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Research on the state-of-charge fusion estimation of lithium-ion batteries by the Extract Segment Fusion method

JOURNAL OF ENERGY STORAGE 2025年 117卷

作者： Zhao, Zhihui Kou, Farong Pan, Zhengniu Chen, Leiming Xian Univ Sci & Technol Sch Mech Engn Xian 710054 Shaanxi Peoples R China Guangxi Univ Sch Phys Sci & Technol Nanning 530004 Guangxi Peoples R China Zhengzhou Univ Aeronaut Sch Mat Sci & Engn Zhengzhou 450046 Henan Peoples R China

The fusion of model-based and data-driven techniques for state-of-charge (SOC) estimation of batteries has become a prominent research focus. Existing approaches often rely on data-driven techniques to compensate for model-based methods, which can result in significant overcompensation. In this paper, a novel fusion method for SOC estimation is proposed. Firstly, a variable forgetting factor recursive least squares method with a limiting factor (Lim-VFFRLS) is employed to identify the parameters of a dual polarization (DP) battery model. Then, SOC estimation based on the Extended Kalman Filter (EKF) is divided into three states: steady state, failure state, and transition state. Subsequently, an innovative Extract Segment Fusion (ESF) method is proposed to integrate the advantages of the DP model-based EKF and a back propagation neural network optimized by the mind evolutionary algorithm (MEA-BP). This method, referred to as EKF-MEA-BP-ESF (EMBE). In contrast, the fusion method using linear interpolation is called EMBL. The performance of EMBE is evaluated using driving cycle condition tests, with results showing that the average mean absolute error (MAE) of SOC estimation is 0.243 %, and the average root mean squared error (RMSE) is 0.298 %. Furthermore, compared to the EKF, MEA-BP, and EMBL methods, the EMBE method reduces the MAE by an average of 47.546 %, 60.034 %, and 50.860 %, respectively, and the RMSE by an average of 55.484 %, 65.175 %, and 53.237 %, respectively. These results demonstrate that the EMBE method significantly enhances both the accuracy and robustness of SOC estimation. This approach shows considerable promise for applications in electric vehicles and large-scale energy storage.

关键词： State of charge fusion estimation BP neural network EKF mind evolutionary algorithm

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Prediction of the displacement in a foundation pit based on neural network model fusion error and variational modal decomposition methods

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MEASUREMENT 2025年 240卷

作者： Sun, Linna Liu, Shengchang Zhang, Liming He, Keqiang Yan, Xiuzheng Qingdao Univ Technol Dept Civil Engn Qingdao 266033 Peoples R China Minist Nat Resources Key Lab Geol Safety Coastal Urban Underground Spac Qingdao 266101 Peoples R China

Prediction of foundation pit displacements is an important part of pit monitoring and early warning system, and accurate prediction of pit displacements can reduce pit disaster accidents. Herein, the Elman neural network model optimized by the mind evolutionary algorithm (MEA) is combined with the variational modal decomposition (VMD) and the fusion error method to accurately predict pit displacements at the top of the slope. Firstly, VMD is used to decompose the time series of pit displacements into trend, period, and random terms, and analyzed the rationality of the decomposition of the sub-displacement sequences;Secondly, the MEA-Elman model is used to predict the trend, period, and random terms and decomposition error in the decomposition of pit displacements, and finally the prediction results of the sub-displacements are superimposed to obtain the total displacement;The measured data of a pit in Qingdao, China, are used to verify the reliability of the model, and the calculated results show that the predictions are consistent with the measured data.

关键词： Pit displacement prediction Variational modal decomposition mind evolutionary algorithm Elman neural network Fusion error

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A machining error tracing method based on MEA-BP neural network for quality improvement of gear hubs

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INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2024年第7期37卷 852-865页

作者： Liu, Yongsheng Zhang, Xinhui Ding, Kai Hui, Jizhuang Zhao, Jin Chan, Felix T. S. Changan Univ Inst Smart Mfg Syst Xian Peoples R China Changan Univ Key Lab Rd Construct Technol & Equipment MOE Xian Peoples R China Xian XD High Voltage Apparat Co Ltd Dept Elect Measurement & Control Xian Peoples R China Macau Univ Sci & Technol Dept Decis Sci Macau Peoples R China

The machining quality of workpieces is greatly influenced by the performance of an equipment. Furthermore, it is difficult to establish an error tracing model with high tracing accuracy using a mathematical method. In this study, the machining quality of gear hubs for an automobile synchronizer produced on an intelligent manufacturing line was evaluated. The main sources of machining errors were analyzed, and the machining error tracing model for the gear hub was established through a back propagation (BP) neural network. To improve the performance of the error tracing model, the weights and thresholds of the BP neural network were optimized using the mind evolutionary algorithm (MEA). The MEA-BP error tracing model was trained and tested using online measurement results and historical data of the production line. The results showed that the average tracing accuracy of the MEA-BP method was 97.4%, which was 12.1% higher than that of the BP method. The average running time of the MEA-BP was far less than that of a genetic algorithm (GA) improved BP method. These comparisons prove that the proposed MEA-BP error tracing method is both feasible and effective. The proposed method can improve the machining quality and error tracing in intelligent manufacturing applications.

关键词： Machining quality error tracing mind evolutionary algorithm back propagation neural network online measurement intelligent manufacturing

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Finite element model updating of continuous girder bridge based on IMEA-BP neural network

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INTERNATIONAL JOURNAL OF STRUCTURAL INTEGRITY 2025年

作者： Xiong, Hui-Zhong Sui, Hongbiao Xiao, Yinbo Huang, Yong Zhang, Guohua Northeast Forestry Univ Sch Civil Engn & Transportat Harbin Peoples R China China Earthquake Adm Inst Engn Mech Harbin Peoples R China China Gezhouba Grp Municipal Engn Co Ltd Harbin Peoples R China

PurposeThis paper aims to update the finite element model of a pre-stressed concrete continuous girder bridge using a back propagation neural network optimized by the improved mind evolutionary algorithm (IMEA).Design/methodology/approachIn this paper, we introduce particle swarm optimization in the convergence process of the mind evolutionary algorithm (MEA) and then dynamically adjust the inertia weights in the moving speed of the particle swarm through a nonlinear decreasing strategy, which is used to solve the drawback of the algorithm's arbitrarily initialized population. Finally, the measured natural frequency of the Yunliang River Bridge is used as the input parameter, and the design parameters are obtained by updating in stages (pile foundation stage and bridge completion stage).FindingsThe relative error of the natural frequencies of the continuous girder bridge is significantly reduced from more than 7% before updating to less than 3%. Compared with the 2.31% average relative error of the natural frequency of the continuous beam bridge updated by the MEA-BP neural network, the error of the natural frequency after the update of the IMEA-BP neural network model is only 1.01%. The IMEA-BP neural network demonstrates stronger applicability and higher update precision. Compared to the bridge finite element models without phased updating, the phased updating presents a better ***/valueThis study provides an accurate and effective method of updating finite element models for continuous girder bridges, as well as a valuable reference for updating finite element models for other types of bridges.

关键词： Finite element model updating Continuous girder bridge Back propagation neural network mind evolutionary algorithm

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Prediction method of primary frequency modulation capability of power system based on MEA-BP algorithm

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ENERGY REPORTS 2023年 9卷 111-118页

作者： Zhang, Yudong Tang, Fan Liang, Xiaobin Sun, Jian Li, Hongxun Ru, Hang State Grid Corp China Southwest Branch 299 shuxiu West Rd Chengdu 610000 Peoples R China Southwest Jiaotong Univ Sch Elect Engn 999 Xian Rd Chengdu 610000 Peoples R China

In order to accurately predict the primary frequency modulation capability of power system when frequency deviation occurs, a method for predicting primary frequency modulation performance of power system based on improved neural network with thought evolution algorithm (MEA) is presented. Nine-dimensional parameters, such as frequency deviation before and after disturbance, system backup capacity, load level, mechanical power and electrical power, are selected as input eigenvalues, and the output is the system power change curve. This paper takes CEPRI36v8 system as an example of simulation, obtains the primary frequency modulation curve of the system by setting the shear load disturbance with PSASP, uses the disturbance data and the primary frequency modulation response data as training set, test set and validation set data, trains the prediction model of primary frequency modulation capability based on MEA-BP, and finds the prediction error is about 0.5% compared with the actual data, which proves that the proposed method can accurately and quickly predict the primary frequency modulation capability after the frequency deviation of the power network. This method can assist power dispatcher to analyze the primary frequency modulation response of power grid after power disturbance.(c) 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Primary frequency Prediction analysis Power system disturbance BP neural network mind evolutionary algorithm

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Geomagnetic Positioning-Aided Wi-Fi FTM Localization algorithm for NLOS Environments

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IEEE COMMUNICATIONS LETTERS 2022年第5期26卷 1022-1026页

作者： Sun, Meng Wang, Yunjia Huang, Lu Jia, Haonan Bi, Jingxue Joseph, Wout Plets, David China Univ Min & Technol Sch Environm & Spatial Informat Key Lab Land Environm & Disaster Monitoring MNR Xuzhou 221116 Jiangsu Peoples R China State Key Lab Satellite Nav Syst & Equipment Tech Shijiazhuang 050081 Hebei Peoples R China Shandong Jianzhu Univ Coll Surveying & Geoinformat Jinan 250101 Peoples R China Univ Ghent Dept Informat Technol WAVES Grp B-9052 Ghent Belgium

WiFi-based indoor localization using the fine time measurement (FTM) protocol has become a popular technique. However, in harsh Non-Line-of-Sight (NLoS) environments, WiFi FTM positioning (WFP) suffers from poor performance. In this letter, a novel WiFi FTM localization method with the assistance of geomagnetic positioning (GP) is proposed. To ensure the accuracy of GP, an enhanced mind evolutionary algorithm (EMEA) is designed. A fine-grained WiFi position estimation method using the overlapping searching area (OSA) and the coincident points selection strategy is proposed. Experimental results show that the EMEA-based GP improves the localization performance of WFP in NLOS environments, the mean localization error (ME) and root-mean-square error (RMSE) of the GP-aided WFP are 1.82 m and 2.08 m, respectively. Compared with the classic WFP using the weighted least square (WLS) method, the ME and RMSE are reduced by 51.7% and 52.4%, respectively.

关键词： Wireless fidelity Location awareness Distance measurement Statistics Sociology Estimation evolutionary computation Indoor localization WiFi FTM magnetic positioning NLOS mind evolutionary algorithm

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Application of MEA Optimized Wavelet Neural Network Model in Traffic Flow Prediction 8th

Application of MEA Optimized Wavelet Neural Network Model in...

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8th International Conference on Artificial Intelligence and Security (ICAIS)

作者： Yu, Qian Wang, Haibo Hunan Automot Engn Vocat Coll Dept Vehicle Engn Zhuzhou 412000 Peoples R China

ISBN: (纸本)9783031067945;9783031067938

In order to improve the prediction mode accuracy of wavelet neural network, a prediction model of optimized wavelet neural network based on mind evolutionary algorithm (MEA) is proposed. Firstly, using MEA with extremely strong global search ability to train and optimize the connection weight and the extension scale of the wavelet neural network. Secondly, establish the wavelet neural network using the optimized connection weight and the extension scale. After comparative study of the prediction mode of MEA optimized wavelet neural network, genetic algorithm (GA) optimized wavelet neural network (GAWNN) and not optimized wavelet neural network (WNN) on the prediction for short-term traffic flow, the results show that MEAWNN method has higher prediction accuracy than other two methods.

关键词： Wavelet neural network mind evolutionary algorithm Genetic algorithm Traffic flow prediction Prediction accuracy

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Predicting daily solar radiation using a novel hybrid long short-term memory network across four climate regions of China

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2023年第1期212卷

作者： Xing, Liwen Cui, Ningbo Guo, Li Gong, Daozhi Wen, Shenglin Zhang, Yixuan Fan, Mengying Sichuan Univ State Key Lab Hydraul & Mt River Engn Chengdu 610065 Peoples R China Sichuan Univ Coll Water Resource & Hydropower Chengdu 610065 Peoples R China Chinese Acad Agr Sci State Engn Lab Efficient Water Use Crops & Disaste Beijing 100081 Peoples R China

Accurate daily solar radiation (Rs) data is crucial for the growth and development of crops, but its direct observation is relatively scarce in most regions around the world. Therefore, accurately estimating daily Rs is of great significance. Regarding the above practical requirement, this study developed a novel hybrid deep learning model (MEA-DBN-LSTM), which combined the Long Short-Term Memory model (LSTM), Deep Belief Network (DBN), and mind evolutionary algorithm (MEA), to estimate the Rs across four different climate regions of China using five easily accessible meteorological input combination (sunshine-based, temperature-based and three complex-based). The results showed that MEA-DBN-LSTM was proved to be the most recommended Rs estimated model across four different climate regions of China, and the corresponding median R2, NSE, RMSE, MAE, and MAPE values ranged 0.805-0.999, 0.656-0.954, 1.069-4.289 MJ m  2 d-1, 0.889-3.532 MJ m  2 d-1, and 0.055-0.296 respectively. Correspondingly, compared to the empirical Rs model, the improvement values of MEA-DBN-LSTM were 12.33-52.13 % for R2, 14.80-69.38 % for NSE, 33.75-69.77 % for RMSE, 36.03-62.70 % for MAE, and 42.40-66.67 % for MAPE, respectively. Moreover, the MEA-DBN-LSTM exhibits improvements in R2, NSE, RMSE, MAE, and MAPE, ranging 1.40-13.50 %, 1.27-24.79 %, 1.81-61.18 %, 1.90-61.30 %, and 2.31-57.55 %, respectively, compared to other forms of LSTM-type models. Specifically, this study found the DBN modules significantly improved the complex-based single LSTM model, but without impact on sunshinebased and temperature-based single LSTM models. Regarding the MEA modules, it was effective in the hyperparameter optimization compared to DBN-LSTM. Furthermore, significant accuracy discrepancies exist among Rs models using different input combinations across four different climate regions, and the complex-based was generally superior to other input combinations. Overall, our findings can provide accurate daily Rs

关键词： Solar radiation Empirical model Long Short-Term Memory Network Deep Belief Network mind evolutionary algorithm

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Predicting the Gas Permeability of Sustainable Cement Mortar Containing Internal Cracks by Combining Physical Experiments and Hybrid Ensemble Artificial Intelligence algorithms

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MATERIALS 2023年第15期16卷 5330-5330页

作者： Chao, Zhiming Yang, Chuanxin Zhang, Wenbing Zhang, Ye Zhou, Jiaxin Shanghai Maritime Univ Coll Ocean Sci & Engn Shanghai 200135 Peoples R China Hohai Univ Inst Water Sci & Technol Nanjing 211106 Peoples R China Mentverse Ltd 25 Cabot Sq London E14 4QZ England

The presence of internal fissures holds immense sway over the gas permeability of sustainable cement mortar, which in turn dictates the longevity and steadfastness of associated edifices. Nevertheless, predicting the gas permeability of sustainable cement mortar that contains internal cracks poses a significant challenge due to the presence of numerous influential variables and intricate interdependent mechanisms. To solve the deficiency, this research establishes an innovative machine learning algorithm via the integration of the mind evolutionary algorithm (MEA) with the Adaptive Boosting algorithm-Back Propagation Artificial Neural Network (ABA-BPANN) ensemble algorithm to predict the gas permeability of sustainable cement mortar that contains internal cracks, based on the results of 1452 gas permeability tests. Firstly, the present study employs the MEA-tuned ABA-BPANN model as the primary tool for gas permeability prediction in cement mortar, a comparative analysis is conducted with conventional machine learning models such as Particle Swarm Optimisation algorithm (PSO) and Genetic algorithm (GA) optimised ABA-BPANN, MEA optimised Extreme Learning Machine (ELM), and BPANN. The efficacy of the MEA-tuned ABA-BPANN model is verified, thereby demonstrating its proficiency. In addition, the sensitivity analysis conducted with the aid of the innovative model has revealed that the gas permeability of durable cement mortar incorporating internal cracks is more profoundly affected by the dimensions and quantities of such cracks than by the stress conditions to which the mortar is subjected. Thirdly, puts forth a novel machine-learning model, which enables the establishment of an analytical formula for the precise prediction of gas permeability. This formula can be employed by individuals who lack familiarity with machine learning skills. The proposed model, namely the MEA-optimised ABA-BPANN algorithm, exhibits significant potential in accurately estimating the gas perm

关键词： cement mortar gas permeability mind evolutionary algorithm internal cracks

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Energy Consumption Prediction of Public Buildings Based on MEA-BP Neural Network 33

Energy Consumption Prediction of Public Buildings Based on M...

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第33届中国控制与决策会议

作者： Shuai Wang Hongyan Ma Jiaming Dou Yongxue Ye Jingjian Yang Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture National Virtual Simulation Experimental Center for Smart City Education Institute of Distributed Energy Storage Safety Big Data

ISBN: (纸本)9781665431293

Data mining technology is often adopted in construction engineering to provide relevant information and reference basis for public building energy consumption *** paper proposes an optimization algorithm applying back propagation(BP) neural network and mind evolutionary algorithm(MEA) to establish a model to predict building energy *** main weather data are used as inputs to the BP neural network and daily electricity consumption is used as an *** MEA is employed to optimise the structure of the network and to predict the energy consumption of *** results demonstrate that the optimized BP neural network has higher fitting accuracy,and the predicted load value is more consistent with the actual value.

关键词： Building energy consumption BP neural network mind evolutionary algorithm

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