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State of health prediction of lithium-ion batteries under early partial data based on IWOA-BiLSTM with single feature

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ENERGY 2024年 295卷

作者： Ma, Yan Li, Jiaqi Gao, Jinwu Chen, Hong Jilin Univ Natl Key Lab Automot Chassis Integrat & Bion Changchun Peoples R China Jilin Univ Dept Control Sci & Engn Renmin St 5988 Changchun 130012 Peoples R China Tongji Univ New Energy Automot Engn Ctr Shanghai 200092 Peoples R China

The safe and stable operation of electric vehicles relies on fast and accurate predictions of the state of health (SOH) of the battery. To address challenges such as limited availability of extensive battery aging data or data with informative missingness, the novel SOH prediction method based on the improved method whale optimization algorithm (IWOA)-Bi-directional Long Short -Term Memory (BiLSTM) with strong correlated single aging feature is proposed. Firstly, to accurately predict the accelerated degradation process of the battery capacity, the knee -point in the capacity degradation curve is identified as a starting point for SOH prediction by Bacon -Watts model. Next, a small number of early partial aging features of the battery cycle are extracted, such as time of charging or discharging, and various correlation analysis methods are used to select the single feature with the highest correlation with capacity degradation to reduce the computational complexity of multiple feature factors. Finally, BiLSTM model is established to predict battery SOH. In addition, in order to improve the efficiency of the adjustment for hyperparameters, IWOA is proposed to optimize the BiLSTM's hyperparameters. Compared to the traditional whale optimization algorithm (WOA), IWOA has better global search capability, robustness, and efficiency through enhancements in search strategy, mutation operation, adaptive parameter adjustment, and performance optimization. The proposed method is validated using battery datasets from NASA and CALCE. Compared with BiLSTM and WOA-BiLSTM, the simulation results indicate that the MSE of SOH prediction based on IWOA-BiLSTM method mostly remains below 0.05, and index of agreement (IA) basically maintains higher than 99%.

关键词： Lithium-ion battery State of health Single aging feature Knee-point improved whale optimization algorithm Bi-directional Long Short-Term Memory

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A Novel Feature Selection Method Using whale optimization algorithm and Genetic Operators for Intrusion Detection System in Wireless Mesh Network

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IEEE ACCESS 2020年 8卷 56847-56854页

作者： Vijayanand, R. Devaraj, D. JB Inst Engn & Technol Dept Comp Sci Hyderabad 500075 India Kalasalingam Acad Educ & Res Krishnankoil 626128 India

Machine learning & x2013;based intrusion detection system (IDS) is an important requirement for securing data traffic in wireless mesh networks. The noisy and redundant features of network data tend to degrade the performance of the attack detection classifiers. Therefore, the selection of informative features plays a vital role in the enhancement to the IDS. In this paper, we propose a wrapper-based approach using the modified whale optimization algorithm (WOA). One drawback of WOA is that premature convergence results in a local optimal solution. To overcome this limitation, we proposed a method in which the genetic algorithm operators were combined with the WOA. The crossover operator was used to further improve the search space of whales, and the mutation operator helped to avoid being stuck in the local optimum. The proposed method selects the informative features in the network data, which helps to accurately detect intrusions. Using a support vector machine (SVM), we identified the types of intrusions based on the selected features. The performance of the improved method was analyzed by using the CICIDS2017 and ADFA-LD standard datasets. Our proposed method had better attack detection rate than the standard WOA and other evolutionary algorithms;it also had good accuracy and was suitable for IDS in the wireless mesh networks. The performance of the IDS was increased by selecting the informative features with the improved whale optimization algorithm. The attack detection ratio was higher than that of the standard WOA.

关键词： whales Feature extraction Classification algorithms Support vector machines optimization Standards Intrusion detection Crossover and mutation operator IDS for WMN improved whale optimization algorithm WOA-based feature selection method WOA plus genetic operators

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A combination forecasting model of wind speed based on decomposition

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ENERGY REPORTS 2021年 7卷 1217-1233页

作者： Tian, Zhongda Li, Hao Li, Feihong Shenyang Univ Technol Sch Artificial Intelligence Shenyang Peoples R China

Due to the intermittent, fluctuating and random characteristics of wind system, the output of wind power will become unstable with the change of wind, which brings severe challenges to the safe and stable operation of the power system. An effective way to solve this problem is to accurately forecast the wind speed. This paper presents a novel wind speed combination forecasting model based on decomposition. The innovation of the forecasting model is as follows. (a) In view of the unstable characteristics of wind speed, variational mode decomposition algorithm is introduced to decompose the historical wind speed data to obtain a series of stable components with different frequencies. (b) Echo state network with good forecasting ability is selected as the forecasting model of each component. (c) To solve the problem that the forecasting performance of echo state network is greatly affected by the parameters of the reservoir, an improved whale optimization algorithm is proposed to optimize these parameters. The optimized echo state network improves the forecasting effect. (d) The final forecasting results are obtained by adding the forecasting values of each component. (e) The performance of the developed forecasting model is verified by using two actual collected data sets of ultra-short-term wind speed and short-term wind speed. Compared with some state-of-theart forecasting models, the comparison result curve between the forecasting value and actual value of wind speed, the forecasting error distribution, the histogram of the forecasting error distribution, the performance indicators, related statistical indicators, and Taylor diagram show that the developed forecasting model has higher prediction accuracy and is able to reflect the change laws of wind speed correctly. (C) 2021 The Author(s). Published by Elsevier Ltd.

关键词： Wind speed Combination forecasting Variational mode decomposition Echo state network improved whale optimization algorithm

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Hybrid neural network control design for uncertain switched nonlinear systems with external disturbances: Application to ship maneuvering system

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2023年第12期45卷 2261-2275页

作者： Bali, Arun Singh, Uday Pratap Kumar, Rahul Shri Mata Vaishno Devi Univ Sch Math Katra 182320 Jammu & Kashmir India

In this work, the problem of hybrid neural network control for a class of switched uncertain nonlinear systems in strict-feedback form is considered. To approximate unknown nonlinear functions, an improved whale optimization algorithm (IWOA)-based hybrid neural network controller is introduced. Based on hybrid neural network approximation ability, a new hybrid neural network controller is constructed via the backstepping technique and the common Lyapunov function (CLF) is used for the stability analysis of the proposed model. The suggested method ensures that all signals in the closed-loop system are semi-global uniform ultimate bounded (SGUUB), and the tracking error converges to a small neighborhood of zero. Finally, the proposed scheme is applied to a ship maneuvering system and a numerical example to verify its effectiveness.

关键词： Hybrid neural network common Lyapunov function improved whale optimization algorithm switched nonlinear systems

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Parameter Identification of the RBF-ARX Model Based on the Hybrid whale optimization algorithm

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2024年第5期71卷 2774-2778页

作者： Li, Chuanjiang You, Chengtian Gu, Ya Zhu, Yanfei Shanghai Normal Univ Coll Informat Mech & Elect Engn Shanghai 201418 Peoples R China

The radial basis function network-based autoregressive with exogenous input (RBF-ARX) model is the nonlinear model based on state dependence with additional input. This brief discusses the parameter estimation of the model and utilizes it to solve the modeling and control problems of piezoelectric ceramics under pressure fluctuation. The system parameters are identified by the hybrid least squares whale optimization algorithm. Compared with the whale optimization algorithm to optimize the parameters of the RBF-ARX model, the proposed algorithm has a faster convergence rate and can avoid falling into the local optimal. Finally, the example is given to illustrate the optimization effect of the proposed algorithm, and the experimental simulation of piezoelectric ceramics under pressure fluctuation is carried out to verify the effectiveness of the proposed algorithm.

关键词： RBF-ARX model improved whale optimization algorithm least squares piezoelectric actuators

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Research and application of short-term load forecasting based on CEEMDAN-LSTM modeling

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ENERGY REPORTS 2024年 12卷 2144-2155页

作者： Liu, Hongli Li, Zhenyu Li, Chao Shao, Lei Li, Ji Tianjin Univ Technol Tianjin 300000 Peoples R China

Accurate short-term load forecasting plays a crucial role in guiding and regulating the operation of power companies. In this study, we propose a combined prediction model based on Long Short-Term Memory (LSTM) and improved whale optimization algorithm (IWOA) to enhance prediction accuracy and address the limitations of existing single methods. The parameter optimization of the LSTM model requires significant computing resources and time. To tackle this issue, we introduced the whale optimization algorithm (WOA) and improved the algorithm through the roulette method to form a whale optimization algorithm that enhances its global search capability and avoids falling into local optimal solutions. Additionally, in order to handle the nonlinearity and non-smoothness of the input data of the model, we utilized fully integrated Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) in this study to improve the efficiency of model training. By combining these techniques, we constructed a new CEEMDAN-IWOA-LSTM combined prediction model. The simulation results in MATLAB demonstrate that the prediction accuracy of this model reaches 99.05 %, which surpasses other prediction models in all evaluation indexes.

关键词： Power load forecasting Long and short-term memory neural networks improved whale optimization algorithm Adaptive noise-complete ensemble empirical modal decomposition

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A Quality Prediction Method for Dual-Nozzle FDM Molded Parts Based on CIWOA-BP

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FIBERS AND POLYMERS 2024年第5期25卷 1913-1923页

作者： Ji, Binghui Cui, Mengyang Mao, Jian Qian, Bo Shanghai Univ Engn Sci Coll Mech & Automot Engn Shanghai 201600 Peoples R China Shanghai Jiao Tong Univ Sichuan Res Inst Chengdu 610213 Peoples R China

The fused deposition molding (FDM) process can manufacture complex molded parts based on molten filaments. The quality of parts molded by this process is measured primarily by tensile strength, flexural strength, and surface roughness. Due to the complexity and variety of FDM process parameters, there is a nonlinear variation relationship between the combination of process parameters and the quality of molded parts, which makes the accurate and stable prediction of quality particularly important. To this end, an improved whale optimization algorithm combined with a neural network is proposed to predict the quality of FDM molded parts and make up for the shortcomings of the traditional methods with low prediction accuracy and instability. Based on the FDM production process, four process parameters, namely, printing layer thickness, nozzle temperature, chamber temperature, and filling interval, were selected as input variables of the model. The prediction model was obtained by optimizing the hyperparameters of the neural network using the improved whale algorithm. A four-factor three-level experimental method was used to obtain experimental data on the tensile strength, bending strength, and surface roughness of the styles. The accuracy and stability of the model were verified according to Monte Carlo cross-validation based on the above data set. The prediction results of model CIWOA-BP are compared with those of model WOA-BP and BP in a comparison experiment. The results show that the FDM part quality prediction method based on the CIWOA-BP model has higher prediction accuracy and stability.

关键词： Fused deposition modeling Quality prediction improved whale optimization algorithm Backpropagation neural network MATLAB

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Integrity management system of hydrogen pipeline based on big data mining model optimized by improved whale algorithm

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JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS 2024年第8期47卷 913-923页

作者： Qin, Dou Gao, Diankui Zhao, Bin Liaoning Petrochem Univ Sch Mech Engn Fushun 113001 Liaoning Peoples R China

In order to improve the integrity management level of the hydrogen pipeline, the intelligent information management system of the hydrogen pipeline based on big data intelligent mining model is proposed. The improved whale optimization algorithm is proposed by applying the adaptive weight update formula and quantum revolving door operation. Finally, a hydrogen pipeline is selected to carry out abnormal data detection, and the running time with different nodes and different clustering algorithms optimized by different intelligent algorithms is obtained, and the results show that the proposed Gaussian clustering optimized by an improved whale optimization algorithm can effectively find out abnormal data of integrity management of hydrogen pipeline. Monitoring precision of the proposed model is improved effectively, and the running time of the proposed model is also shortened;therefore, the proposed model can be an effective means for integrity management of the hydrogen pipeline.

关键词： Hsiau Shu-San Hydrogen pipeline integrity management big data Gaussian clustering improved whale optimization algorithm

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Intermittent Fault Diagnosis and Prognosis for Steer-by-Wire System Using Composite Degradation Model

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IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS 2023年第2期13卷 557-571页

作者： Yu, Ming Wang, Zhicheng Wang, Hai Jiang, Wuhua Zhu, Rensheng Hefei Univ Technol Sch Elect Engn & Automat Hefei 230009 Peoples R China Murdoch Univ Sch Engn & Energy Perth WA 6150 Australia Hefei Univ Technol Sch Automot & Transportat Engn Hefei 230009 Peoples R China Hefei Univ Technol Sch Mech Engn Hefei 230009 Peoples R China

For automotive manufacturers, intelligent fault diagnosis and prognosis techniques to ensure reliability of their products become increasingly important with the introduction of Industry 4.0. In this paper, an intelligent intermittent fault diagnosis and prognosis method is proposed for steer-by-wire (SBW) system based on composite degradation model. First, a nonlinear bond graph model of the SBW system is established, by which an integrated fault signature matrix combining analytical redundancy relations and dedicated observers is developed to enhance the fault isolability. Second, in order to identify the features (i.e., fault appearing and disappearing instants) of each intermittent fault, an improved whale optimization algorithm is proposed by introducing nonlinear convergence factor, Cauchy-Gaussian mixture mutation and greedy selection strategy. After that, the composite degradation model is established to predict the remaining useful life of the component with intermittent fault, where duration feature and frequency feature are considered with the aid of observation window. Finally, the effectiveness of the proposed approach is validated by simulation and experimental results.

关键词： Steer-by-wire system intermittent fault integrated fault signature matrix improved whale optimization algorithm composite degradation model Industry 4.0

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Detection of faults and DG islanding in PV-Wind DC ring bus microgrid by using optimized VMD based improved broad learning system

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ISA TRANSACTIONS 2022年 131卷 533-551页

作者： Anjaiah, Kanche Dash, P. K. Sahani, Mrutyunjaya Siksha O Anusandhan Deemed Be Univ Bhubaneswar Odisha India

This paper presents a novel approach for the detection and classification of photovoltaic with wind based DC ring bus microgrid DC faults and DG (distributed generation) islanding events. This novel approach consists of adaptive variational mode decomposition (AVMD) and an improved broad learning system (IBLS). Initially, DC fault current signals are captured from the DC bus under different operating conditions and processed through the AVMD to decompose the signals into intrinsic mode functions (IMFs). The VMD is made adaptive by minimizing the objective function of the L-kurtosis index for optimal modal number (K) and penalty factor (Sigma) through the improved whale optimization (IWO) algorithm. From the optimal IMFs, the most significant IMFs are chosen based on the threshold of the L-kurtosis index, and they are passed through statistical features to extract efficient data. Further, the training and testing of this data set is carried out through IBLS for obtaining the accurate detection and discrimination of DC faults. The conventional BLS method is improved through elastic net ridge regression for calculating the weights. The effectiveness of the proposed AVMD based IBLS algorithm is verified by its superiority in terms of relative computation time (RCT), classification accuracy (CA) with the confusion matrix, and their performance indices by comparing with other existing methods under different case studies. Finally, the simplicity and practicability of the proposed work are tested and implemented in the dSPACE 1104 embedded processor.(c) 2022 Published by Elsevier Ltd on behalf of ISA.

关键词： Adaptive variational mode decomposition DC-ring microgrid DG islanding Fault detection Faults classification improved broad learning system improved whale optimization algorithm dSPACE interface

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