Reliable Operation Research Articles

An Investigation into the Viability of Cell-Level Temperature Control in Lithium-Ion Battery Packs

Abstract This article focuses on the thermal management and temperature balancing of lithium-ion battery packs. As society transitions to relying more heavily on renewable energy, the need for energy storage rises considerably, as storage facilitates power regulation between these sources and the grid. Lithium-ion batteries are leading the market for energy storage options, but their properties are temperature sensitive, with thermal abuse resulting in shortened pack lifetime and possible safety issues. Current battery thermal management systems (BTMS) are implemented in a number of ways to ensure consistent and reliable operation. However, they are typically limited in architecture and restricted in ability to attend to temperature gradients. This work proposes a BTMS topology that permits control of the individual cooling received by a cell in a pack. First, an analysis is done using timescale separation to confirm that cell-level temperature control is capable of extending the lifetime of a pack as compared to pack-level control. The analysis is used to guide the gain tuning of a state feedback controller, which directs more cooling effort to cells of higher temperatures. Validation of the BTMS topology and control is performed through the simulation of a battery pack, with variations in total cooling power and resistance heterogeneity. The outcome of the validation studies indicates that the proposed BTMS configuration is better equipped to reduce temperature differences and extend pack life. This benefit increases as total input power increases, giving the controller more freedom to cool unhealthy cells while remaining within power constraints.

A dual-stream temporal convolutional network for remaining useful life prediction of rolling bearings

Abstract Remaining useful life (RUL) prediction plays an indispensable role in the reliable operation and improved maintenance of rolling bearings. Currently, data-driven methods based on deep learning have made significant progress in RUL prediction. However, most of such methods only consider the correlation between channels, ignoring the importance of different time steps for RUL prediction. In addition, it is still challenging to effectively fuse the degradation features of rolling bearings to improve the model RUL prediction performance. To address the above issues, this paper proposes a novel data-driven RUL prediction method named dual-stream temporal convolution network (DSTCN). First, a hybrid attention temporal convolution block (HATCB) is designed to capture the correlation of degraded features on the channel dimension and temporal dimension. Second, a one-dimensional attention fusion module is designed. This module is capable of weight recalibration and assignment to adaptively fuse different degraded features. Afterward, the Hilbert Marginal spectrum is obtained using the Hilbert Huang Transform and used as the input to one stream. Meanwhile, vibration signals are used as the input of the other stream, thus building a dual-stream temporal convolution network to realize RUL prediction. The effectiveness of the proposed method is validated with two life-cycle datasets, and the results show that the method has lower prediction error than other methods for RUL prediction and prognostic analysis.

Research on optimal configuration of mobile energy storage in distribution networks considering various energy utilization efficiencies

The increasing integration of renewable energy sources such as wind and solar into the distribution grid introduces new complexities and instabilities to traditional electrical grids. This study tackles these challenges by optimizing the configurations of Modular Mobile Battery Energy Storage (MMBES) in urban distribution grids, particularly focusing on capacity-limited areas. Our method investigates five core attributes of energy storage configurations and develops a model capable of adapting to the uncertainties presented by extreme scenarios. This approach not only enhances the adaptability of energy storage systems but also equips decision-makers with proactive and flexible tools for decision-making in complex environments. Empirical evidence from the study shows that modular mobile energy storage significantly improves distribution grid performance by effectively managing the challenges posed by renewable integration. Furthermore, the research confirms that optimizing decision-makers’ cognitive parameters to align with subjective preferences ensures economic viability and enhances grid resilience. This study offers a new perspective and methodology for configuring energy storage, contributing to more flexible and reliable grid operations amidst widespread renewable integration.

Remaining useful life prediction method for jointless track circuits based on multivariate feature fusion and nonlinear Wiener process

Abstract Predicting the Remaining Useful Life (RUL) of track circuits is essential to ensure the safe and reliable operation of high-speed railways. In response to the challenges faced by current machine-learning-based RUL prediction methods, which struggle to represent the uncertainty in the probability distribution of RUL predictions, this paper suggests a hybrid-driven method for estimating remaining life. Firstly, the track circuit Health Index (HI) is constructed by feature dimensionality reduction and fusion of the original multivariate monitoring data through Kernel Principal Component Analysis (KPCA) and Autoencoder (AE); Secondly, the degraded state of the rail circuit is modelled using a nonlinear Wiener degradation model. Finally, the principle of First Hitting Time (FHT) is used to derive the Probability Density Function (PDF) of the anticipated RUL. The efficacy and superiority of the approach presented in this paper are validated by experimental research on the track circuit monitoring dataset. The method enhances forecast accuracy and reduces prediction uncertainty, offering robust technical support for track circuit maintenance decision-making.

An Overview of Systems-Theoretic Guarantees in Data-Driven Model Predictive Control

The development of control methods based on data has seen a surge of interest in recent years. When applying data-driven controllers in real-world applications, providing theoretical guarantees for the closed-loop system is of crucial importance to ensure reliable operation. In this review, we provide an overview of data-driven model predictive control (MPC) methods for controlling unknown systems with guarantees on systems-theoretic properties such as stability, robustness, and constraint satisfaction. The considered approaches rely on the fundamental lemma from behavioral theory in order to predict input–output trajectories directly from data. We cover various setups, ranging from linear systems and noise-free data to more realistic formulations with noise and nonlinearities, and we provide an overview of different techniques to ensure guarantees for the closed-loop system. Moreover, we discuss avenues for future research that may further improve the theoretical understanding and practical applicability of data-driven MPC.

Optimal execution of logical Hadamard with low‐space overhead in rotated surface code

AbstractFault‐tolerant quantum computation requires error‐correcting codes that enable reliable universal quantum operations. This study introduces a novel approach that executes the logical Hadamard with low‐space requirements while preserving the original definition of logical operators within the framework of the rotated surface codes. Our method leverages a boundary deformation method to rotate the logical qubit transformed by transversal Hadamard. Following this, the original encoding of the logical qubit is reinstated through logical flip‐and‐shift operations. The estimated space–time cost for a logical Hadamard operation with a code distance d is 5d2 + 3d2. The efficiency enhancement of the proposed method is approximately four times greater than those of previous approaches, regardless of the code distance. Unlike the traditional method, implementing a logical Hadamard requires only two patches instead of seven. Furthermore, the proposed method ensures the parallelism of quantum circuits by preventing interferences between adjacent logical data qubits.

A fusion algorithm of multidimensional element space mapping architecture for SOC estimation of lithium-ion batteries under dynamic operating conditions

Accurate estimation of the state of charge (SOC) is pivotal for ensuring the reliable operation of lithium-ion batteries in electric vehicles, particularly under complex real-world conditions. This paper introduces a novel SOC estimation method named Multidimensional Elemental Space Mapping Architecture (MESMA), which integrates the battery equivalent circuit model with a data-driven approach to tackle adaptability under intricate conditions and high-dimensional input data correlation. Initially, a least squares method incorporating a forgetting factor is employed to identify the battery circuit model parameters and analyze their correlation with SOC. Subsequently, a feature fusion algorithm is introduced to discern spatial variables highly correlated with SOC by leveraging both original voltage and current data and circuit model parameters as inputs to a Convolutional Neural Network (CNN) model, and an eight-input and one-output spatially mapped feature CNN model is established. Finally, the XGBoost model is harnessed for battery SOC estimation. The effectiveness and adaptability of MESMA are validated using experimental data from both public single condition and mixed condition datasets, including FUDS, Mixed-1, etc. The results unequivocally demonstrate that MESMA accurately estimates the SOC of lithium-ion batteries under varying operating conditions. Furthermore, it exhibits remarkable adaptability and stability across different temperatures, effectively capturing SOC trends.

Data-driven adaptive Lyapunov function based graphical deep convolutional neural network for smart grid congestion management

Optimal power flow by leveraging network grid topology will ensure stable operation of the smart grid. Energy management in grid-connected systems aimed to reduce computational non-linearities and ensure reliable operation of the smart grid. The conventional method manages congestion with optimal scheduling for every 10–15 min. Hence congestion in the smart grid occurs during secured energy distribution. In smart grid, instant congestion and energy management are needed. This research, work is devoted to a novel data-driven adaptive Lyapunov function with a Graphical Deep Convolutional Neural Network (GDCNN) regulated optimal flow by accurate energy management. By employing novel Graph theory-based network, the congestion data are obtained to train the proposed GDCNN. A Comparison of obtained results with existing baseline methods has been carried for claiming the novelties of proposed GDCNN. It is observed, that compared to existing machine learning-based extended subspace identification techniques. Our method has better optimal power regulation within 1.8 s by controlling power sources. Also, numerical simulation on IEEE 68 bus system shows the proposed GDCNN have superior performance, reliability, and optimal energy management. This by integrating the benefits of adaptive Lyapunov function and graphical convolutional network.

The Research of Intra-Pulse Modulated Signal Recognition of Radar Emitter under Few-Shot Learning Condition Based on Multimodal Fusion

Radar radiation source recognition is critical for the reliable operation of radar communication systems. However, in increasingly complex electromagnetic environments, traditional identification methods face significant limitations. These methods often struggle with high noise levels and diverse modulation types, making it difficult to maintain accuracy, especially when the Signal-to-Noise Ratio (SNR) is low or the available training data are limited. These difficulties are further intensified by the necessity to generalize in environments characterized by a substantial quantity of noisy, low-quality signal samples while being constrained by a limited number of desirable high-quality training samples. To more effectively address these issues, this paper proposes a novel approach utilizing Model-Agnostic Meta-Learning (MAML) to enhance model adaptability in few-shot learning scenarios, allowing the model to quickly learn with limited data and optimize parameters effectively. Furthermore, a multimodal fusion neural network, DCFANet, is designed, incorporating residual blocks, squeeze and excitation blocks, and a multi-scale CNN, to fuse I/Q waveform data and time–frequency image data for more comprehensive feature extraction. Our model enables more robust signal recognition, even when the signal quality is severely degraded by noise or when only a few examples of a signal type are available. Testing on 13 intra-pulse modulated signals in an Additive White Gaussian Noise (AWGN) environment across SNRs ranging from −20 to 10 dB demonstrated the approach’s effectiveness. Particularly, under a 5−way5−shot setting, the model achieves high classification accuracy even at −10dB SNR. Our research underscores the model’s ability to address the key challenges of radar emitter signal recognition in low-SNR and data-scarce conditions, demonstrating its strong adaptability and effectiveness in complex, real-world electromagnetic environments.

High performance Calcium Copper Titanate/Polyimide dielectrics composites enabled through colloidal stabilization

AbstractTransition to electrified transportation demands advanced power electronic components with the capability to improve range, reliability, and cost of ownership to accelerate mass market adoption. Thus, improvement in the current designs, manufacturing processes, materials, and components capable of providing reliable and efficient operation at higher temperatures play important roles in meeting future needs. To address these challenges, this article focuses on novel dielectric materials designed to reduce the volume of the most important and bulky component of a power electronic system, a capacitor. A new composite dielectric material was developed by integrating the positive attributes of both polymer and ceramic capacitors to overcome the challenges of state‐of‐the‐art dielectric materials. The developed composite properties have been evaluated and showed promising results, achieving a dielectric constant of 250 at 100 Hz, 25°C, unseen in current literature. Additionally, these materials can function at high temperatures (>150°C) with good breakdown strength, providing promising working conditions for capacitors, especially in electric vehicle applications.Highlights Dielectric composites were synthesized from calcium copper titanate and polyimide. A dispersive agent showed promise in achieving a homogenous, stable mixture. Composites were prepared using tape casting. A dielectric constant of 250 was achieved at 100 Hz, 25°C. At elevated temperature, the dielectric constant increased to over 700%.

Environmental and Welfare Effects of Large-Scale Integration of Renewables in the Electricity Sector

AbstractThe 2022 energy crisis highlighted the dependence of the Europe electricity sector on imported natural gas and the need to accelerate the adoption of renewables to the power system. However, operating a reliable power system with high share of renewables might require curtailing some renewables and activating conventional generators not scheduled in the day-ahead markets to ensure system reliability. These actions can result in environmental impacts, higher system costs and welfare impacts for customers. We use a novel high-granularity data from the Spanish power system for the period 2019–2022 to estimate the effects of these actions and forecast future impact of implementing ambitious targets of the European Gas Reduction Plan. We show that reliance on conventional generators will sharply increase with the addition of renewables. However, higher electricity consumption reduces the negative welfare impacts of integrating renewables. Until renewables and storage technologies advance further, conventional generators are needed for reliable operation of the systems.

Research on Intelligent Disinfection Robot Based on STC89C52 Microcontroller Control System

An intelligent disinfection robot based on STC89C52 microcontroller as the control system is proposed to address the current problems of high cost, scene limitations, and complex control factors. With the assistance of software development, the peripheral circuit of the chip is designed, and the robot can dynamically adjust its path according to its own and surrounding companions' position and motion status. The research results indicate that the various modules of the system can work in coordination, with high accuracy in tracking routes, precise image recognition, timely obstacle avoidance, fast response speed, and safe and reliable system operation. The disinfection robot carried out disinfection and sterilization test on the most common staphylococcus aureus and mold, and the test results showed that the pathogenic bacteria decreased significantly after 2 min, and all bacteria were removed after 3 min, and the killing rate of bacteria was up to>96.0%. This method improves the obstacle avoidance ability and collaborative efficiency of robot systems in complex environments, and can effectively respond to various unexpected situations.

A Hybrid Prediction Model for Short-Term Load Forecasting in Power Systems

Short-term load forecasting (STLF) plays a vital role in effective power system management by assisting power dispatch centers in developing generation plans and ensuring smooth system operation. This study introduces a novel hybrid prediction model called iSSA-LSSVM to tackle the STLF challenge. By integrating the Salp Swarm Algorithm (SSA) with Least Squares Support Vector Machines (LSSVM), the iSSA-LSSVM model significantly improves LSSVM's prediction accuracy. One of the key contributions is the model's ability to autonomously ne-tune LSSVM hyperparameters, eliminating the need for manual adjustments and optimizing performance. Modifying the SSA within iSSA-LSSVM enhances the original algorithm's exploration and exploitation capabilities, ensuring better search efficiency and precision. Using a dataset with four independent variables as input and electrical power output as the target variable, the model demonstrates superior predictive performance. Comparative analysis with three other models shows that iSSA-LSSVM achieves a lower Mean Square Error (MSE) and faster convergence. This improvement in accuracy and efficiency enhances STLF, allowing power dispatch centers to develop more precise generation plans and ensure more reliable power system operation.

A multi-rate sensor fusion and multi-task learning network for concurrent fault diagnosis of hydraulic systems

Hydraulic systems are widely used in key modern industrial fields such as mechanical manufacturing, aerospace, and heavy machinery, and their efficient and reliable operation is crucial to ensuring production safety and efficiency. However, hydraulic systems often experience concurrent faults, such as pump failures, valve blockages, pipeline leaks, and fluid contamination, which pose significant challenges to the fault diagnosis in hydraulic systems. This paper introduces a multi-task learning network that deconstructs the challenge of concurrent fault diagnosis into specific sub-tasks, enabling the simultaneous identification and classification of multiple hydraulic components' faults. Automatic channel filtering is designed to screen out sensitive channels of each component from multi-rate sensors. A dual-flow model is used to feature extraction, which can simultaneously extract the local spatial features and global semantic information. Then, four classification models are designed to identify the extracted shared features. An uncertainty weight loss is also proposed to balance the loss of different tasks. The experimental results show that our model significantly outperforms traditional methods and other popular multi-output methods in diagnosing concurrent faults.

Modeling of Induction Motor Direct Starting with and without Considering Current Displacement in Slot

This article presents a mathematical model of three-phase induction motor (IM) with a squirrel cage rotor and investigates its starting modes. Specifically, two scenarios are considered: direct starting of an IM and direct starting considering the current displacement effect in the rotor slots. Analyzing the starting modes of an IM without the use of automatic control systems is crucial for ensuring reliable, efficient, and safe operation of equipment across various industrial and commercial sectors. Understanding and accounting for the processes occurring during the starting mode of an IM allows for minimizing risks, enhancing energy efficiency, and reducing operational costs. This article details the mathematical modeling methods used for analyzing these starting modes and the results obtained from the modeling. These results were compared with data obtained experimentally, allowing for the assessment of the accuracy and reliability of the proposed model. The conducted research highlights the importance of considering current displacement in the rotor slots for accurate modeling and analysis of induction motor starting modes, particularly in capturing the differences in the amplitudes of the starting current and the faster transition to steady-state operation. Conclusions drawn from the comparison of modeling and experimental data provide valuable insights for the further development of control and operation methods for induction motors.

Graph neural network-based anomaly detection for human cyber physical systems

ABSTRACT Human Cyber Physical Systems (HCPS) encompass humans, networks, and physical devices, characterized by intricate interactions and interdependencies. The complexity of HCPS networks makes them vulnerable to network issues and cyberattacks, potentially resulting in network paralysis and significantly impacting the reliable operation of HCPS. Therefore, precise anomaly detection is imperative for HCPS. To tackle this issue, we integrate HCPS with the Software Defined Networking (SDN) infrastructure layer and propose an anomaly detection strategy based on Graph Neural Networks (GNN). Our approach utilizes Graph Sample and Aggregates (GraphSAGE) to capture adjacent feature information of nodes, revealing hidden relationships within the graph. These embeddings are then combined with Graph Attention Networks (GAT), which calculate attention coefficients between a node and its neighboring nodes, representing the importance of neighboring nodes to the central node. Finally, a fully connected layer is employed to obtain anomaly node labels. To assess the performance of our method, we conduct experiments using real datasets and compare them with other advanced anomaly detection methods in terms of accuracy, precision, recall, F1 score, and area under the ROC curve (AUC). Our method accurately detects anomalous nodes, offering a viable solution for practical applications.

Evaluation of Advances in Battery Health Prediction for Electric Vehicles from Traditional Linear Filters to Latest Machine Learning Approaches

In recent years, there has been growing interest in Li-ion battery State-of-Health (SOH) estimation due to its critical role in ensuring the safe and reliable operation of Electric Vehicles (EVs). Effective energy management and accurate SOH prediction are essential for the reliability and sustainability of EVs. This paper presents an in-depth review of SOH estimation techniques, starting with an overview of seminal methods that lay the theoretical groundwork for battery modeling and SOH prediction. The review then evaluates recent advancements in Machine Learning (ML) and Artificial Intelligence (AI) techniques, emphasizing their contributions to improving SOH estimation. Through a rigorous screening process, the paper systematically assesses the evolution of these advanced methods, addressing specific research questions to evaluate their effectiveness and practical implications. Key findings highlight the potential of hybrid models that integrate Equivalent Circuit Models (ECMs) with Deep Learning approaches, offering enhanced accuracy and real-time performance. Additionally, the paper discusses limitations of current methods, such as challenges in translating laboratory-based models to real-world conditions and the computational complexity of some prospective methods. In conclusion, this paper identifies promising future research directions aimed at optimizing hybrid models and overcoming existing constraints to advance SOH estimation and battery management in Electric Vehicles.

Complex signals parameters optimization on the base of linear approximations using the gradient method and Newton’s method

The article examines the effectiveness of the gradient descent and Newton methods for optimizing the parameters of ensembles of complex signals. Algorithms have been developed and implemented that increase the accuracy of setting parameters and ensure reasonable optimization of spectral, temporal and statistical characteristics of signals. The effectiveness of the application of the methods was confirmed experimentally on the example of reducing the error and increasing the level of immunity. The obtained results substantiate the improvement of the parameters of complex signals, which proves the efficiency of use for wireless telecommunication systems in order to ensure stable and reliable operation in conditions of dynamic changes in the environment and a high level of interference.The article compares the mathematical methods of optimization, namely the gradient method and Newton's method, proposes mathematical models and constructs algorithms that empirically prove the effectiveness of the application of the studied mathematical methods in the specified scientific area - for optimizing the parameters of ensembles of complex signals. Scientific works [1-6, 9, 12] present algorithms based on the gradient method and Newton's method, but they do not consider in detail the comparative analysis of the effectiveness of these methods for optimizing the parameters of ensembles of complex signals for implementation in various scientific and practical tasks. The effectiveness of the algorithms proposed in the article was confirmed experimentally, which made it possible to reduce the error and improve the characteristics of ensembles of complex signals.As a result of the experiments using the methods of gradient descent and Newton, a significant reduction of the error and an improvement of the stability of the signals were achieved. Newton's method reduced the error from 0.1 to 0.0027, justifying the high accuracy of setting the signal parameters. The gradient descent method provided a stable reduction of the gradient norm from 12.75 to less than 1.23, effectively reducing the interference level, i.e. increasing the interference immunity.

Cathode-supported SOFCs enabling redox cycling and coking recovery in hydrocarbon fuel utilization

Although cathode-supported solid oxide fuel cells (SOFCs), characterized by significantly thinner anode layers than anode-supported SOFCs, are known to have unique advantages in ensuring stability and reliability against redox operating conditions and carbon deposition, they have received relatively less attention due to their lower performance. Furthermore, there is a lack of in-depth theoretical and experimental studies on their outstanding redox cycle characteristics than the anode-supported cells. Here, applying a sintering aid to the electrolyte considerably reduces the sintering temperature of the electrolyte, with the shrinkage and microstructure of the cathode effectively controlled to lower the diffusion resistance. Consequently, the operating temperature was lowered by more than 100 °C compared to previously reported cathode-supported SOFCs; moreover, at 650 °C, a maximum power density more than twice that reported in previous reports. The redox stability of the cathode-supported cells was demonstrated experimentally and theoretically through a redox cycling test and a 3-D finite element method-based SOFC structure model. Moreover, the outstanding redox cycle property of cathode-supported cells has been experimentally demonstrated for the first time to have the potential to mitigate carbon deposition that occurs when utilizing methane fuel. Hence, this study presents new insights for achieving stable and reliable SOFC operation.

Remaining useful life prediction of nuclear reactor control rod drive mechanism based on dynamic temporal convolutional network

The control rod drive mechanism (CRDM) is a critical equipment of the nuclear reactor, and the prediction of its remaining useful life (RUL) is important for the efficient maintenance and ensuring the safe, reliable operation of nuclear power plants. In this paper, a novel framework for the RUL prediction of CRDM is proposed, which is a dynamic temporal convolution network (DTCN) based on dynamic activation function and attention mechanism. Firstly, the temporal convolution network (TCN) is used as the backbone of the prediction model, to extract the temporal dependence of the input data. Next, the dynamic activation function DReLU is integrated into the TCN, which can dynamically activate features and capture variable degradation information. Then, introducing the attention mechanism improves the influence of important high-level features extracted by the network on RUL prediction, thereby improving the efficiency of feature extraction in the network. Finally, the DTCN outputs the predicted RUL by performing non-linear mapping on the extracted features. The CRDM accelerated life test platform is established and a series of experiments are conducted using the collected CRDM full-life vibration dataset. The results demonstrated the performance and advantages of the proposed method.