Semi-supervised self-learning-based lifetime prediction for batteries

IEEE Transactions on Industrial Informatics

Published On 2022/9/15

Accurate and reliable degradation and lifetime prediction for lithium-ion batteries is the main challenge for smart prognostic and health management. This article proposes a novel semi-supervised self-learning method for battery lifetime prediction. First, three health indicators (HIs) are extracted from the partial capacity-voltage curve. Second, the capacity estimation model and lifetime prediction model are built using data from three randomly selected batteries in the source domain. Then, the HIs are used to reconstruct the historical capacities to provide pseudo values for self-training of the lifetime model. Finally, the self-trained lifetime model is used to predict future degradation. The uncertainty expression is also included to provide the probabilistic prediction of future capacities. Different application scenarios are considered in the verification. The mean lifetime prediction error is less than 23 cycles with only three …

Journal

IEEE Transactions on Industrial Informatics

Published On

2022/9/15

Volume

19

Issue

5

Page

6471 - 6481

Authors

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

Position

Professor at

H-Index(all)

104

H-Index(since 2020)

72

I-10 Index(all)

0

I-10 Index(since 2020)

0

Citation(all)

0

Citation(since 2020)

0

Cited By

0

Research Interests

Power Electronics

Smart Batteries

AI

University Profile Page

Daniel Stroe

Daniel Stroe

Aalborg Universitet

Position

Head of Battery Storage Systems Research Programme at

H-Index(all)

49

H-Index(since 2020)

46

I-10 Index(all)

0

I-10 Index(since 2020)

0

Citation(all)

0

Citation(since 2020)

0

Cited By

0

Research Interests

Lithium-ion Batteries

Energy Storage

Electric Vehicles

Renewable Energy

Energy Management

University Profile Page

Yunhong Che

Yunhong Che

Chongqing University

Position

H-Index(all)

16

H-Index(since 2020)

16

I-10 Index(all)

0

I-10 Index(since 2020)

0

Citation(all)

0

Citation(since 2020)

0

Cited By

0

Research Interests

Energy Storage Systems

Transportation electrification

Prognostics and Health Management

Battery

University Profile Page

Other Articles from authors

Daniel Stroe

Daniel Stroe

Aalborg Universitet

Data in Brief

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Yunhong Che

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Health Prediction for Lithium-Ion Batteries Under Unseen Working Conditions

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Daniel Stroe

Daniel Stroe

Aalborg Universitet

On the Use of Randomly Selected Partial Charges to Predict Battery State-of-Health

As society becomes more reliant on Lithium-ion (Li-ion) batteries, state-of-health (SOH) estimation will need to become more accurate and reliable. Therefore, SOH modelling is in the process of shifting from using simple and continuous charge/discharge profiles, to more dynamic profiles constructed to mimic real operation, when ageing the Li-ion batteries. However, in most cases, when ageing the batteries, the same exact profile is just repeated until the battery reaches its end-of-life. Using data from batteries aged in this fashion to build a model, there is a very real possibility that the model will rely on the built-in repetitiveness of the profile. Therefore, this work will examine the dependence of the performance of a multiple linear regression on the number of charges used to train the model, and their location within the profile used to age the batteries. The investigation shows that it is possible to build models using randomly selected partial charges while still reaching errors as low as 0.5%. Furthermore, it shows that only two randomly sampled partial charges are needed to achieve errors of less than 1%. Lastly, as the number of randomly sampled partial charges used to create the model increases, then the dependence on particular partial charges tends to decrease.

Yunhong Che

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Online Sensorless Temperature Estimation of Lithium-Ion Batteries Through Electro-Thermal Coupling

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Daniel Stroe

Daniel Stroe

Aalborg Universitet

Batteries

Lithium-Ion Supercapacitors and Batteries for Off-Grid PV Applications: Lifetime and Sizing

The intermittent nature of power generation from photovoltaics (PV) requires reliable energy storage solutions. Using the storage system outdoors exposes it to variable temperatures, affecting both its storage capacity and lifespan. Utilizing and optimizing energy storage considering climatic variations and new storage technologies is still a research gap. Therefore, this paper presents a modified sizing algorithm based on the Golden Section Search method, aimed at optimizing the number of cells in an energy storage unit, with a specific focus on the unique conditions of Denmark. The considered energy storage solutions are Lithium-ion capacitors (LiCs) and Lithium-ion batteries (LiBs), which are tested under different temperatures and C-rates rates. The algorithm aims to maximize the number of autonomy cycles—defined as periods during which the system operates independently of the grid, marked by intervals between two consecutive 0% State of Charge (SoC) occurrences. Testing scenarios include dynamic temperature and dynamic load, constant temperature at 25 °C, and constant load, considering irradiation and temperature effects and cell capacity fading over a decade. A comparative analysis reveals that, on average, the LiC storage is sized at 70–80% of the LiB storage across various scenarios. Notably, under a constant-temperature scenario, the degradation rate accelerates, particularly for LiBs. By leveraging the modified Golden Section Search algorithm, this study provides an efficient approach to the sizing problem, optimizing the number of cells and thus offering a potential solution for energy storage in off-grid PV systems.

Daniel Stroe

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Aalborg Universitet

Journal of Energy Storage

Identification of the aging state of lithium-ion batteries via temporal convolution network and self-attention mechanism

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Daniel Stroe

Daniel Stroe

Aalborg Universitet

Battery state-of-health estimation using machine learning

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Yunhong Che

Yunhong Che

Chongqing University

Thermal state monitoring of lithium-ion batteries: Progress, challenges, and opportunities

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Daniel Stroe

Daniel Stroe

Aalborg Universitet

Advanced Energy Materials

Unravelling the Mechanism of Pulse Current Charging for Enhancing the Stability of Commercial LiNi0.5Mn0.3Co0.2O2/Graphite Lithium‐Ion Batteries

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Daniel Stroe

Daniel Stroe

Aalborg Universitet

Thermal state monitoring of lithium-ion batteries: Progress, challenges, and opportunities

Transportation electrification is a promising solution to meet the ever-rising energy demand and realize sustainable development. Lithium-ion batteries, being the most predominant energy storage devices, directly affect the safety, comfort, driving range, and reliability of many electric mobilities. Nevertheless, thermal-related issues of batteries such as potential thermal runaway, performance degradation at low temperatures, and accelerated aging still hinder the wider adoption of electric mobilities. To ensure safe, efficient, and reliable operations of lithium-ion batteries, monitoring their thermal states is critical to safety protection, performance optimization, as well as prognostics, and health management. Given insufficient onboard temperature sensors and their inability to measure battery internal temperature, accurate and timely temperature estimation is of particular importance to thermal state monitoring. Toward …

Yunhong Che

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Chongqing University

Reliability Engineering & System Safety

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Daniel Stroe

Daniel Stroe

Aalborg Universitet

State-of-energy balancing control with cascaded H-bridge for second-life batteries

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Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

IEEE Transactions on Industrial Electronics

Health Prediction for Lithium-Ion Batteries Under Unseen Working Conditions

Battery health prediction is significant while challenging for intelligent battery management. This article proposes a general framework for both short-term and long-term predictions of battery health under unseen dynamic loading and temperature conditions using domain-adaptive multitask learning (MTL) with long-term regularization. First, features extracted from partial charging curves are utilized for short-term state of health predictions. Then, the long-term degradation trajectory is directly predicted by recursively using the predicted features within the multitask framework, enhancing the model integrity and lowering the complexity. Then, domain adaptation (DA) is adopted to reduce the discrepancies between different working conditions. Additionally, a long-term regularization is introduced to address the shortcoming that arises when the model is extrapolated recursively for future health predictions. Thus, the short …

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

IEEE/ASME Transactions on Mechatronics

Online Sensorless Temperature Estimation of Lithium-Ion Batteries Through Electro-Thermal Coupling

Owing to the nonnegligible impacts of temperature on the safety, performance, and lifespan of lithium-ion batteries, it is essential to regulate battery temperature to an optimal range. Temperature monitoring plays a fundamental role in battery thermal management, yet it is still challenged by limited onboard temperature sensors, particularly in large-scale battery applications. As such, developing sensorless temperature estimation is of paramount importance to acquiring the temperature information of each cell in a battery system. This article proposes an estimation approach to obtain the cell temperature by taking advantage of the electrothermal coupling effect of batteries. An electrothermal coupled model, which captures the interactions between the electrical and the thermal dynamics, is established, parameterized, and experimentally validated. A closed-loop observer is then designed based on this coupled model …

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

Artificial Intelligence-Based State-of-Health Estimation of Lithium-Ion Batteries

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Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

arXiv preprint arXiv:2402.07777

Novel Low-Complexity Model Development for Li-ion Cells Using Online Impedance Measurement

Modeling of Li-ion cells is used in battery management systems (BMS) to determine key states such as state-of-charge (SoC), state-of-health (SoH), etc. Accurate models are also useful in developing a cell-level digital-twin that can be used for protection and diagnostics in the BMS. In this paper, a low-complexity model development is proposed based on the equivalent circuit model (ECM) of the Li-ion cells. The proposed approach uses online impedance measurement at discrete frequencies to derive the ECM that matches closely with the results from the electro-impedance spectroscopy (EIS). The proposed method is suitable to be implemented in a microcontroller with low-computational power, typically used in BMS. Practical design guidelines are proposed to ensure fast and accurate model development. Using the proposed method to enhance the functions of a typical automotive BMS is described. Experimental validation is performed using large prismatic cells and small-capacity cylindrical cells. Root-mean-square error (RMSE) of less than 3\% is observed for a wide variation of operating conditions.

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

IEEE Transactions on Industrial Informatics

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Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

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Small-Sample-Learning-Based Lithium-Ion Batteries Health Assessment: An Optimized Ensemble Framework

Machine Learning is widely studied in battery state of health (SOH) estimation due to its advantage in establishing the non-linear mapping between measurements and SOH. However, the requirement of a big dataset and the lack of robustness limit the practical application, especially in small sample learning. To tackle these challenges, an optimal ensemble framework called BaggELM (bagging extreme learning machine) is proposed for battery SOH estimation. Specifically, the required dataset is reduced by optimizing the input voltage and the hyperparameters of the BaggELM algorithm. Moreover, a statistical post-processing method is used to aggregate multiple ELMs, and the final estimate is determined by the maximum probability density value. As a result, the effects of random parameterization of ELM and the training data size on SOH estimation are suppressed, thus improving the robustness and accuracy of …

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

Intelligent Cell Balancing Control for Lithium-Ion Battery Packs

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Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

Grid Impedance Shaping for Grid-Forming Inverters: A Soft Actor-Critic Deep Reinforcement Learning Algorithm

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IEEE Transactions on Industrial Informatics

Transferable Deep Slow Feature Network With Target Feature Attention for Few-Shot Time-Series Prediction

Data-driven methods for predicting quality variables in wastewater treatment processes (WWTPs) have mostly ignored the slow time-varying nature of WWTP, and they are data-consuming that need a large amount of independent and homogeneously distributed data, which makes it difficult to collect. To address this issue with few-shot and inconsistent distribution, a transfer learning method called transferable deep slow feature network (TDSFN) for time-series prediction is proposed by leveraging the knowledge of relevant datasets. TDSFN extracts nonlinear slow features of WWTP with inertia from the time series through a deep slow feature network and constructs the domain invariant features based on them. Target feature attention is designed in TDSFN to enhance the predictor adaptability to the target domain by assigning weights to the source features based on their similarity to target features. Furthermore, a …

Alejandro Castillo Atoche

Alejandro Castillo Atoche

Universidad Autónoma de Yucatán

IEEE Transactions on Industrial Informatics

Adhesion Testing System Based on Convolutional Neural Networks for Quality Inspection of Flexible Strain Sensors

Manufacturing reliable strain sensors based on nanostructured materials faces several challenges, such as ensuring quality inspection of the adhesion between the active sensor and its substrate. In order to overcome this, the use of a deep learning-based technique is proposed herein to determine the in-situ adhesion strength. This study conducts an adhesion strength analysis between carbon nanotubes (CNTs) over a polymeric substrate (as components of a strain sensor), using image analysis. In line with the edge-computing paradigm, a novel inspection system is presented. An embedded processor equipped with a convolutional neural network architecture is used to inspect the CNT adhesion deposited on a substrate surface using deep learning semantic segmentation. This determines the relative concentration of CNTs covering the peeled area and its spatial probabilistic distribution map. Experimental …

Soumya Ranjan Mohanty

Soumya Ranjan Mohanty

Indian Institute of Technology, BHU

IEEE Transactions on Industrial Informatics

Intelligent Fault Detection and Classification for an Unbalanced Network With Inverter-Based DG Units

In this article, a machine-learning-based fault detection and classification method is proposed. Two supervised learning-based protection modules are developed for the relays considered in the study—one to detect the fault and discriminate between the symmetrical or unsymmetrical nature of the fault and another to detect the faulty phase(s). A robust set of features using both relay voltage and current signals is utilized for developing the modules. The features are obtained using the multiresolution decomposition based on the empirical wavelet transform. The modules are tested on the unbalanced IEEE 13-node network integrated with inverter-based distributed generation systems capable of reactive power injection in the low-voltage ride-through mode of operation. Varying penetration levels and intermittent output of distributed generation, fault resistance, fault inception time, noise in the signals, and switching …

Yan Wang 王龑

Yan Wang 王龑

Fudan University

IEEE Transactions on Industrial Informatics

MGR3Net: Multigranularity Region Relation Representation Network for Facial Expression Recognition in Affective Robots

Automatic facial expression recognition (FER) based on face images is essential for affective robots, which are designed for interactive companions and intelligent healthcare. Although existing DL-based FERs have made significant progress, an accurate FER model in robots is challenging due to the subtle differences in facial expressions across various scenarios. To address this issue, we propose a multigranularity region relation representation network (MGR 3 Net) to improve the robustness and generalization of FER via attention-guided global-local fusion. The MGR 3 Net is composed of three modules: multigranularity attention (MGA), holistic-regional feature extractor (HRFE), and hybrid feature fusion. In the MGA module, we first process each holistic cropped face image into three granularity of face regions from coarse to fine, which are four region-cropped faces, face partitions, and face partitions …

Hongli Dong

Hongli Dong

Universität Duisburg-Essen

IEEE Transactions on Industrial Informatics

Quantized Distributed Economic Dispatch for Microgrids: Paillier Encryption–Decryption Scheme

This article is concerned with the secure distributed economic dispatch (DED) problem of microgrids. A quantized distributed optimization algorithm using the Paillier encryption–decryption scheme is developed. This algorithm is designed to optimally coordinate the power outputs of a collection of distributed generators (DGs) in order to meet the total load demand at the lowest generation cost under the DG capacity limits while ensuring communication efficiency and security. First, to facilitate data encryption and reduce data release, a novel dynamic quantization scheme is integrated into the DED algorithm, through which the effects of quantization errors can be eliminated. Next, utilizing matrix norm analysis and mathematical induction, a sufficient condition is provided to demonstrate that the developed DED algorithm converges precisely to the optimal solution under finite quantization levels (and even the three …

Hongseok Kim

Hongseok Kim

Sogang University

IEEE Transactions on Industrial Informatics

FedAND: Federated Learning Exploiting Consensus ADMM by Nulling Drift

In this article, we propose FedAND, a unified federated learning optimization algorithm, to tackle client drift and server drift issues under partial client participation. Federated learning is gaining popularity due to privacy concerns and mobile computing, but it still faces challenges due to heterogeneous and distributed data. FedAND leverages consensus alternating direction method of multipliers (ADMM) and resolves the server drift caused by the server state in the global update. Under partial participation, we prove that FedAND preserves the strong convergence properties of ADMM while suppressing the server drift, which in turn reduces the client drift and thus achieves better convergence. Our empirical results demonstrate superior performance compared to other methods such as FedProx, FedADMM, FedPD, and FedDyn in diverse scenarios of statistical and system heterogeneity under partial client participation.

Jun Yang

Jun Yang

Loughborough University

IEEE Transactions on Industrial Informatics

Multistep dual control for exploration and exploitation in autonomous search with convergence guarantee

Inspired by the concept of recently proposed dual control for exploration and exploitation, this article presents a multistep dual control for exploration and exploitation with guaranteed convergence in search for autonomous sources. To deal with an unknown source position and environment, the proposed dual control algorithm faces significant challenges in demonstrating its recursive feasibility and convergence. With the help of the properties of Bayesian estimators, we redesign a multistep dual control for exploitation and exploration algorithm with necessary terminal ingredients and show that the recursive feasibility and the convergence of the modified dual control algorithm are guaranteed. Two simulation scenarios are conducted, which demonstrate that the proposed algorithm outperforms the stochastic model-predictive control approach and the informative path planning approach in terms of searching …