A Hybrid Data-Driven Approach for Multistep Ahead Prediction of State of Health and Remaining Useful Life of Lithium-Ion Batteries

Ali, Muhammad Umair; Zafar, Amad; Masood, Haris; Kallu, Karam Dad; Khan, Muhammad Attique; Tariq, Usman; Kim, Ye Jin; Chang, Byoungchol

doi:10.1155/2022/1575303

Cited by 13 publications

(7 citation statements)

References 57 publications

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“…At present, there are three main prediction methods for lithium-ion battery remaining useful life (RUL): mechanismbased model (Fang et al, 2021), semi-empirical model (Varini et al, 2019), and data-driven model (Ali et al, 2022;Pugalenthi et al, 2022). The mechanism-based prediction method is to establish a degradation model by analyzing the internal structure of the lithium-ion battery, which can be divided into three categories: electrochemical model, equivalent circuit model, and empirical model.…”

Section: Introductionmentioning

confidence: 99%

An improved deep extreme learning machine to predict the remaining useful life of lithium-ion battery

Gao

Huang

2022

Front. Energy Res.

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To aim at the problem of inaccurate prediction of the remaining useful life of the lithium-ion battery, an improved grey wolf optimizer optimizes the deep extreme learning machine (CGWO-DELM) data-driven forecasting method is proposed. This method uses the grey wolf optimization algorithm based on an adaptive normal cloud model to optimize the bias of the deep extreme learning machine, the weight of the input layer, the selection of activation function, and the number of hidden layer nodes. In this article, indirect health factors that can characterize the degradation of battery performance are extracted from the discharge process, and the correlation between them and capacity is analyzed using the Pearson coefficient and Kendel coefficient. Then, the CGWO-DELM prediction model is constructed to predict the capacitance of the lithium-ion battery. The remaining useful life of lithium-ion batteries is indirectly predicted with a 1.44 A·h failure threshold. The prediction results are compared with deep extreme learning machines, long-term memory, other prediction methods, and the current public prediction methods. The results show that the CGWO-DELM prediction method can more accurately predict the remaining useful life of lithium-ion batteries.

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Section: Introductionmentioning

confidence: 99%

An improved deep extreme learning machine to predict the remaining useful life of lithium-ion battery

Gao

Huang

2022

Front. Energy Res.

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“…The KRLS handles the nonlinear issues as a nonlinear online prediction model by depicting samples from the actual space to the high-dimensional space using the kernel functions [64]. It adapts and dynamically updates the model depending on time series data, lowering model complexity and computation time.…”

Section: Kernel Recursive Least Square Modelmentioning

confidence: 99%

Management of Distributed Renewable Energy Resources with the Help of a Wireless Sensor Network

2022

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Photovoltaic (PV) and wind energy are widely considered eco-friendly renewable energy resources. However, due to the unpredictable oscillations in solar and wind power production, efficient management to meet load demands is often hard to achieve. As a result, precise forecasting of PV and wind energy production is critical for grid managers to limit the impact of random fluctuations. In this study, the kernel recursive least-squares (KRLS) algorithm is proposed for the prediction of PV and wind energy. The wireless sensor network (WSN) typically adopted for data collection with a flexible configuration of sensor nodes is used to transport PV and wind production data to the monitoring center. For efficient transmission of the data production, a link scheduling technique based on sensor node attributes is proposed. Different statistical and machine learning (ML) techniques are examined with respect to the proposed KRLS algorithm for performance analysis. The comparison results show that the KRLS algorithm surpasses all other regression approaches. For both PV and wind power feed-in forecasts, the proposed KRLS algorithm demonstrates high forecasting accuracy. In addition, the link scheduling proposed for the transmission of data for the management of distributed renewable energy resources is compared with a reference technique to show its comparable performance. The efficacy of the proposed KRLS model is better than other regression models in all assessment events in terms of an RMSE value of 0.0146, MAE value of 0.00021, and R2 of 99.7% for PV power, and RMSE value of 0.0421, MAE value of 0.0018, and R2 of 88.17% for wind power. In addition to this, the proposed link scheduling approach results in 22% lower latency and 38% higher resource utilization through the efficient scheduling of time slots.

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“…Moreover, the real-time monitoring of ECG signals can help the practitioners to quickly locate the affected regions. However, ECG images need experts to examine them which can delay the detection process (11,12). Moreover, existing ECG-based COVID-19 recognition frameworks are concerned with converting the input samples into statistical data and performing the required computation, which in turn causes an increase in the computational cost and the detection time.…”

Section: Introductionmentioning

confidence: 99%

Efficient-ECGNet framework for COVID-19 classification and correlation prediction with the cardio disease through electrocardiogram medical imaging

et al. 2022

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In the last 2 years, we have witnessed multiple waves of coronavirus that affected millions of people around the globe. The proper cure for COVID-19 has not been diagnosed as vaccinated people also got infected with this disease. Precise and timely detection of COVID-19 can save human lives and protect them from complicated treatment procedures. Researchers have employed several medical imaging modalities like CT-Scan and X-ray for COVID-19 detection, however, little concentration is invested in the ECG imaging analysis. ECGs are quickly available image modality in comparison to CT-Scan and X-ray, therefore, we use them for diagnosing COVID-19. Efficient and effective detection of COVID-19 from the ECG signal is a complex and time-taking task, as researchers usually convert them into numeric values before applying any method which ultimately increases the computational burden. In this work, we tried to overcome these challenges by directly employing the ECG images in a deep-learning (DL)-based approach. More specifically, we introduce an Efficient-ECGNet method that presents an improved version of the EfficientNetV2-B4 model with additional dense layers and is capable of accurately classifying the ECG images into healthy, COVID-19, myocardial infarction (MI), abnormal heartbeats (AHB), and patients with Previous History of Myocardial Infarction (PMI) classes. Moreover, we introduce a module to measure the similarity of COVID-19-affected ECG images with the rest of the diseases. To the best of our knowledge, this is the first effort to approximate the correlation of COVID-19 patients with those having any previous or current history of cardio or respiratory disease. Further, we generate the heatmaps to demonstrate the accurate key-points computation ability of our method. We have performed extensive experimentation on a publicly available dataset to show the robustness of the proposed approach and confirmed that the Efficient-ECGNet framework is reliable to classify the ECG-based COVID-19 samples.

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A Hybrid Data-Driven Approach for Multistep Ahead Prediction of State of Health and Remaining Useful Life of Lithium-Ion Batteries

Cited by 13 publications

References 57 publications

An improved deep extreme learning machine to predict the remaining useful life of lithium-ion battery

An improved deep extreme learning machine to predict the remaining useful life of lithium-ion battery

Management of Distributed Renewable Energy Resources with the Help of a Wireless Sensor Network

Efficient-ECGNet framework for COVID-19 classification and correlation prediction with the cardio disease through electrocardiogram medical imaging

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