Commercial mobile robots are usually equipped with multiple depth sensors that can measure the point cloud information around the robot's environment. The installation process of these sensors contains assembly error and sensor measurement error, so it is necessary to calibrate each sensor to align the point cloud. In order to obtain the sensor calibration results of commercial robots under normal working conditions, this study proposes a fixture free multi depth sensor joint calibration method that can be deployed on low‐cost embedded computing units, which efficiently aligns the point clouds of each sensor. During the calibration process, the robot is placed in the center of three upright thin plates perpendicular to the ground. 2D LIDAR depicts high‐precision contours of the upright thin plates. In the calibration process of each depth sensor, the roll angle and pitch angle of the sensor point cloud are first calibrated to make it perpendicular to the ground, and then the yaw angle and position of the point cloud are calibrated to fit the high‐precision contour of the upright thin plate. The results show that this method can be deployed on low‐cost embedded computing units, with real‐time and accurate calibration results. The convergence of calibration results can be achieved through up to 5 iterations, and the average running time is less than 120 ms. This research achievement provides a reference for multi‐sensor calibration of commercial robots.
{"title":"A novel joint depth sensor calibration method without fixture for mobile robots’ navigation","authors":"Yiming Lu, Rupeng Yuan, Tiegang Xue","doi":"10.1049/tje2.12384","DOIUrl":"https://doi.org/10.1049/tje2.12384","url":null,"abstract":"Commercial mobile robots are usually equipped with multiple depth sensors that can measure the point cloud information around the robot's environment. The installation process of these sensors contains assembly error and sensor measurement error, so it is necessary to calibrate each sensor to align the point cloud. In order to obtain the sensor calibration results of commercial robots under normal working conditions, this study proposes a fixture free multi depth sensor joint calibration method that can be deployed on low‐cost embedded computing units, which efficiently aligns the point clouds of each sensor. During the calibration process, the robot is placed in the center of three upright thin plates perpendicular to the ground. 2D LIDAR depicts high‐precision contours of the upright thin plates. In the calibration process of each depth sensor, the roll angle and pitch angle of the sensor point cloud are first calibrated to make it perpendicular to the ground, and then the yaw angle and position of the point cloud are calibrated to fit the high‐precision contour of the upright thin plate. The results show that this method can be deployed on low‐cost embedded computing units, with real‐time and accurate calibration results. The convergence of calibration results can be achieved through up to 5 iterations, and the average running time is less than 120 ms. This research achievement provides a reference for multi‐sensor calibration of commercial robots.","PeriodicalId":510109,"journal":{"name":"The Journal of Engineering","volume":"87 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141034933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A method for phase retrieval from magnitude‐only data for electrically small antennas intended for use in relation to body‐centric wireless communication, is presented. The method utilizes the spherical wave expansion (SWE) description of the electromagnetic field radiated by the antenna under test (AUT). The expansion coefficients of the SWE are optimized such that errors between the reconstructed and provided data magnitudes are as small as possible. The required additional phase information is acquired from additional magnitude‐only data sets of the AUT in different orientations. These orientations are defined by rotations of the AUT along the Eulerian angles. The performance of the method is evaluated for a realistic configuration, which consists of an inverted‐A antenna in the vicinity of an ear. The sensitivity of the method to the angular resolution of the field data, as well as to the Eulerian rotation accuracy, is investigated. Finally, the reconstruction method is tested on a set of practical antenna measurements conducted in an RF‐anechoic chamber.
本文介绍了一种从仅有幅度的数据中获取相位的方法，该方法适用于与以人体为中心的无线通信有关的小型天线。该方法利用球面波展开（SWE）描述被测天线（AUT）辐射的电磁场。SWE 的扩展系数经过优化，使重建数据与提供的数据幅度之间的误差尽可能小。所需的附加相位信息可从不同方向的 AUT 附加纯幅值数据集中获取。这些方向由 AUT 沿欧拉角的旋转来定义。对该方法的性能进行了实际配置评估，该配置包括耳朵附近的倒 A 型天线。研究了该方法对现场数据的角度分辨率以及欧拉旋转精度的敏感性。最后，在射频消声室进行的一组实际天线测量中测试了重构方法。
{"title":"Phase‐reconstruction from magnitude‐only data of electrically small antennas for body‐centric wireless communication","authors":"J. Ø. Nielsen, S. Kvist, K. Jakobsen","doi":"10.1049/tje2.12361","DOIUrl":"https://doi.org/10.1049/tje2.12361","url":null,"abstract":"A method for phase retrieval from magnitude‐only data for electrically small antennas intended for use in relation to body‐centric wireless communication, is presented. The method utilizes the spherical wave expansion (SWE) description of the electromagnetic field radiated by the antenna under test (AUT). The expansion coefficients of the SWE are optimized such that errors between the reconstructed and provided data magnitudes are as small as possible. The required additional phase information is acquired from additional magnitude‐only data sets of the AUT in different orientations. These orientations are defined by rotations of the AUT along the Eulerian angles. The performance of the method is evaluated for a realistic configuration, which consists of an inverted‐A antenna in the vicinity of an ear. The sensitivity of the method to the angular resolution of the field data, as well as to the Eulerian rotation accuracy, is investigated. Finally, the reconstruction method is tested on a set of practical antenna measurements conducted in an RF‐anechoic chamber.","PeriodicalId":510109,"journal":{"name":"The Journal of Engineering","volume":"109 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140379580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mohammad Ali Fotouhi Ghazvini, K. Antoniadou-Plytaria, David Steen, Le Anh Tuan
In this study, an optimisation model is developed for two‐stage energy management of a residential building to minimise energy cost under monthly power‐based tariffs for peak demand and time‐variable electricity prices. The expected peak demand is determined in the first stage, and then the energy management system minimizes energy costs during the second stage. The second stage's optimisation problem is solved in a rolling time window, facilitating real‐time operation of flexible energy sources in the building. This includes optimal charging and discharging of the battery energy system, electric vehicle battery charging, heating system operation, and determining the optimal start times for washing machines and dishwashers, all close to real‐time. The proposed approach enables users to predict and manage peak demand in daily operation, staying below the predetermined value through a close to real‐time energy management system. The effectiveness of this two‐stage approach in demand‐side management for residential buildings is demonstrated through a realistic case study.
{"title":"Two‐stage demand‐side management in energy flexible residential buildings","authors":"Mohammad Ali Fotouhi Ghazvini, K. Antoniadou-Plytaria, David Steen, Le Anh Tuan","doi":"10.1049/tje2.12372","DOIUrl":"https://doi.org/10.1049/tje2.12372","url":null,"abstract":"In this study, an optimisation model is developed for two‐stage energy management of a residential building to minimise energy cost under monthly power‐based tariffs for peak demand and time‐variable electricity prices. The expected peak demand is determined in the first stage, and then the energy management system minimizes energy costs during the second stage. The second stage's optimisation problem is solved in a rolling time window, facilitating real‐time operation of flexible energy sources in the building. This includes optimal charging and discharging of the battery energy system, electric vehicle battery charging, heating system operation, and determining the optimal start times for washing machines and dishwashers, all close to real‐time. The proposed approach enables users to predict and manage peak demand in daily operation, staying below the predetermined value through a close to real‐time energy management system. The effectiveness of this two‐stage approach in demand‐side management for residential buildings is demonstrated through a realistic case study.","PeriodicalId":510109,"journal":{"name":"The Journal of Engineering","volume":" May","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140383259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Ferdosian, H. Abdi, Shahram Karimi, Saeed Kharrati
With the increase in population and the growth of technology, the load demand has increased and major changes in spinning reserve are unavoidable. Short‐term forecasting to hourly predict the required load and spinning reserve is of great importance. All of the power system studies in planning and operation fields are depend on short‐term hourly load forecasting. In this work, the problem of load forecasting and spinning reserve based on deep learning (DL) algorithms and traditional methods is investigated with the help of the proposed information combination system. The proposed method tries to reduce the weaknesses of the stated methods and increase the accuracy of the predicted signal. First, short‐term predicting of load and spinning reserve is performed using a combination of adaptive network‐based fuzzy inference system (ANFIS) and meta‐heuristic algorithms including differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO). The ANFIS‐PSO is selected as the best ANFIS combination in load and spinning reserve prediction with a lower error criterion than other methods. Also, the long short‐term memory (LSTM) network can provide good accuracy for load and spinning reserve forecasting. Therefore, the combination of ANFIS‐PSO and LSTM is used to reduce the average error and error variance.
{"title":"Short‐term load and spinning reserve prediction based on LSTM and ANFIS with PSO algorithm","authors":"M. Ferdosian, H. Abdi, Shahram Karimi, Saeed Kharrati","doi":"10.1049/tje2.12356","DOIUrl":"https://doi.org/10.1049/tje2.12356","url":null,"abstract":"With the increase in population and the growth of technology, the load demand has increased and major changes in spinning reserve are unavoidable. Short‐term forecasting to hourly predict the required load and spinning reserve is of great importance. All of the power system studies in planning and operation fields are depend on short‐term hourly load forecasting. In this work, the problem of load forecasting and spinning reserve based on deep learning (DL) algorithms and traditional methods is investigated with the help of the proposed information combination system. The proposed method tries to reduce the weaknesses of the stated methods and increase the accuracy of the predicted signal. First, short‐term predicting of load and spinning reserve is performed using a combination of adaptive network‐based fuzzy inference system (ANFIS) and meta‐heuristic algorithms including differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO). The ANFIS‐PSO is selected as the best ANFIS combination in load and spinning reserve prediction with a lower error criterion than other methods. Also, the long short‐term memory (LSTM) network can provide good accuracy for load and spinning reserve forecasting. Therefore, the combination of ANFIS‐PSO and LSTM is used to reduce the average error and error variance.","PeriodicalId":510109,"journal":{"name":"The Journal of Engineering","volume":"28 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140427312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Benchi Jiang, Yansheng Zhou, Zhijian Tu, Jiabao Pan
Grease in the normal operation of the rotate vector (RV) reducer has a role that cannot be ignored, for the variable working conditions of the RV reducer, the performance of the lubricant changes directly affect its reliable operation. Therefore, the study of the rheological properties of the grease has become the focus of the study of RV reducer performance. Here, SK‐1A grease is taken as the research object, and its rheological characteristics under wide temperature range working conditions (−20–40°C) are investigated through rheological experiments to analyze the potential influence of the performance of RV reducer. However, the ordinary way of research is too complicated to better research the rheological properties of grease for a variety of working conditions. The Elman neural network (ENN) model was used to predict the rheological properties, and the results were compared with those of back propagation (BP) and radial basis function (RBF) neural networks. The results demonstrate that the ENN model demonstrates high prediction accuracy for grease rheological property prediction by comparing three types of predictions. This method can provide a theoretical reference for the accurate prediction of the rheological properties of lubricating grease affected by complex multifactors.
{"title":"Rheological characteristics and behaviour prediction of lubricating grease for RV reducer across a wide temperature range","authors":"Benchi Jiang, Yansheng Zhou, Zhijian Tu, Jiabao Pan","doi":"10.1049/tje2.12362","DOIUrl":"https://doi.org/10.1049/tje2.12362","url":null,"abstract":"Grease in the normal operation of the rotate vector (RV) reducer has a role that cannot be ignored, for the variable working conditions of the RV reducer, the performance of the lubricant changes directly affect its reliable operation. Therefore, the study of the rheological properties of the grease has become the focus of the study of RV reducer performance. Here, SK‐1A grease is taken as the research object, and its rheological characteristics under wide temperature range working conditions (−20–40°C) are investigated through rheological experiments to analyze the potential influence of the performance of RV reducer. However, the ordinary way of research is too complicated to better research the rheological properties of grease for a variety of working conditions. The Elman neural network (ENN) model was used to predict the rheological properties, and the results were compared with those of back propagation (BP) and radial basis function (RBF) neural networks. The results demonstrate that the ENN model demonstrates high prediction accuracy for grease rheological property prediction by comparing three types of predictions. This method can provide a theoretical reference for the accurate prediction of the rheological properties of lubricating grease affected by complex multifactors.","PeriodicalId":510109,"journal":{"name":"The Journal of Engineering","volume":"292 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140469091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The consumption of coal in winter heating period in northern China is large, and the combustion generates greenhouse gases that pollute the environment. At the same time, wind abandonment is widespread in northern China, causing waste of energy. In order to solve these problems, this paper proposes to apply clean energy heating and waste wind power generation for heating, and build a multi‐objective optimal dispatching model under the goal of considering customer satisfaction and operating costs. Finally, taking a region in the north of China as an example, the improved genetic algorithm is used to solve the model, the improved genetic algorithm ensures the survival rate of excellent genes, which is more efficient than the traditional genetic algorithm. The example results verify that the use of clean energy heating can increase the wind power consumption space and reduce the heating cost in winter.
{"title":"Optimal dispatching of clean energy heating considering customer satisfaction","authors":"Haifeng Cheng, Houjing Guo, Huang Minli, Zhixuan Pan, Cheng Jin, Wu Dabala, Jieren Tan","doi":"10.1049/tje2.12355","DOIUrl":"https://doi.org/10.1049/tje2.12355","url":null,"abstract":"The consumption of coal in winter heating period in northern China is large, and the combustion generates greenhouse gases that pollute the environment. At the same time, wind abandonment is widespread in northern China, causing waste of energy. In order to solve these problems, this paper proposes to apply clean energy heating and waste wind power generation for heating, and build a multi‐objective optimal dispatching model under the goal of considering customer satisfaction and operating costs. Finally, taking a region in the north of China as an example, the improved genetic algorithm is used to solve the model, the improved genetic algorithm ensures the survival rate of excellent genes, which is more efficient than the traditional genetic algorithm. The example results verify that the use of clean energy heating can increase the wind power consumption space and reduce the heating cost in winter.","PeriodicalId":510109,"journal":{"name":"The Journal of Engineering","volume":"165 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140476077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Leeban Moses, Perarasi T. Sambantham, Muhammad Faheem, Shoukath Ali K, A. Khan
The exponential growth in data traffic from smart devices has led to a need for highly capable wireless networks with faster data transmission rates and improved spectral efficiency. Allocating resources efficiently in a 5G communication system with a huge number of machine type communication (MTC) devices is essential to ensure optimal performance and meet the diverse requirements of different applications. The LTE‐A network offers high‐speed mobile data services and caters to MTC devices and has relatively low data service requirements compared to human‐to‐human (H2H) communications. LTE‐A networks require advanced scheduling schemes to manage the limited spectrum and ensure efficient transmissions. This necessitates effective resource allocation schemes to minimize interference between cells in future networks. To address this issue, a joint delay and energy aware Levy flight Brownian movement‐based dragonfly optimization (DELFBDO)‐based uplink resource allocation scheme for LTE‐A Networks is proposed in this work to optimize energy efficiency, maximize the throughput and reduce the latency. The DELFDO algorithm efficiently organizes packets in both time and frequency domains for H2H and MTC devices, resulting in improved quality of service while minimizing energy consumption. The Simulation results demonstrate that the proposed method increases the energy efficiency by producing the appropriate channel and power assignment for UEs and MTC devices.
{"title":"Joint delay and energy aware dragonfly optimization‐based uplink resource allocation scheme for LTE‐A networks in a cross‐layer environment","authors":"Leeban Moses, Perarasi T. Sambantham, Muhammad Faheem, Shoukath Ali K, A. Khan","doi":"10.1049/tje2.12353","DOIUrl":"https://doi.org/10.1049/tje2.12353","url":null,"abstract":"The exponential growth in data traffic from smart devices has led to a need for highly capable wireless networks with faster data transmission rates and improved spectral efficiency. Allocating resources efficiently in a 5G communication system with a huge number of machine type communication (MTC) devices is essential to ensure optimal performance and meet the diverse requirements of different applications. The LTE‐A network offers high‐speed mobile data services and caters to MTC devices and has relatively low data service requirements compared to human‐to‐human (H2H) communications. LTE‐A networks require advanced scheduling schemes to manage the limited spectrum and ensure efficient transmissions. This necessitates effective resource allocation schemes to minimize interference between cells in future networks. To address this issue, a joint delay and energy aware Levy flight Brownian movement‐based dragonfly optimization (DELFBDO)‐based uplink resource allocation scheme for LTE‐A Networks is proposed in this work to optimize energy efficiency, maximize the throughput and reduce the latency. The DELFDO algorithm efficiently organizes packets in both time and frequency domains for H2H and MTC devices, resulting in improved quality of service while minimizing energy consumption. The Simulation results demonstrate that the proposed method increases the energy efficiency by producing the appropriate channel and power assignment for UEs and MTC devices.","PeriodicalId":510109,"journal":{"name":"The Journal of Engineering","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140478104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. A. Martínez-Ciro, F. López-Giraldo, J. M. Luna-Rivera, A. M. Ramirez-Aguilera
A visible light positioning (VLP) system for multi‐cell networks based on multi‐colour LED that combines frequency division multiplexing with the received signal strength and a trilateration method is proposed. First, it employs experimental measurements to model the designed visible light communication (VLC) system under the characteristics of the target scenario. Second, it introduces a low‐cost VLC transmitter that exploits the chromatic space to transmit the VLP information. Third, it characterizes the VLC transmitter and proposes a linearization for electro‐optical responses of RGB LED. An RGB digital colour sensor and simple method calibrate the chromatic emission. The experimental results show that the proposed VLP multi‐cell architecture achieves a positioning accuracy lower than .
本文提出了一种基于多色 LED 的多蜂窝网络可见光定位（VLP）系统，该系统结合了频分复用、接收信号强度和三坐标法。首先，它利用实验测量来模拟目标场景特征下设计的可见光通信（VLC）系统。其次，介绍了一种利用色度空间传输可见光信息的低成本 VLC 发射器。第三，描述了 VLC 发射器的特性，并提出了 RGB LED 光电响应的线性化方法。RGB 数字色彩传感器和简单的方法校准了色度发射。实验结果表明，所提出的 VLP 多单元结构的定位精度低于 .
{"title":"Experimental demonstration of an indoor visible light positioning system using RGB LEDs for multi‐cell networks","authors":"R. A. Martínez-Ciro, F. López-Giraldo, J. M. Luna-Rivera, A. M. Ramirez-Aguilera","doi":"10.1049/tje2.12349","DOIUrl":"https://doi.org/10.1049/tje2.12349","url":null,"abstract":"A visible light positioning (VLP) system for multi‐cell networks based on multi‐colour LED that combines frequency division multiplexing with the received signal strength and a trilateration method is proposed. First, it employs experimental measurements to model the designed visible light communication (VLC) system under the characteristics of the target scenario. Second, it introduces a low‐cost VLC transmitter that exploits the chromatic space to transmit the VLP information. Third, it characterizes the VLC transmitter and proposes a linearization for electro‐optical responses of RGB LED. An RGB digital colour sensor and simple method calibrate the chromatic emission. The experimental results show that the proposed VLP multi‐cell architecture achieves a positioning accuracy lower than .","PeriodicalId":510109,"journal":{"name":"The Journal of Engineering","volume":"677 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140482748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accurate state of charge (SOC) estimation and fault identification and localization are crucial in the field of battery system management. This article proposes an innovative method based on sliding mode observation theory for SOC estimation and short‐circuit fault location. The core of this new method is the design of an adaptive sliding mode observer, which reduces jitter by introducing adaptive switching gain, establishes an internal loop of gain and error, and improves the performance of SOC estimation. In addition, recursive least squares method was used to identify the key parameters of the model. Secondly, based on obtaining the SOC of each battery cell in series with the energy storage PACK, the specificity of the faulty battery cell in SOC change trend is utilized to identify and locate the short‐circuit fault of the energy storage PACK. The simulation and test results show that the designed adaptive sliding mode observer can significantly improve the estimation accuracy of SOC and has better stability. Compared to the commonly used Kalman estimation and BP neural network estimation methods, the designed method has improved accuracy by 5.53% and 3.42%, respectively. In addition, based on the accurate identification of SOC, the short‐circuit fault diagnosis results of the battery PACK have a high accuracy, confirming the feasibility and effectiveness of the designed strategy that includes SOC estimation and short‐circuit fault identification and positioning, and has broad application prospects.
{"title":"SOC estimation and fault identification strategy of energy storage battery PACK: Based on adaptive sliding mode observer","authors":"Huang Xueyi, Tinglong Pan","doi":"10.1049/tje2.12352","DOIUrl":"https://doi.org/10.1049/tje2.12352","url":null,"abstract":"Accurate state of charge (SOC) estimation and fault identification and localization are crucial in the field of battery system management. This article proposes an innovative method based on sliding mode observation theory for SOC estimation and short‐circuit fault location. The core of this new method is the design of an adaptive sliding mode observer, which reduces jitter by introducing adaptive switching gain, establishes an internal loop of gain and error, and improves the performance of SOC estimation. In addition, recursive least squares method was used to identify the key parameters of the model. Secondly, based on obtaining the SOC of each battery cell in series with the energy storage PACK, the specificity of the faulty battery cell in SOC change trend is utilized to identify and locate the short‐circuit fault of the energy storage PACK. The simulation and test results show that the designed adaptive sliding mode observer can significantly improve the estimation accuracy of SOC and has better stability. Compared to the commonly used Kalman estimation and BP neural network estimation methods, the designed method has improved accuracy by 5.53% and 3.42%, respectively. In addition, based on the accurate identification of SOC, the short‐circuit fault diagnosis results of the battery PACK have a high accuracy, confirming the feasibility and effectiveness of the designed strategy that includes SOC estimation and short‐circuit fault identification and positioning, and has broad application prospects.","PeriodicalId":510109,"journal":{"name":"The Journal of Engineering","volume":"167 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140482061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The SWISS rectifier is a three‐phase BUCK type Power Factor Correction rectifier with the advantages of adjustable output full voltage range, low voltage stress in the back stage devices, and high efficiency. However, because the voltage ripple of the input filter capacitor will cause the input current deviation, the input current of the SWISS rectifier will produce distortion at the sector boundary, which will affect the system performance. To this end, a multi‐stage predictive model method based on the spherical algorithm is presented. By predicting the grid‐side capacitor voltage and the input current state of the rectifier, the harmonic injection network switching tube is controlled in advance to supplement the grid‐side capacitor voltage, so that the grid‐side capacitor voltage approximately tracks the input voltage. Meanwhile, considering the current step that may be generated after the current distortion returns to normal, which leads to the resonance problem with the pre‐stage filter, this problem is incorporated into the value function and damping is optimized according to the feedback value. Finally, a 10‐kW SWISS rectifier on the SIMULINK platform is used to verify the feasibility of the new control method.
{"title":"SWISS rectifier structure sector boundary current distortion suppression based on multi‐step predictive control","authors":"Zhun Cheng, Jiangwei Deng, Bing Luo, Yang Zhang","doi":"10.1049/tje2.12354","DOIUrl":"https://doi.org/10.1049/tje2.12354","url":null,"abstract":"The SWISS rectifier is a three‐phase BUCK type Power Factor Correction rectifier with the advantages of adjustable output full voltage range, low voltage stress in the back stage devices, and high efficiency. However, because the voltage ripple of the input filter capacitor will cause the input current deviation, the input current of the SWISS rectifier will produce distortion at the sector boundary, which will affect the system performance. To this end, a multi‐stage predictive model method based on the spherical algorithm is presented. By predicting the grid‐side capacitor voltage and the input current state of the rectifier, the harmonic injection network switching tube is controlled in advance to supplement the grid‐side capacitor voltage, so that the grid‐side capacitor voltage approximately tracks the input voltage. Meanwhile, considering the current step that may be generated after the current distortion returns to normal, which leads to the resonance problem with the pre‐stage filter, this problem is incorporated into the value function and damping is optimized according to the feedback value. Finally, a 10‐kW SWISS rectifier on the SIMULINK platform is used to verify the feasibility of the new control method.","PeriodicalId":510109,"journal":{"name":"The Journal of Engineering","volume":"300 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140480808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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