# Improved LightGBM-based framework for electric vehicle lithium-ion battery remaining useful life prediction using multi health indicators

Symmetry

Published On 2022/8/1

To improve the prediction accuracy and prediction speed of battery remaining useful life (RUL), this paper proposes an improved light gradient boosting machine (LightGBM)-based framework. Firstly, the features from the electrochemical impedance spectroscopy (EIS) and incremental capacity-differential voltage (IC-DV) curve are extracted, and the open circuit voltage and temperature are measured; then, those are regarded as multi HIs to improve the prediction accuracy. Secondly, to adaptively adjust to multi HIs and improve prediction speed, the loss function of the LightGBM model is improved by the adaptive loss. The adaptive loss is utilized to adjust the loss function form and limit the saturation value for the first-order derivative of the loss function so that the improved LightGBM can achieve an adaptive adjustment to multiple HIs (ohmic resistance, charge transfer resistance, solid electrolyte interface (SEI) film resistance, Warburg resistance, loss of conductivity, loss of active material, loss of lithium ion, isobaric voltage drop time, and surface average temperature) and limit the impact of error on the gradient. The model parameters are optimized by the hyperparameter optimization method, which can avoid the lower training efficiency caused by manual parameter adjustment and obtain the optimal prediction performance. Finally, the proposed framework is validated by the database from the battery aging and performance testing experimental system. Compared with traditional prediction methods, GBDT (1.893%, 4.324 s), 1D-CNN (1.308%, 47.381 s), SVR (1.510%, 80.333 s), RF (1.476%, 852.075 s), and XGBoost (1.119%, 24.912 s), the …

Journal

Symmetry

Published On

2022/8/1

Volume

14

Issue

8

Page

1584

## Authors

#### 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

#### Hongjie Jia

##### Tianjin University

Position

Professor of Electrical Engineering

H-Index(all)

62

H-Index(since 2020)

52

I-10 Index(all)

0

I-10 Index(since 2020)

0

Citation(all)

0

Citation(since 2020)

0

Cited By

0

Research Interests

Power System

Smart Grid

Integrated Energy Systems

University Profile Page

#### Yunfei Mu

##### Tianjin University

Position

School of Electrical Engineering&Automation

H-Index(all)

39

H-Index(since 2020)

38

I-10 Index(all)

0

I-10 Index(since 2020)

0

Citation(all)

0

Citation(since 2020)

0

Cited By

0

Research Interests

Power system stability and control

New energy application

Electric vehicle

University Profile Page

#### Qian Xiao

##### Tianjin University

Position

Assitant Professor

H-Index(all)

15

H-Index(since 2020)

15

I-10 Index(all)

0

I-10 Index(since 2020)

0

Citation(all)

0

Citation(since 2020)

0

Cited By

0

Research Interests

Microgrids

DC Distribution Network

Multilevel Converters

BESS

Energy Router

University Profile Page

#### Yu Jin

##### Harbin Institute of Technology

Position

H-Index(all)

10

H-Index(since 2020)

10

I-10 Index(all)

0

I-10 Index(since 2020)

0

Citation(all)

0

Citation(since 2020)

0

Cited By

0

Research Interests

Multilevel converters

Battery energy storage system

FACTs

University Profile Page

### Other Articles from authors

Hongjie Jia

Tianjin University

Applied Energy

##### Bi-level optimal operations for grid operator and low-carbon building prosumers with peer-to-peer energy sharing

Low-carbon buildings (LCBs) are normally equipped with distributed energy resources (DERs) such as photovoltaic (PV) panels and batteries, thereby creating LCB prosumers that both produce and consume energy. Peer-to-peer (P2P) energy sharing among LCB prosumers could bring higher economic benefits for themselves, and facilitate better local power balance for the power grid. To fully harness the benefits of P2P energy sharing for both LCB prosumers and the power grid, a bi-level optimization method for LCB prosumers and the power grid operator is proposed in this paper. The grid operator at the upper level imposes the optimal time-varying network charge to LCB prosumers at the lower level to maximize its profit. And LCB prosumers with the objective of minimizing their costs adjust the schedules including P2P energy sharing and their heating loads to respond to the grid operator's optimal network …

*2024/4/1*

Hongjie Jia

Tianjin University

Applied Energy

##### Two-stage affine assessment method for flexible ramping capacity: An inverter heat pump virtual power plant case

The increasing penetration of renewable energy generation brings about variability and randomness, which poses challenges to the power systems due to a potential shortage of flexibility resources. Inverter heat pumps (IHPs) can be utilized to address this issue by providing flexible ramping capacity (FRC). However, unlike conventional generation that offers a fixed FRC, the FRC of individual IHPs and their aggregation are influenced by their operational constraints and the indoor temperature thresholds set by buildings. These thresholds, in turn, are affected by uncertainties in ambient temperature and solar irradiation. For the individual IHP, an IHP-FRC assessment model is established. This individual IHP-FRC assessment model is based on the building thermal dynamic model and user comfort model, incorporating uncertainties in ambient temperature and solar irradiation through affine representations. For the …

*2024/7/1*

Yunfei Mu

Tianjin University

Heliyon

##### Online lithium-ion battery intelligent perception for thermal fault detection and localization

—Equipping lithium-ion batteries with a reasonable thermal fault diagnosis can avoid thermal runaway and ensure the safe and reliable operation of the batteries. This research built a lithium-ion battery thermal fault diagnosis model that optimized the original mask region-based convolutional neural network based on the battery dataset in both parameters and structure. The model processes the thermal images of the battery surface, identifies problematic batteries, and locates the problematic regions. A backbone network is used to process the battery thermal images and extract feature information. Through the RPN network, the thermal feature is classified and regressed, and the Mask branch is used to ultimately determine the faulty battery's location. Additionally, we have optimized the original mask region-based convolutional neural network based on the battery dataset in both parameters and structure. The …

*2024/2/29*

Yunfei Mu

Tianjin University

Applied Energy

##### Optimal pricing of integrated community energy system for building prosumers with P2P multi-energy trading

Buildings are typically integrated with multiple distributed energy resources (DERs), enabling them to act as building prosumers engaged in both energy production and consumption. Peer-to-peer (P2P) energy trading among building prosumers is crucial to improve their benefits. However, further exploration is required to balance the benefits between building prosumers and the system operator (e.g., the integrated community energy system (ICES) operator) since they are different entities. In this context, this paper proposes a comprehensive network charge and energy sale pricing scheme for the ICES operator on heterogeneous building prosumers with P2P multi-energy trading. The interaction between the ICES operator and building prosumers is modelled as a bi-level optimization problem that belongs to the hierarchical structure, while considering the heterogeneity of thermal insulation performance of …

*2024/7/1*

Hongjie Jia

Tianjin University

Heliyon

##### Online lithium-ion battery intelligent perception for thermal fault detection and localization

—Equipping lithium-ion batteries with a reasonable thermal fault diagnosis can avoid thermal runaway and ensure the safe and reliable operation of the batteries. This research built a lithium-ion battery thermal fault diagnosis model that optimized the original mask region-based convolutional neural network based on the battery dataset in both parameters and structure. The model processes the thermal images of the battery surface, identifies problematic batteries, and locates the problematic regions. A backbone network is used to process the battery thermal images and extract feature information. Through the RPN network, the thermal feature is classified and regressed, and the Mask branch is used to ultimately determine the faulty battery's location. Additionally, we have optimized the original mask region-based convolutional neural network based on the battery dataset in both parameters and structure. The …

*2024/2/29*

Yu Jin

Harbin Institute of Technology

IET Energy Systems Integration

##### A simplified control parameter optimisation method of the hybrid modular multilevel converter in the medium‐voltage DC distribution network for improved stability under a weak …

To improve the stability of the hybrid modular multilevel converter (MMC), a simplified dominant mode‐based control parameter optimisation method of the hybrid MMC system is proposed. Firstly, in the medium‐voltage DC distribution network, the small‐signal model of the hybrid MMC is established. Secondly, the influence of a weak AC system on stability is analysed through eigenvalue analysis. Finally, a simplified objective function is designed for eigenvalues of the dominant mode by considering only real parts, and improved small‐signal stability can be achieved by control parameters optimisation. The proposed method optimises all control parameters at the same time, which further reduces the number of algorithm iterations. Simulation results show that by the proposed control parameter optimisation method, the hybrid MMC has better transient performance and reduced disturbance under SCR variation …

*2024*

Hongjie Jia

Tianjin University

Energy Reports

##### DPGS: Data-driven photovoltaic grid-connected system exploiting deep learning and two-stage single-phase inverter

The increasing demand for clean energy to address the looming energy crisis has led to the widespread use of photovoltaic grid-connected technology, particularly in microgrids. To fully harness solar energy, this study proposes a data-driven strategy for photovoltaic maximum power point tracking with adaptive adjustment to environmental dynamics. Exploiting deep learning and incremental adjustment, our data-driven photovoltaic-grid systems (DPGS) upgrade the traditional perturbation and observation (P&O) MPPT to a dynamic evolutionary scheme. DPGS gathers the photovoltaic panel's output voltage and current, calculates the current power, and then outputs the appropriate reference voltage based on the power difference. The photovoltaic voltage is then adjusted using a data-driven strategy. In this study, a double-hidden layer deep learning network is utilized to output the prediction control signal of the …

*2024/6/1*

Yunfei Mu

Tianjin University

Engineering

##### The Static Stability Region of an Integrated Electricity-Gas System Considering Voltage and Gas Pressure

In an integrated electricity-gas system (IEGS), load fluctuations affect not only the voltage in the power system but also the gas pressure in the natural gas system. The static voltage stability region (SVSR) method is a tool for analyzing the overall static voltage stability in a power system. However, in an IEGS, the SVSR boundary may be overly optimistic because the gas pressure may collapse before the voltage collapses. Thus, the SVSR method cannot be directly applied to an IEGS. In this paper, the concept of the SVSR is extended to the IEGS-static stability region (IEGS-SSR) while considering voltage and gas pressure. First, criteria for static gas pressure stability in a natural gas system are proposed, based on the static voltage stability criteria in a power system. Then, the IEGS-SSR is defined as a set of active power injections that satisfies multi-energy flow (MEF) equations and static voltage and gas pressure …

*2024/2/10*

Yunfei Mu

Tianjin University

Applied Energy

##### Two-stage affine assessment method for flexible ramping capacity: An inverter heat pump virtual power plant case

The increasing penetration of renewable energy generation brings about variability and randomness, which poses challenges to the power systems due to a potential shortage of flexibility resources. Inverter heat pumps (IHPs) can be utilized to address this issue by providing flexible ramping capacity (FRC). However, unlike conventional generation that offers a fixed FRC, the FRC of individual IHPs and their aggregation are influenced by their operational constraints and the indoor temperature thresholds set by buildings. These thresholds, in turn, are affected by uncertainties in ambient temperature and solar irradiation. For the individual IHP, an IHP-FRC assessment model is established. This individual IHP-FRC assessment model is based on the building thermal dynamic model and user comfort model, incorporating uncertainties in ambient temperature and solar irradiation through affine representations. For the …

*2024/7/1*

Hongjie Jia

Tianjin University

Engineering

##### The Static Stability Region of an Integrated Electricity-Gas System Considering Voltage and Gas Pressure

In an integrated electricity-gas system (IEGS), load fluctuations affect not only the voltage in the power system but also the gas pressure in the natural gas system. The static voltage stability region (SVSR) method is a tool for analyzing the overall static voltage stability in a power system. However, in an IEGS, the SVSR boundary may be overly optimistic because the gas pressure may collapse before the voltage collapses. Thus, the SVSR method cannot be directly applied to an IEGS. In this paper, the concept of the SVSR is extended to the IEGS-static stability region (IEGS-SSR) while considering voltage and gas pressure. First, criteria for static gas pressure stability in a natural gas system are proposed, based on the static voltage stability criteria in a power system. Then, the IEGS-SSR is defined as a set of active power injections that satisfies multi-energy flow (MEF) equations and static voltage and gas pressure …

*2024/2/10*

Hongjie Jia

Tianjin University

Energy Reports

##### Evolution prediction method for electric private car ownership considering the decision-making behaviour of consumers

A global consensus has been reached that promotes full electrification in the automotive field to solve environmental problems, and Electric Vehicles (EVs) have developed rapidly in recent years. China has gradually promoted electrification for taxis, trucks, and private cars. Due to the large number of electric private cars (EPCs), the evolutionary impact of EPC ownership on the charging demand and even the power grid at different stages cannot be ignored. But the evolution of EPC ownership is affected by many factors, and the prediction accuracy is low. Thus, an evolution prediction method of EPC ownership considering the decision-making behaviour of consumers is proposed. Firstly, considering the mutual influence of the individual mental state of consumers for EPC, the social network of consumers is generated based on the small-world network model. Secondly, to simulate the psychological state of …

*2024/6/1*

Yunfei Mu

Tianjin University

IET Energy Systems Integration

##### A simplified control parameter optimisation method of the hybrid modular multilevel converter in the medium‐voltage DC distribution network for improved stability under a weak …

To improve the stability of the hybrid modular multilevel converter (MMC), a simplified dominant mode‐based control parameter optimisation method of the hybrid MMC system is proposed. Firstly, in the medium‐voltage DC distribution network, the small‐signal model of the hybrid MMC is established. Secondly, the influence of a weak AC system on stability is analysed through eigenvalue analysis. Finally, a simplified objective function is designed for eigenvalues of the dominant mode by considering only real parts, and improved small‐signal stability can be achieved by control parameters optimisation. The proposed method optimises all control parameters at the same time, which further reduces the number of algorithm iterations. Simulation results show that by the proposed control parameter optimisation method, the hybrid MMC has better transient performance and reduced disturbance under SCR variation …

*2024*

Yunfei Mu

Tianjin University

Energy Reports

##### DPGS: Data-driven photovoltaic grid-connected system exploiting deep learning and two-stage single-phase inverter

The increasing demand for clean energy to address the looming energy crisis has led to the widespread use of photovoltaic grid-connected technology, particularly in microgrids. To fully harness solar energy, this study proposes a data-driven strategy for photovoltaic maximum power point tracking with adaptive adjustment to environmental dynamics. Exploiting deep learning and incremental adjustment, our data-driven photovoltaic-grid systems (DPGS) upgrade the traditional perturbation and observation (P&O) MPPT to a dynamic evolutionary scheme. DPGS gathers the photovoltaic panel's output voltage and current, calculates the current power, and then outputs the appropriate reference voltage based on the power difference. The photovoltaic voltage is then adjusted using a data-driven strategy. In this study, a double-hidden layer deep learning network is utilized to output the prediction control signal of the …

*2024/6/1*

Hongjie Jia

Tianjin University

IET Energy Systems Integration

##### A simplified control parameter optimisation method of the hybrid modular multilevel converter in the medium‐voltage DC distribution network for improved stability under a weak …

To improve the stability of the hybrid modular multilevel converter (MMC), a simplified dominant mode‐based control parameter optimisation method of the hybrid MMC system is proposed. Firstly, in the medium‐voltage DC distribution network, the small‐signal model of the hybrid MMC is established. Secondly, the influence of a weak AC system on stability is analysed through eigenvalue analysis. Finally, a simplified objective function is designed for eigenvalues of the dominant mode by considering only real parts, and improved small‐signal stability can be achieved by control parameters optimisation. The proposed method optimises all control parameters at the same time, which further reduces the number of algorithm iterations. Simulation results show that by the proposed control parameter optimisation method, the hybrid MMC has better transient performance and reduced disturbance under SCR variation …

*2024*

Hongjie Jia

Tianjin University

Energy and AI

##### CWGAN-GP with residual network model for lithium-ion battery thermal image data expansion with quantitative metrics

Lithium batteries find extensive applications in energy storage. Temperature is a crucial indicator for assessing the state of lithium-ion batteries, and numerous experiments require thermal images of lithium-ion batteries for research purposes. However, acquiring thermal imaging samples of lithium-ion battery faults is challenging due to factors such as high experimental costs and associated risks. To address this, our study proposes the utilization of a Conditional Wasserstein Generative Adversarial Network with Gradient Penalty and Residual Network (CWGAN-GP with Residual Network) to augment the dataset of thermal images depicting lithium-ion battery faults. We employ various evaluation metrics to quantitatively analyze and compare the generated thermal images of lithium-ion batteries. Subsequently, the expanded dataset, comprising four types of thermal images depicting lithium-ion battery faults, is input …

*2024/5/1*

Yunfei Mu

Tianjin University

Energy and AI

##### CWGAN-GP with residual network model for lithium-ion battery thermal image data expansion with quantitative metrics

Lithium batteries find extensive applications in energy storage. Temperature is a crucial indicator for assessing the state of lithium-ion batteries, and numerous experiments require thermal images of lithium-ion batteries for research purposes. However, acquiring thermal imaging samples of lithium-ion battery faults is challenging due to factors such as high experimental costs and associated risks. To address this, our study proposes the utilization of a Conditional Wasserstein Generative Adversarial Network with Gradient Penalty and Residual Network (CWGAN-GP with Residual Network) to augment the dataset of thermal images depicting lithium-ion battery faults. We employ various evaluation metrics to quantitatively analyze and compare the generated thermal images of lithium-ion batteries. Subsequently, the expanded dataset, comprising four types of thermal images depicting lithium-ion battery faults, is input …

*2024/5/1*

Yunfei Mu

Tianjin University

International Journal of Electrical Power & Energy Systems

##### A real time peer-to-peer energy trading for prosumers utilizing time-varying building virtual energy storage

With the increasing penetration of distributed energy resources (DER) in the electric power system, Peer-to-Peer (P2P) energy trading has become a promising paradigm for future electric power systems. Building thermal load, which is an important demand side resource, should be considered carefully in the design of a P2P trading method. In this paper, we investigate the application of building thermal energy storage capability in P2P energy trading. We aggregate and model building thermal loads as a virtual energy storage and derive a time-varying virtual energy storage system (T-VESS) model to quantify the flexibility of a building. Key parameters of T-VESS, including charging/discharging rate, energy capacity, and state of charge (SOC), are analyzed so that T-VESS can be embedded in the building prosumer model to participate effectively in electricity-oriented P2P energy trading. We propose a real time …

*2024/1/1*

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 …

*2024/4/15*

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 …

*2024/3/1*

Remus Teodorescu

Aalborg Universitet

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

The State of Health (SOH) estimation for automotive batteries is currently assessed with different techniques which may involve long testing procedure or require costly hardware to be implemented. This paper aims at contributing to this domain by exploiting the response of a lead-acid battery with respect to a short-term current profile using an Artificial Neural Network (ANN) classifier for SOH estimation. The method is applicable onboard the vehicle and no additional instrumentation is required on the retained vehicle. The design and validation of a SOH method with a short-term current profile using Artificial Intelligence (AI) in lead-acid batteries, which are commonly used in heavy-duty vehicles for cranking and cabin systems, are presented. The paper validates the considered approach with experimental data, which are representative of actual vehicle operations. In detail, the paper describes the retained …

*2022/8/14*

### Other articles from Symmetry journal

jerry dwyer

Texas Tech University

Symmetry

##### Dynamics of Iterations of the Newton Map of sin(z)

The dynamical systems of trigonometric functions are explored, with a focus on sz=sin(z) and the fractal image created by iterating the Newton map, Fs(z), of s(z). The basins of attraction created from iterating Fs(z) are analyzed, and some bounds are determined for the primary basins of attraction. We further prove x and y-axis symmetry of the Newton map as well as some interesting results on periodic points on the real axis.

*2024/1/30*

Prof. Dr. Rekha Srivastava (Maiden Name: Rekha Panda)

University of Victoria

Symmetry

##### A New Subclass of Analytic Functions Associated with the q-Derivative Operator Related to the Pascal Distribution Series

A new subclass TXq[λ,A,B] of analytic functions is introduced by making use of the q-derivative operator associated with the Pascal distribution. Certain properties of analytic functions in the subclass TXq[λ,A,B] are derived. Some known results are generalized.

*2024/2/28*

Mariia Nazarkevych

Lviv Polytechnic National University

Symmetry

##### Influence of the Symmetry Neural Network Morphology on the Mine Detection Metric

Presently, active detectors are widely used to detect mines, providing high accuracy. However, the principle of the operation of active detectors can lead to the explosion of hidden mines. The novelty of this work is the development of the morphology of a neural network for the classification of mines made of different materials (metallic, semi-metallic, plastic) with high accuracy (99.23%), based on a vector of input features with the following components: the value of the output voltage of the FLC-100 magnetic field sensor, which measures magnetic field anomalies in the vicinity of mines with an accuracy of 10−10–10−4 Tesla; six different soil types, depending on the humidity; and the height at which the magnetic field sensor is located above the mine. Due to the fact that mines, when made of different materials (metallic, semi-metallic, plastic), have different magnetic properties, the neural network method of mine classification, based on the sensor data regarding anomalies of the magnetic field in the vicinity of mines, allows the classification of mines made of different materials. The accuracy of mine classification was assessed with two-layer and three-layer neural networks on various metrics (confusion matrix, ROC curves, accuracy–loss curves), using ADAM, RMSprop, and SGD optimisers, and analyses and comparisons were then carried out. The impact of asymmetry in the neuron number and the types of activation functions in the first and second hidden layers on the values of the accuracy and loss metrics was studied. In particular, it was established that the asymmetry of the number of neurons in the first and second hidden layers relative to the …

*2024/4/17*

Wojciech Sitek

Politechnika Slaska

Symmetry

##### A Microstructural Study of Cu-10Al-7Ag Shape Memory Alloy in As-Cast and Quenched Conditions

Shape memory alloys (SMAs) represent an exceptional class of smart materials as they are able to recover their shape after mechanical deformation, making them suitable for use in actuators, sensors and smart devices. These unique properties are due to the thermoelastic martensitic transformation that can occur during both thermal and mechanical deformation. Cu-based SMAs, especially those incorporating Al and Ag, are attracting much attention due to their facile production and cost-effectiveness. Among them, Cu-Al-Ag SMAs stand out due to their notably high temperature range for martensitic transformation. In this study, a Cu-based SMA with a new ternary composition of Cu-10Al-7Ag wt.% was prepared by arc melting and the samples cut from this casting alloy were quenched in water. Subsequently, the phase composition and the development of the microstructure were investigated. In addition, the morphology of the martensite was studied using advanced techniques such as electron backscatter diffraction (EBSD) and transmission electron microscopy (TEM). The analyzes confirmed the presence of martensitic structures in both samples; mainly 18R (β1′) martensite was present but a small volume fraction of (γ1′) martensite also was noticed in the as-quenched sample. The observation of fine, twinned martensite plates in the SMA alloy with symmetrically occurring basal plane traces between the twin variants underlines the inherent correlation between microstructural symmetry and the properties of the material and provides valuable insights into its behavior. The hardness of the quenched sample was found to be lower than the as …

*2024/5*

Qingyuan Zhang

Beihang University

Symmetry

##### Cascading Failure Modeling for Circuit Systems Considering Continuous Degradation and Random Shocks Using an Impedance Network

The reliability of circuit systems is primarily affected by cascading failures due to their complex structural and functional coupling. Causes of cascading failure during circuit operation include the continuous degradation process of components and external random shocks. Circuit systems can exhibit asymmetric structural changes and functional loss during cascading failure propagation due to the coupling of degradation and shock and their uncertainty effects. To tackle this issue, this paper abstracts the circuit into an impedance network and constructs a component failure behavior model that considers the correlation between degradation and shock. The interactions between soft and hard failure processes among different components are discussed. Two types of cascading failure propagation processes are described: slow propagation associated with continuous degradation and damage shock, and fast propagation due to fatal shock. Based on this, a cascading failure simulation algorithm is developed. This article presents a case study to demonstrate the proposed models and to analyze the reliability of a typical circuit system.

*2024/4/17*

Chengxi Zhang 张承玺

Harbin Institute of Technology

Symmetry

##### A Review of Statistical-based Fault Detection and Diagnosis with Probabilistic Models

As industrial processes grow increasingly complex, fault identification becomes challenging, and even minor errors can significantly impact both productivity and system safety. Fault detection and diagnosis (FDD) has emerged as a crucial strategy for maintaining system reliability and safety through condition monitoring and abnormality recovery to manage this challenge. Statistical-based FDD methods that rely on large-scale process data and their features have been developed for detecting faults. This paper overviews recent investigations and developments in statistical-based FDD methods, focusing on probabilistic models. The theoretical background of these models is presented, including Bayesian learning and maximum likelihood. We then discuss various techniques and methodologies, e.g., probabilistic principal component analysis (PPCA), probabilistic partial least squares (PPLS), probabilistic independent component analysis (PICA), probabilistic canonical correlation analysis (PCCA), and probabilistic Fisher discriminant analysis (PFDA). Several test statistics are analyzed to evaluate the discussed methods. In industrial processes, these methods require complex matrix operation and cost computational load. Finally, we discuss the current challenges and future trends in FDD.

*2024/4/8*

Tuo Leng

Shanghai University

Symmetry

##### FGeo-TP: A Language Model-Enhanced Solver for Euclidean Geometry Problems

The application of contemporary artificial intelligence techniques to address geometric problems and automated deductive proofs has always been a grand challenge to the interdisciplinary field of mathematics and artificial intelligence. This is the fourth article in a series of our works, in our previous work, we established a geometric formalized system known as FormalGeo. Moreover, we annotated approximately 7000 geometric problems, forming the FormalGeo7k dataset. Despite the fact that FGPS (Formal Geometry Problem Solver) can achieve interpretable algebraic equation solving and human-like deductive reasoning, it often experiences timeouts due to the complexity of the search strategy. In this paper, we introduced FGeo-TP (theorem predictor), which utilizes the language model to predict the theorem sequences for solving geometry problems. The encoder and decoder components in the transformer architecture naturally establish a mapping between the sequences and embedding vectors, exhibiting inherent symmetry. We compare the effectiveness of various transformer architectures, such as BART or T5, in theorem prediction, and implement pruning in the search process of FGPS, thereby improving its performance when solving geometry problems. Our results demonstrate a significant increase in the problem-solving rate of the language model-enhanced FGeo-TP on the FormalGeo7k dataset, rising from 39.7% to 80.86%. Furthermore, FGeo-TP exhibits notable reductions in solution times and search steps across problems of varying difficulty levels.

*2024/4/3*

Jan Brandts

Universiteit van Amsterdam

Symmetry

##### Regular Tessellations of Maximally Symmetric Hyperbolic Manifolds

We first briefly summarize several well-known properties of regular tessellations of the three two-dimensional maximally symmetric manifolds, E2, S2, and H2, by bounded regular tiles. For instance, there exist infinitely many regular tessellations of the hyperbolic plane H2 by curved hyperbolic equilateral triangles whose vertex angles are 2π/d for d=7,8,9,… On the other hand, we prove that there is no curved hyperbolic regular tetrahedron which tessellates the three-dimensional hyperbolic space H3. We also show that a regular tessellation of H3 can only consist of the hyperbolic cubes, hyperbolic regular icosahedra, or two types of hyperbolic regular dodecahedra. There exist only two regular hyperbolic space-fillers of H4. If n>4, then there exists no regular tessellation of Hn.

*2024/1/24*

Victor Bovdi (В. А. Бовди)

United Arab Emirates University

Symmetry

##### On Some Aspects of the Courant-Type Algebroids, the Related Coadjoint Orbits and Integrable Systems

Poisson structures related to affine Courant-type algebroids are analyzed, including those related with cotangent bundles on Lie-group manifolds. Special attention is paid to Courant-type algebroids and their related R structures generated by suitably defined tensor mappings. Lie–Poisson brackets that are invariant with respect to the coadjoint action of the loop diffeomorphism group are created, and the related Courant-type algebroids are described. The corresponding integrable Hamiltonian flows generated by Casimir functionals and generalizing so-called heavenly-type differential systems describing diverse geometric structures of conformal type in finite dimensional Riemannian manifolds are described.

*2024/1/5*

Antonio Valderrabano-Gonzalez

Universidad Panamericana

Symmetry

##### A Symmetric Sixth-Order Step-Up Converter with Asymmetric PWM Achieved with Small Energy Storage Components

This research explores an improved operation of a recently studied converter, the so-called two-phase sixth-order boost converter (2P6OBC). The converter consists of a symmetric design of power stations followed by an LC filter; its improved operation incorporates an asymmetric pulse width modulation (PWM) scheme for transistor switching, sometimes known as an interleaved PWM approach. The new operation leads to improved performance for the 2P6OBC. Along with studying the 2P6OBC, one of the contributions of this research is providing design equations for the converter and comparing it versus the interleaved (or multiphase) boost converter, known for its competitiveness and advantages; the single-phase boost topology was also included in the comparison. The comparison consisted of a design scenario where all converters must achieve the same power conversion with an established maximum switching ripple, and then the stored energy in passive components is compared. Although the 2P6OBC requires a greater number of components, the total amount of stored energy is smaller. It is known that the stored energy is related to the size of the passive components. Still, the article includes a discussion of this topic. The new operation of the converter offers more streamlined, cost-effective, and efficient alternatives for a range of applications within power electronics. The final design of the 2P6OBC required only 68% of the stored energy in inductors compared to the multiphase boost converter, and 60% of the stored energy in capacitors. This result is outstanding, considering that the multiphase boost converter is a very competitive …

*2024/4/10*

Ivan Lazovic

Univerzitet u Beogradu

Symmetry

##### Symmetric U-Net Model Tuned by FOX Metaheuristic Algorithm for Global Prediction of High Aerosol Concentrations

In this study, the idea of using a fully symmetric U-Net deep learning model for forecasting a segmented image of high global aerosol concentrations is implemented. As the forecast relies on historical data, the model used a sequence of the last eight segmented images to make the prediction. For this, the classic U-Net model was modified to use ConvLSTM2D layers with MaxPooling3D and UpSampling3D layers. In order to achieve complete symmetry, the output data are given in the form of a series of eight segmented images shifted by one image in the time sequence so that the last image actually represents the forecast of the next image of high aerosol concentrations. The proposed model structure was tuned by the new FOX metaheuristic algorithm. Based on our analysis, we found that this algorithm is suitable for tuning deep learning models considering their stochastic nature. It was also found that this algorithm spends the most time in areas close to the optimal value where there is a weaker linear correlation with the required metric and vice versa. Taking into account the characteristics of the used database, we concluded that the model is capable of generating adequate data and finding patterns in the time domain based on the ddc and dtc criteria. By comparing the achieved results of this model using the AUC-PR metric with the previous results of the ResNet3D-101 model with transfer learning, we concluded that the proposed symmetric U-Net model generates data better and is more capable of finding patterns in the time domain.

*2024/5*

Xiaofeng Liao

Chongqing University

Symmetry

##### A Novel Spatiotemporal Periodic Polynomial Model for Predicting Road Traffic Speed

Accurate and fast traffic prediction is the data-based foundation for achieving traffic control and management, and the accuracy of prediction results will directly affect the effectiveness of traffic control and management. This paper proposes a new spatiotemporal periodic polynomial model for road traffic, which integrates the temporal, spatial, and periodic features of speed time series and can effectively handle the nonlinear mapping relationship from input to output. In terms of the model, we establish a road traffic speed prediction model based on polynomial regression. In terms of spatial feature extraction methods, we introduce a maximum mutual information coefficient spatial feature extraction method. In terms of periodic feature extraction methods, we introduce a periodic trend modeling method into the prediction of speed time series, and effective fusion is carried out. Four strategies are evaluated based on the Guangzhou road speed dataset: a univariate polynomial model, a spatiotemporal polynomial model, a periodic polynomial model, and a spatiotemporal periodic polynomial model. The test results show that the three methods proposed in this article can effectively improve prediction accuracy. Comparing the spatiotemporal periodic polynomial model with multiple machine learning models and deep learning models, the prediction accuracy is improved by 5.94% compared to the best feedforward neural network. The research in this article can effectively deal with the temporal, spatial, periodic, and nonlinear characteristics of speed prediction, and to a certain extent, improve the accuracy of speed prediction.

*2024/4/30*

Bhuwan Khatri Chhetri

Georgia Institute of Technology

Symmetry

##### Revisiting the Absolute Configuration of Peyssonnoside A Using Vibrational Circular Dichroism Spectroscopy

Peyssonnoside A is an unusual natural product consisting of a diterpene unit and a sulfonated monosaccharide. The experimental and theoretical comparison of Optical Rotatory Dispersion (ORD) and quantitative Nuclear Magnetic Resonance (NMR) data provided strong evidence for the stereochemistry of the diterpene unit. However, predicted Vibrational Circular Dichroism (VCD) spectra of Peyssonnoside A at the B3LYP/6-311++G(2d,2p) level showed poor correlation to the corresponding experimental spectra, preventing independent absolute configuration (AC) determination from VCD analysis. New calculations using the B3PW91 functional and the 6-311G(3df,2pd) basis set suggest that we can now independently and confidently assign the AC of Peyssonnoside A through VCD analyses. The use of f-polarization functions is responsible for the current successful assignment, compared to previously failed VCD analysis. This study highlights two important points: (a) the importance of using multiple levels of theories for satisfactorily reproducing the experimental spectra and (b) for quantitative comparisons using similarity indices, it is important to consider not only the VCD spectra but also the corresponding absorption spectra.

*2024/1/23*

Ewa Roszkowska

Uniwersytet w Bialymstoku

Symmetry

##### Modifying Hellwig’s Method for Multi-Criteria Decision-Making with Mahalanobis Distance for Addressing Asymmetrical Relationships

Hellwig’s method is a multi-criteria decision-making technique designed to facilitate the ranking of alternatives based on their proximity to the ideal solution. Typically, this approach calculates distances using the Euclidean norm, assuming implicitly that the considered criteria are independent. However, in real-world situations, the assumption of criteria independence is rarely met. The paper aims to propose an extension of Hellwig’s method by incorporating the Mahalanobis distance. Substituting the Euclidean distance with the Mahalanobis distance has proven to be effective in handling correlations among criteria, especially in the context of asymmetrical relationships between criteria. Subsequently, we investigate the impact of the Euclidean and Mahalanobis distance measures on the several variants of Hellwig procedures, analyzing examples based on various illustrative data with 10 alternatives and 4 criteria. Additionally, we examine the influence of three normalization formulas in Hellwig’s aggregation procedures. The investigation results indicate that both the distance measure and normalization formulas have some impact on the final rankings. The evaluation and ranking of alternatives using the Euclidean distance measure are influenced by the normalization formula, albeit to a limited extent. In contrast, the Mahalanobis distance-based Hellwig’s method remains unaffected by the choice of normalization formulas. The study concludes that the ranking of alternatives is strongly dependent on the distance measure employed, whether it is Euclidean or Mahalanobis. The Mahalanobis distance-based Hellwig method is deemed a valuable …

*2024/1/6*

Cuba-Dorado, A.

Universidade de Vigo

Symmetry

##### Explanatory model for elite canoeists’ performance using a functional electromechanical dynamometer based on detected lateral asymmetry

Canoe modality in flatwater canoeing has a clear asymmetrical nature. This study aimed (1) to determine the magnitude and direction of neuromuscular properties, range of motion (ROM) and lower-limb strength asymmetries in female and male canoeists; (2) to establish sex-individualized asymmetry thresholds for canoeists’ neuromuscular properties, ROM and lower-limb strength; and (3) to determine the relationship of canoeists’ neuromuscular properties, ROM and lower-limb strength asymmetries with a specific canoe–dynamometer performance test. Twenty-one international canoeists were assessed through tensiomyography (TMG), ROM, lower-limb explosive strength, and a specific canoe incremental dynamometric test. The magnitude of asymmetry assessed through TMG and ROM was not modulated either by sex or performance level (international medal vs. non-medal). Females showed greater asymmetry than males on muscle tone of the erector spinae towards non-stroke side (22.75% vs. 9.72%) and the tibialis anterior (30.97% vs. 16.29%), and Fmax in explosive leg press (2.41% vs. 0.63%) towards the stroke side. International medalists showed greater asymmetry in semitendinosus contraction time towards non-stroke side (20.51% vs. 9.43%) and reached Vmax earlier in explosive leg press towards stroke side leg (19.20% vs. 9.40%). A greater asymmetry in Fmax and in Vm, and a smaller asymmetry in Tvmax and in leg press showed a small predictive capacity for canoeists’ performance on a specific canoe incremental dynamometry test. Reporting reference data from world-class canoeists’ asymmetries can be of great …

*2024/3/14*

Ruzayn Quaddoura

Zarqa University

Symmetry

##### Bipartite (P6,C6)-Free Graphs: Recognition and Optimization Problems

The canonical decomposition of a bipartite graph is a new decomposition method that involves three operators: parallel, series, and K⨁ S. The class of weak-bisplit graphs is the class of totally decomposable graphs with respect to these operators, and the class of bicographs is the class of totally decomposable graphs with respect to parallel and series operators. We prove in this paper that the class of bipartite (P6,C6)-free graphs is the class of bipartite graphs that are totally decomposable with respect to parallel and K⨁S operators. We present a linear time recognition algorithm for (P6,C6)-free graphs that is symmetrical to the linear recognition algorithms of weak-bisplit graphs and star1,2,3-free bipartite graphs. As a result of this algorithm, we present efficient solutions in this class of graphs for two optimization graph problems: the maximum balanced biclique problem and the maximum independent set problem.

*2024/4/7*

K Chandrasekaran

National Institute of Technology, Karnataka

Symmetry

##### A New Asymmetric H-6 Structured Multilevel Inverter with Reduced Power Components

Multilevel inverters play a key role in improving the power quality for industrial, domestic, and renewable energy sectors due to sinusoidal output voltage through small voltage steps, lesser THD (total harmonic distortion), and EMI (electromagnetic interference). There are several variants in MLI structures to generate a stepped voltage with their own operating characteristics, which flaws in switching devices with gate drivers, current conducting switches varied with varied voltage levels, and switches with different abilities in blocking voltagesto overcome increases in implementation costs and restrict its usage in high-power applications. Therefore, this article paves a solution for the above problem, which orients a new structure for asymmetric operation to propel large voltage levels with small values of switches in parallel with conventional topologies. The subtlety of the proposed topology is governed by a multicarrier pulse width modulation scheme, and ten different voltage magnitude algorithms are developed and compared foreffectiveness.Hitherto, many existing MLI topologies with reduced power switches have beendeveloped; among these, the H6 structure attempts to curtail the reduced conduction path. The operation of the suggested topology is confirmed in a Matlab/Simulink environment, and real-time performance is investigated using a laboratory prototype to accord the simulated results.

*2024/1/5*

Johnny Posada Contreras

Universidad Autónoma de Occidente

Symmetry

##### A Symmetric Sixth-Order Step-Up Converter with Asymmetric PWM Achieved with Small Energy Storage Components

This research explores an improved operation of a recently studied converter, the so-called two-phase sixth-order boost converter (2P6OBC). The converter consists of a symmetric design of power stations followed by an LC filter; its improved operation incorporates an asymmetric pulse width modulation (PWM) scheme for transistor switching, sometimes known as an interleaved PWM approach. The new operation leads to improved performance for the 2P6OBC. Along with studying the 2P6OBC, one of the contributions of this research is providing design equations for the converter and comparing it versus the interleaved (or multiphase) boost converter, known for its competitiveness and advantages; the single-phase boost topology was also included in the comparison. The comparison consisted of a design scenario where all converters must achieve the same power conversion with an established maximum switching ripple, and then the stored energy in passive components is compared. Although the 2P6OBC requires a greater number of components, the total amount of stored energy is smaller. It is known that the stored energy is related to the size of the passive components. Still, the article includes a discussion of this topic. The new operation of the converter offers more streamlined, cost-effective, and efficient alternatives for a range of applications within power electronics. The final design of the 2P6OBC required only 68% of the stored energy in inductors compared to the multiphase boost converter, and 60% of the stored energy in capacitors. This result is outstanding, considering that the multiphase boost converter is a very competitive …

*2024/4/10*

Ke-ke Shang 尚可可

Nanjing University

Symmetry

##### Propagation Dynamics of an Epidemic Model with Heterogeneous Control Strategies on Complex Networks

Complex network theory involves network structure and dynamics; dynamics on networks and interactions between networks; and dynamics developed over a network. As a typical application of complex networks, the dynamics of disease spreading and control strategies on networks have attracted widespread attention from researchers. We investigate the dynamics and optimal control for an epidemic model with demographics and heterogeneous asymmetric control strategies (immunization and quarantine) on complex networks. We derive the epidemic threshold and study the global stability of disease-free and endemic equilibria based on different methods. The results show that the disease-free equilibrium cannot undergo a Hopf bifurcation. We further study the optimal control strategy for the complex system and obtain its existence and uniqueness. Numerical simulations are conducted on scale-free networks to validate and supplement the theoretical results. The numerical results indicate that the asymmetric control strategies regarding time and degree of node for populations are superior to symmetric control strategies when considering control cost and the effectiveness of controlling infectious diseases. Meanwhile, the advantages of the optimal control strategy through comparisons with various baseline immunization and quarantine schemes are also shown.

*2024/1/31*

Ugur Camci

Roger Williams University

Symmetry

##### Noether Symmetry Analysis of the Klein–Gordon and Wave Equations in Bianchi I Spacetime

We investigate the Noether symmetries of the Klein–Gordon Lagrangian for Bianchi I spacetime. This is accomplished using a set of new Noether symmetry relations for the Klein–Gordon Lagrangian of Bianchi I spacetime, which reduces to the wave equation in a special case. A detailed Noether symmetry analysis of the Klein–Gordon and the wave equations for Bianchi I spacetime is presented, and the corresponding conservation laws are derived.

*2024/1/18*