Fast Charging Li-Ion Battery Capacity Fade Prognostic Modeling Using Correlated Parameters' Decomposition and Recurrent Wavelet Neural Network

Khalid, Asadullah; Sarwat, Arif I.

doi:10.1109/itec51675.2021.9490177

Cited by 8 publications

(10 citation statements)

References 11 publications

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“…Furthermore, capacity fading tests using the cycle shown in Fig. 1a also indicate that the higher the charge and discharge rates, the higher the capacity fading [2,5]. Despite, if the test cycle shown in Fig.…”

Section: Article Titlementioning

confidence: 89%

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Real-World Approach for Evaluating Capacity Fading in Lithium-ion Batteries Using Novel Cyclic Testing Methods

Mulpuri

Sah

Kumar

2023

Preprint

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Battery lifespan estimation is an essential aspect of battery management systems. It allows end users and original equipment manufacturers to plan for future battery replacements. However, accurately estimating the capacity of a battery is complex due to the different capacity fading phenomenon which are related to different factors such as temperature, charge-discharge rate, rest period duration, etc. A realistic cyclic testing method is needed to evaluate the life span, as it lets the users assess various factors’ impacting battery performance over time. Different cyclic testing methods are proposed in the literature; however, the most efficient approach will depend on the specific application and the testing objectives. A new approach, closer to real-world driving scenarios, is proposed in this work for battery capacity fading evaluation. A deeper comprehension of the results helps develop an empirical relation appropriate to evaluate capacity fading at given testing conditions easily.

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Section: Article Titlementioning

confidence: 89%

“…The absence of rest periods in testing cycles alters the reaction kinetics rate within the cell. It prevents the cell from reaching an equilibrium state concerning temperature, charge, and concentration [4,5]. These accelerated reaction kinetics lead to higher degradation rates, causing cyclic aging.…”

Section: Article Titlementioning

confidence: 99%

Real-World Approach for Evaluating Capacity Fading in Lithium-ion Batteries Using Novel Cyclic Testing Methods

Mulpuri

Sah

Kumar

2023

Preprint

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“…The reaction mechanism between pyrophoric metal (Na) and hygroscopic metal halides is as shown in Eq. (11), where 'Me' stands for Ni or Fe metals.…”

Section: Na-β Based Batteriesmentioning

confidence: 99%

“…For instance, the Lead Acid (Pb-Acid) chemistry has had over 160 years since its discovery in 1859 to go through ameliorations. Charge holding capacity, time duration, and degradation abridging potential are the crucial appraising factors that have been improved upon for every other storage chemistry that is currently commercialized [10,11]. Further study on improvement and discussions of these key topics are presented in the following sections.…”

Section: Introductionmentioning

confidence: 99%

Sizing and Lifecycle Assessment of Electrochemical Batteries for Electric Vehicles and Renewable Energy Storage Systems

Sarwat¹,

Khalid²,

Jalal³

et al. 2023

Smart Mobility - Recent Advances, New Perspectives and Applications

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Electrochemical batteries have demonstrated quality performances in reducing emissions in Electric Vehicles (EV) and Renewable Energy Storage (RES) systems. These chemistries, although most of them commercialized, contribute to ecological toxicity and global warming in their lifecycle phases. With the addition of new energy storage chemistries, sizing uncertainty and resulting environmental damage are increasing. This chapter presents a comprehensive comparative exploration of 14 electrochemical batteries, including chemistries in the research and development phase. To identify the appropriate chemistry, the capacity range sizing criteria, and formulations are presented with case studies of Environmental Protection Agency (EPA) approved driving profiles for EVs, and consumption load profiles for RES systems, dependent on a given set of operational constraints. Furthermore, a lifecycle impact assessment (LCA) metric, the Cradle-to-Gate technique, is computed to evaluate the sized storage chemistries’ environmental impact supported by five case studies considering short-, medium-, and long-term duration operations and storage services.

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“…In contrast to the model-based methods, data-driven methods take the battery as a black box without paying attention to the physical essence of it. These methods estimate the battery’s SOC directly by learning non-linear relationships between the SOC and the battery measurements (such as current and voltage) [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ]. In particular, more and more neural networks have been developed for SOC estimation due to their strong ability for nonlinear fitting [ 34 ].…”

Section: Introductionmentioning

confidence: 99%

A Dual-Input Neural Network for Online State-of-Charge Estimation of the Lithium-Ion Battery throughout Its Lifetime

Qian

Qin

et al. 2022

Materials

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Online state-of-charge (SOC) estimation for lithium-ion batteries is one of the most important tasks of the battery management system in ensuring its operation safety and reliability. Due to the advantages of learning the long-term dependencies in between the sequential data, recurrent neural networks (RNNs) have been developed and have shown their superiority over SOC estimation. However, only time-series measurements (e.g., voltage and current) are taken as inputs in these RNNs. Considering that the mapping relationship between the SOC and the time-series measurements evolves along with the battery degradation, there still remains a challenge for RNNs to estimate the SOC accurately throughout the battery’s lifetime. In this paper, a dual-input neural network combining gated recurring unit (GRU) layers and fully connected layers (acronymized as a DIGF network) is developed to overcome the above-mentioned challenge. Its most important characteristic is the adoption of the state of health (SOH) of the battery as the network input, in addition to time-series measurements. According to the experimental data from a batch of LiCoO2 batteries, it is validated that the proposed DIGF network is capable of providing more accurate SOC estimations throughout the battery’s lifetime compared to the existing RNN counterparts. Moreover, it also shows greater robustness against different initial SOCs, making it more applicable for online SOC estimations in practical situations. Based on these verification results, it is concluded that the proposed DIGF network is feasible for estimating the battery’s SOC accurately throughout the battery’s lifetime against varying initial SOCs.

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Fast Charging Li-Ion Battery Capacity Fade Prognostic Modeling Using Correlated Parameters' Decomposition and Recurrent Wavelet Neural Network

Cited by 8 publications

References 11 publications

Real-World Approach for Evaluating Capacity Fading in Lithium-ion Batteries Using Novel Cyclic Testing Methods

Real-World Approach for Evaluating Capacity Fading in Lithium-ion Batteries Using Novel Cyclic Testing Methods

Sizing and Lifecycle Assessment of Electrochemical Batteries for Electric Vehicles and Renewable Energy Storage Systems

A Dual-Input Neural Network for Online State-of-Charge Estimation of the Lithium-Ion Battery throughout Its Lifetime

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