery temperature for RUL. Although literature [11] is also based on battery temperature, the established model is a multi-channel neural network capacity prediction model that comprehensively considers the role of charge and discharge terminal voltages and currents in battery performance degradation.…”

Section: Introductionmentioning

confidence: 99%

“…increase. Based on reference [10] ,X et al [12] incorporated the voltage into a stack-type denoising auto encoder to directly predict the RUL of a lithium battery. However, the above literature only builds an RUL prediction model for lithium batteries with one internal state (i.e.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Software-Based Lifetime Prediction of Lithium Batteries Using Deep Learning for Wireless Networks

Hasanuzzaman

2022

Preprint

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The operation of Wireless Sensor Networks (WSNs) is constrained by a number of factors, one of the most important of which is available energy. Electrochemical batteries are commonly used to power sensor nodes. The operating temperature and discharge current values have a significant impact on the stored energy in battery devices. As a result, estimating their voltage/charge behavior over time, which are important factors for the implementation of energy-conscious policies, becomes challenging. To this end, this paper proposes a RUL prediction model based wireless networks that integrates the three internal states of battery capacity, impedance and temperature, and introduces a two-way long and short-term memory network to learn the time correlation of the three state data. The goal of this research is to develop a software-based method for estimating the state of charge and voltage of batteries in WSN nodes using a temperature-dependent analytical battery model. The equivalence between dropout technology and Bayesian variational inference technology is used to quantify the uncertainty of RUL prediction results, and the 95% confidence interval and probability density distribution of the prediction results are obtained, and the influence of different dropout rates on the prediction uncertainty is analyzed. Impact. The experiment shows that the comparison experiment of four different deep learning model frameworks and two internal state input schemes verifies the effectiveness of the learning method based on the two-way long and short-term memory network.

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Recent Advances in the Development of Li‐Air Batteries, Experimental and Predictive Approaches – Prospective, Challenges, and Opportunities

Jilani,

Awan,

Taqvi

et al. 2023

ChemBioEng Reviews

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The significance of lithium‐air batteries (LABs) has recently increased as they are a highly competitive alternative to lithium‐ion batteries (LIBs) for powering electric automobiles. The reason behind this is their excessive theoretical energy density. The advancement, breakthroughs, and drawbacks of LABs are examined. A comprehensive evaluation of various energy storage devices is provided, followed by an evaluation of several electrochemical storage technologies, their advantages, and drawbacks. Also, recent advances in LABs are reviewed and potential machine learning techniques are explored to address the existing obstacles hindering the widespread adoption of LABs.

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Cycle life estimation of lithium-ion polymer batteries using artificial neural network and support vector machine with time-resolved thermography

Cited by 34 publications

References 39 publications

Machine learning toward advanced energy storage devices and systems

Machine learning toward advanced energy storage devices and systems

A Software-Based Lifetime Prediction of Lithium Batteries Using Deep Learning for Wireless Networks

Recent Advances in the Development of Li‐Air Batteries, Experimental and Predictive Approaches – Prospective, Challenges, and Opportunities

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