Online state of health prediction method for
            <scp>lithium‐ion</scp>
            batteries, based on gated recurrent unit neural networks

Ungurean, Lucian; Micea, Mihai V.; Cârstoiu, Gabriel

doi:10.1002/er.5413

Cited by 86 publications

(32 citation statements)

References 24 publications

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“…Reference [125] proposes a method based on support vector regression (SVR) to accurately predict the RUL. Reference [126] realizes battery SOH prediction based on the gated recurrent unit (GRU). Reference [127] estimates battery parameters based on a deep Bayesian neural network.…”

Section: Neural Network Algorithmmentioning

confidence: 99%

Toward Group Applications: A Critical Review of the Classification Strategies of Lithium-Ion Batteries

Zhang

et al. 2020

WEVJ

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To solve the problems of the decreased reliability and safety of battery pack due to the inconsistency between batteries after single batteries are grouped is of great significance to find an appropriate sorting method of single batteries. This study systematically reviews the available literature on battery sorting applications for battery researchers and users. These methods can be roughly divided into three types: direct measurement, sorting based on the model, and sorting based on the material chemistry of batteries. Among them, direct measurement is about the direct measurement of the state parameters of batteries using some professional instruments or testing tools to sort and group batteries with similar or close parameters. Sorting based on the model classifies batteries into groups by establishing a battery equivalent model and carrying out model identification and parameter estimation with machine learning or artificial intelligence algorithm. Sorting based on the material chemistry of batteries is to explore some characteristics related to the chemical mechanism inside the battery. On the basis of reading extensive literature, the methods for classification of battery are provided with an in-depth explanation, and each corresponding strengths and weaknesses of these methods are analyzed. Finally, the future developments of advanced sorting algorithms and batteries prospect.

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Section: Neural Network Algorithmmentioning

confidence: 99%

Toward Group Applications: A Critical Review of the Classification Strategies of Lithium-Ion Batteries

Zhang

et al. 2020

WEVJ

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“…unprecedented results across various application domains 16,17,18,19,20 . In the task of unsupervised learning, generative models are one of the most promising technologies.…”

Section: Introductionmentioning

confidence: 99%

Day-ahead renewable scenario forecasts based on generative adversarial networks

Jiang¹,

Mao²,

Chai³

et al. 2021

Preprint

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<p>With the increasing penetration of renewable resources such as wind and solar, especially in terms of large-scale integration, the operation and planning of power systems are faced with great risks due to the inherent stochasticity of natural resources. Although this uncertainty can be anticipated, the timing, magnitude, and duration of fluctuations cannot be predicted accurately. In addition, the outputs of renewable power sources are correlated in space and time, and this brings further challenges for predicting the characteristics of their future behavior. To address these issues, this paper describes an unsupervised distribution learning method for renewable scenario forecasts that considers spatiotemporal correlation based on generative adversarial network (GAN), which has been shown to generate realistic time series for stochastic processes. We first utilize an improved GAN to learn unknown data distributions and model the dynamic processes of renewable resources. We then generate a large number of forecasted scenarios using stochastic constrained optimization. For validation, we use power generation data from the National Renewable Energy Laboratory wind and solar integration datasets. The simulation results show that the generated trajectories not only reflect the future power generation dynamics, but also correctly capture the temporal, spatial, and fluctuant characteristics of the real power generation processes. The experimental comparisons verify the superiority of the proposed method and indicate that it can reduce at least 50% of the training iterations of the generative model for scenario forecasts.<br></p>

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“…However, only battery capacity data were considered for SoH estimation without leveraging easily measurable battery signals such as voltage and current. Ungurean et al 29 implemented gated recurrent unit neural networks (GRU) for online SoH prediction of lithium‐ion batteries. Compared with LSTM, GRU obtained slightly higher prediction errors but required fewer learnable parameters.…”

Section: Introductionmentioning

confidence: 99%

“…30 did not evaluate individual role of each battery measurable signal in estimating battery capacity. Hence, the previous works, related to data‐driven based battery capacity estimation, 28‐30,40,41 lacked analysis in terms of effect of sampling rate of battery input data on battery capacity estimation. Also, the relative contributory roles of battery measurable signals such as voltage (V), current (I), temperature (T), and charge capacity (C) in interpreting battery aging characteristics have not been studied so far 20,35,38 .…”

Section: Introductionmentioning

confidence: 99%

Deep learning networks for capacity estimation for monitoring SOH of Li‐ion batteries for electric vehicles

et al. 2020

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Summary Data‐driven modeling using measurable battery signals tends to provide robust battery capacity estimation without delving deep into electrochemical phenomenon inside the battery. Nowadays, with the advent of artificial intelligence, deep neural networks are playing crucial role in data modeling and analysis. In this article, models of three different families of network architectures such as feed‐forward neural network (FNN), convolutional neural network (CNN), and long short‐term memory neural network (LSTM) are proposed for battery capacity estimation. Measurements from a set of two rechargeable Li‐ion batteries are considered for the model performance evaluation. The battery capacity estimation by different models has been evaluated by considering the effect of certain parameters such as model complexity, sampling rate of battery measurable signals and type of battery measurable signals. With its ability to process time‐series data efficiently by memorizing long‐term dependencies, LSTM outperforms other model architectures in estimating battery capacity more accurately and flexibly with 4.69% and 19.16% decline in average test root mean square error (RMSE) as compared with FNN and CNN, respectively. Simpler architectures of LSTM and FNN are able to perform well as compared with CNN, which needs architecture with certain hidden layers to interpret the battery aging process. Moreover, investigations reveal that sparsely sampled battery signals help all the proposed models to learn the battery dynamics in a better way as compared to densely sampled battery signals which also entails for less complex model learning process. Further, among all battery measurable signals, battery temperature has relatively less weightage in estimating battery capacity.

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Online state of health prediction method for lithium‐ion batteries, based on gated recurrent unit neural networks

Cited by 86 publications

References 24 publications

Toward Group Applications: A Critical Review of the Classification Strategies of Lithium-Ion Batteries

Toward Group Applications: A Critical Review of the Classification Strategies of Lithium-Ion Batteries

Day-ahead renewable scenario forecasts based on generative adversarial networks

Deep learning networks for capacity estimation for monitoring SOH of Li‐ion batteries for electric vehicles

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