Assessing the Health of LiFePO4 Traction Batteries through Monotonic Echo State Networks

Sánchez, Luciano; Anseán, David; Otero, José; Couso, Inés

doi:10.3390/s18010009

Cited by 12 publications

(5 citation statements)

References 44 publications

(54 reference statements)

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“…Since both active materials operate within the stability window of the electrolyte, the formation of an effective and protecting SEI on Li 4 Ti 5 O 12 does not occur Figure A shows the voltage profile of an LTO–LFP battery cell in the absence and in the presence of a redox mediator galvanostatically cycled at 0.3 C. The profiles are comparable to those reported in the literature for this battery chemistries. − The capacity in the first discharge step in the absence of a redox mediator was 8.5 mAh, which is close to the theoretical value of 10 mAh (1 mAh cm –2 ). The only significant difference in the voltage profile related to the presence of a redox mediator is the longer charge process and the short discharge step.…”

Section: Resultssupporting

confidence: 80%

New Technique for Probing the Protecting Character of the Solid Electrolyte Interphase as a Critical but Elusive Property for Pursuing Long Cycle Life Lithium-Ion Batteries

García-Quismondo

Alvarez-Conde

García

et al. 2022

ACS Appl. Mater. Interfaces

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The formation of a protecting nanolayer, so-called solid electrolyte interphase (SEI), on the negative electrode of Li-ion batteries (LIBs) from product precipitation of the cathodic decomposition of the electrolyte is a blessing since the electrically insulating nature of this nanolayer protects the electrode surface, preventing continuous electrolyte decomposition and enabling the large nominal cell voltage of LIBs, e.g., 3.3–3.8 V. Thus, the protection performance of the nanolayer SEI is essential for LIBs to achieve a long cycle life. Unfortunately, the evaluation of this critical property of the SEI is not trivial. Herein, a new, cheap, and easily implementable methodology, the redox-mediated enhanced coulometry, is presented to estimate the protecting quality of the SEI. The key element of the methodology is the addition of a redox mediator in the electrolyte during the degassing step (after the SEI formation cycle). The redox mediator leads to an internal self-discharge process that is inversely proportional to the protecting character of the SEI. Also, the self-discharge process results in an easily measurable decrease in Coulombic efficiency. The influence of vinylene carbonate as an electrolyte additive in the resulting SEI is used as a case study to showcase the potential of the proposed methodology.

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Section: Resultssupporting

confidence: 80%

New Technique for Probing the Protecting Character of the Solid Electrolyte Interphase as a Critical but Elusive Property for Pursuing Long Cycle Life Lithium-Ion Batteries

García-Quismondo

Alvarez-Conde

García

et al. 2022

ACS Appl. Mater. Interfaces

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“…Year Application [71] 2017 Fuel cell voltage ageing [32] 2017 Health of automotive batteries [72] 2017 Slugging flow phenomenon [13] 2017 Temperature/Rainfall [73] 2018 Lorenz/Rossler/Sunspot-Runoff [34] 2019 Industrial processes [35] 2019 Fuel cell durability [74] 2019 Photovoltaic voltage [75] 2020 Electricity load [76] 2020 Electricity load [77] 2020 Energy consumption/Wind power generation [78] 2020 Temperature of exhaust gas [36] 2020 Faults in airplane engines [79] 2020 Multiple time series [25] 2020 Blood glucose concentration [80] 2021 Multiple time series [81] 2021 Electrical load [16] 2021 Air Quality Index [82] 2022 Chaotic time series…”

Section: Refmentioning

confidence: 99%

“…Time series forecasting estimates values that a time series takes in the future, allowing the implementation of decision-making strategies, e.g., abandonment of fossil fuels to reduce the surface temperature of the Earth. Specifically, time series forecasting is very relevant for the energy domain (e.g., electricity load demand [7,8], solar and wind power estimation [9,10]), meteorology (e.g., prediction of wind speed [11], temperature [12,13], humidity [12], precipitation [13,14]), air pollution monitoring (e.g., prediction of PM 2.5 , PM 10 , NO 2 , O 3 , SO 2 , and CO 2 concentrations [12,15,16]), the finance domain (e.g., stock market index and shares prediction [17,18], the stock price [19,20], exchange rate [21,22]), health (e.g., prediction of infective diseases diffusion [23], diabetes mellitus [24], blood glucose concentration [25], and cancer growth [26]), traffic (e.g., traffic speed and flow prediction [27][28][29][30]), and industrial production (e.g., petroleum production [31], remaining life prediction [23,32,33], industrial processes [34], fuel cells durability [35], engine faults [36]). Deep learning algorithms are currently the leading methods in machine learning due to their successful application to many computer science domains (e.g., computer vision, natural language processing, speech recognition).…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Time Series Forecasting: Advances and Open Problems

Casolaro,

Capone,

Iannuzzo

et al. 2023

Information

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A time series is a sequence of time-ordered data, and it is generally used to describe how a phenomenon evolves over time. Time series forecasting, estimating future values of time series, allows the implementation of decision-making strategies. Deep learning, the currently leading field of machine learning, applied to time series forecasting can cope with complex and high-dimensional time series that cannot be usually handled by other machine learning techniques. The aim of the work is to provide a review of state-of-the-art deep learning architectures for time series forecasting, underline recent advances and open problems, and also pay attention to benchmark data sets. Moreover, the work presents a clear distinction between deep learning architectures that are suitable for short-term and long-term forecasting. With respect to existing literature, the major advantage of the work consists in describing the most recent architectures for time series forecasting, such as Graph Neural Networks, Deep Gaussian Processes, Generative Adversarial Networks, Diffusion Models, and Transformers.

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“…ESNs are widely used in industry [207,208,17,207], considering in particular the energy and manufacturing sectors. in energy fields, ESNs have applied in traditional energy forecasting [209], like prognostic of fuel cells [210,97,211], health of batteries [212], oil production platform control [213], gas prediction [132] and electric ship [214], and clean energy forecasting, like wind power generation [215,216] and photovoltaic power prediction [164,217,217,197].…”

Section: Real-world Tasks Orientatedmentioning

confidence: 99%

A Review of Designs and Applications of Echo State Networks

Sun¹,

Song²,

Hong³

et al. 2020

Preprint

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Recurrent Neural Networks (RNNs) have demonstrated their outstanding ability in sequence tasks and have achieved state-of-the-art in wide range of applications, such as industrial, medical, economic and linguistic. Echo State Network (ESN) is simple type of RNNs and has emerged in the last decade as an alternative to gradient descent training based RNNs. ESN, with a strong theoretical ground, is practical, conceptually simple, easy to implement. It avoids non-converging and computationally expensive in the gradient descent methods. Since ESN was put forward in 2002, abundant existing works have promoted the progress of ESN, and the recently introduced Deep ESN model opened the way to uniting the merits of deep learning and ESNs. Besides, the combinations of ESNs with other machine learning models have also overperformed baselines in some applications. However, the apparent simplicity of ESNs can sometimes be deceptive and successfully applying ESNs needs some experience. Thus, in this paper, we categorize the ESN-based methods to basic ESNs, DeepESNs and combinations, then analyze them from the perspective of theoretical studies, network designs and specific applications. Finally, we discuss the challenges and opportunities of ESNs by summarizing the open questions and proposing possible future works.

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Assessing the Health of LiFePO4 Traction Batteries through Monotonic Echo State Networks

Cited by 12 publications

References 44 publications

New Technique for Probing the Protecting Character of the Solid Electrolyte Interphase as a Critical but Elusive Property for Pursuing Long Cycle Life Lithium-Ion Batteries

New Technique for Probing the Protecting Character of the Solid Electrolyte Interphase as a Critical but Elusive Property for Pursuing Long Cycle Life Lithium-Ion Batteries

Deep Learning for Time Series Forecasting: Advances and Open Problems

A Review of Designs and Applications of Echo State Networks

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