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State of health estimation of lithium-ion batteries based on multiple health factors and BO-Seq2Seq model

COMPUTERS & ELECTRICAL ENGINEERING 2024年 116卷

作者： Wang, Qilin Xie, Song Guo, Wenqi Li, Guishu Lv, Pengfei Civil Aviat Flight Univ China Coll Civil Aviat Safety Engn Guanghan 618307 Sichuan Peoples R China Civil Aircraft Fire Sci & Safety Engn Key Lab Sich Guanghan 618307 Sichuan Peoples R China Sichuan Key Technol Engn Res Ctr All Elect Navigab Guanghan 618307 Sichuan Peoples R China

State of health (SoH) is a key indicator for evaluating the degradation level of lithium-ion batteries (LIBs), and its accurate prediction is crucial for the reliability and safety of battery management systems. This paper proposes an improved sequence-to-sequence (Seq2Seq) model for SoH estimation based on the selection of relevant multiple types of health factors (HFs). In order to better describe the SoH of LIBs, Pearson's correlation coefficient is employed to select HFs that is highly correlated with the SoH. The hyperparameter optimization method based on bayesian optimization (BO) is used to solve the challenge of hyperparameter selection in Seq2Seq models. This method effectively utilizes the strong global search ability of BO to obtain the global optimal solution. The proposed method is validated using a dataset provided by NASA. The results show that the BO-Seq2Seq model proposed in this work exhibits competitive accuracy in estimation performance, with an error of less than 1 %. Compared with methods that only consider a single HF, the method that considers multiple HFs in this work significantly improves the accuracy of estimation. The estimation performance indicators of the proposed BO-Seq2Seq model, such as the root mean square errors are all within 1 %, the mean absolute errors are all within 0.5 % and the mean square errors are all within 0.004 %, are significantly lower than other methods.

关键词： Lithium-ion battery bayesian optimization algorithm Sequence-to-sequence Multiple health factors State of health

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Forecasting the hydropower unit vibration based on adaptive variational mode decomposition and neural network

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APPLIED SOFT COMPUTING 2024年 150卷

作者： Lu, Zhaoheng Tao, Ran Xiao, Ruofu Li, Puxi China Agr Univ Coll Water Resources & Civil Engn Beijing 100083 Peoples R China China Agr Univ Beijing Engn Res Ctr Safety & Energy Saving Techno Beijing 100083 Peoples R China State Key Lab Hydropower Equipment Harbin 150046 Peoples R China

The feature extraction, diagnosis and prediction of vibration and fault are important to maintain the stable operation of units with the large-scale popularization of virtual power plants in hydropower plants. Therefore, more accurate and suitable state detection methods and forecasting models for complex conditions are needed. VMD is limited by the presetting of mode number, penalty factor and initial center frequency, which greatly reduces the accuracy of the algorithm. In this study, IAVMD algorithm is proposed. The initial center frequencies and the mode number can be determined by the scale space plane and the energy loss coefficient. The numerical model of penalty factor is constructed based on center frequency differences. Finally, the mode numbers are combined and adjusted by autocorrelation and other methods. Through comparative analysis of simulation signal and experimental signal, IAVMD shows great improvements in complex signals. At the same time, it is proved that the forecasting model combining IAVMD with BO and LSTM has significantly improved the prediction accuracy. Through comparative analysis, the results prove that the prediction model constructed by this method has higher accuracy.

关键词： Variational mode decomposition Signal analysis Operation status detection bayesian optimization algorithm Long short-term memory network

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Research on mine water source classifications based on BO-CatBoost

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2024年第10期196卷 876-876页

作者： Li, Han Yang, Zhenwei Lv, Hang Wang, Man Zhang, Bo Yan, Hengrui Zhang, Hongyu Henan Polytech Univ Inst Resources & Environm Jiaozuo 454000 Peoples R China Collaborat Innovat Ctr Coal Work Safety & Clean Hi Jiaozuo 454000 Peoples R China China Pingmei Shenma Holding Grp Co Ltd Pingdingshan 467000 Henan Peoples R China

Mine water surge is one of the main safety risks in coal mines. This research offers a novel mine water source identification model (BO-CatBoost) to successfully avoid and control mine sudden water catastrophes by properly identifying the sources of mine water. First, the classification model is trained and built using the Categorical Boosting (CatBoost) algorithm. The Gaussian process bayesian optimization (BO) algorithm is used to optimize parameters, and the optimal parameter combination is integrated into the CatBoost algorithm to build the BO-CatBoost mine water source identification model, which further improves the accuracy of mine water source identification. The model was also applied to the Pingdingshan mine to verify the practicality of the model. Then, 29 groups of unknown water sources in Pingdingshan were selected as validation samples for the model and compared with the conventional CatBoost, Light Gradient Boosting Machine (LightGBM), and Extreme Gradient Boosting (Xgboost) models. The comparison results demonstrate that the accuracy of LightGBM, Xgboost, CatBoost, and BO-CatBoost models can reach 69%, 79.3%, 79.3%, and 100% respectively, and the RMSE is 0.947, 0.643, 0.719, and 0.0 respectively. The comprehensive analysis shows that, when it comes to mine water source detection, the BO-CatBoost model performs noticeably better than other models in terms of discriminative accuracy and generalization capacity. Lastly, the multi-output prediction and decision-making process of the BO-CatBoost water source identification model is visualized by the interpretability analysis performed with the SHAP approach. The research demonstrates that the BO-CatBoost model can more precisely and impartially identify mine water sources, offering fresh concepts for mine water source detection.

关键词： Mine water source Pingdingshan coalfield CatBoost SHAP bayesian optimization algorithm

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State of health estimation for lithium-ion batteries with bayesian optimized Gaussian process regression

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2024年

作者： Ye, Li-hua He, Zhou Ke, Cheng-long Cheng, Xing Zhao, Xu Zhang, Zi-xing Shi, Ai-ping Jiangsu Univ Sch Automot & Transportat Engn 301 Xuefu Rd Zhenjiang Jiangsu Peoples R China

The estimation of the state of health (SOH) of lithium-ion batteries is of great importance to ensure the safe and stable operation of a lithium-ion battery management system (BMS). Accurate estimation of SOH can effectively prevent unnecessary losses of lithium-ion batteries due to failures. Therefore, a bayesian-optimized Gaussian process regression (BO-GPR) method is proposed for SOH estimation. Firstly, the ageing characteristics reflecting the SOH of the battery are extracted from the charge/discharge time and incremental capacity (IC) curve, and the correlation between the health characteristics and the SOH is analysed by means of grey correlation (GRA). On the other hand, a bayesian optimization algorithm is used to automatically find the optimal hyperparameters of the GPR model and trained using the battery dataset to obtain the SOH estimating model. To validate the effectiveness of the method, the datasets provided by NASA and Oxford are used as experimental objects and compared with different data-driven models to verify the accuracy and reliability of the proposed model. The experimental results show that the method proposed in this paper has a high accuracy in the estimation of the SOH, with the maximum average estimation error being within 1%.

关键词： Lithium-ion battery state of health bayesian optimization algorithm Gaussian process regression

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A Novel Malware Detection Model in the Software Supply Chain Based on LSTM and SVMs

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APPLIED SCIENCES-BASEL 2024年第15期14卷 6678页

作者： Zhou, Shuncheng Li, Honghui Fu, Xueliang Jiao, Yuanyuan Inner Mongolia Agr Univ Coll Comp & Informat Engn Hohhot 010018 Peoples R China

With the increasingly severe challenge of Software Supply Chain (SSC) security, the rising trend in guarding against security risks has attracted widespread attention. Existing techniques still face challenges in both accuracy and efficiency when detecting malware in SSC. To meet this challenge, this paper introduces two novel models, named the bayesian optimization-based Support Vector Machine (BO-SVM) and the Long Short-Term Memory-BO-SVM (LSTM-BO-SVM). The BO-SVM model is constructed on an SVM foundation, with its hyperparameters optimized by bayesian optimization. To further enhance its accuracy and efficiency, the LSTM-BO-SVM model is proposed, building upon BO-SVM and employing LSTM networks for pre-classification. Extensive experiments were conducted on two datasets: the balanced ClaMP dataset and the unbalanced CICMalDroid-2020 dataset. The experimental results indicate that the BO-SVM model is superior to other models in terms of accuracy;the accuracy of the LSTM-BO-SVM model on the two datasets is 98.2% and 98.6%, respectively, which is 2.9% and 2.2% higher than that of the BO-SVM on these two datasets.

关键词： software supply chain malware detection long short-term memory network bayesian optimization algorithm support vector machine

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Efficient Amplification of Large-Signal Spoof Surface Plasmon Polaritons in a Subwavelength Size

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ADVANCED MATERIALS TECHNOLOGIES 2024年第5期9卷

作者： Liao, Zhen Wu, Shaofei Guo, Jia Liu, Leilei Cai, Jialin Nanjing Univ Posts & Telecommun Coll Elect & Opt Engn Natl & Local Joint Engn Lab RF Integrat & Microass Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Coll Flexible Elect Future Technol Nanjing 210023 Peoples R China Hangzhou Dianzi Univ Key Lab RF Circuit & Syst Minist Educ Hangzhou 310018 Peoples R China Hangzhou Dianzi Univ Zhejiang Key Lab Large Scale Integrated Circuit De Hangzhou 310018 Peoples R China

The advancement of communication systems demands ever-smaller sizes, leading to the integration of more circuits in compact forms. A promising solution lies in Spoof Surface Plasmon Polaritons (SSPPs), which enable strong confinement, low interference, and a significant wave vector, thereby facilitating smaller sizes. However, the amplification of large plasmonic signals in a compact size remains a daunting scientific challenge for the further development of SSPPs communication systems. In this study, this challenge is tackled by selecting GaN High Electron Mobility Transistors (HEMTs) and load/source-pull technologies. To ensure efficient operation, impedance-matching networks are designed, resulting in a remarkably small amplifier structure. The approach involves utilizing the bayesian optimization (BO) algorithm to fine-tune the matching network, leading to exceptional amplification performance. The results obtained in the frequency range of 1-3 GHz are highly promising, with both simulated and measured results demonstrating an impressive output power exceeding 40 dBm, a power added efficiency (PAE) surpassing 60%, and a gain exceeding 10 dB. These remarkable achievements underscore the viability of amplifying large-signal SSPPs and outputting high-power SSPPs waves within a subwavelength size, opening exciting possibilities for SSPPs communication, sensing, and imaging systems. A novel design of a high-power SSPPs amplifier at 1-3GHz utilizing HEMT is presented. The optimized structure is simulated, fabricated, and measured, resulting in an impressive output power exceeding 40 dBm, a PAE surpassing 60%, and a gain exceeding 10 dB. These results dvalidate the proposed design for amplifying large-signal SSPPs and its practical realization for high-power SSPPs ***

关键词： bayesian optimization algorithm power amplification spoof surface plasmon polaritons

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Explainable highway performance degradation prediction model based on LSTM

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ADVANCED ENGINEERING INFORMATICS 2024年 61卷

作者： Sun, Xin Wang, Honglei Mei, Shilong Guizhou Univ Sch Management Guiyang 550025 Peoples R China Guizhou Minzu Univ Architectural Engn Coll Guiyang 550025 Peoples R China Key Lab Internet Collaborat Intelligent Mfg Guizho Guiyang 550025 Peoples R China Guizhou Univ Coll Architecture & Urban Planning Guiyang 550025 Peoples R China

With the dense and huge highway network in China, the highway maintenance management system has become the most concerned issue for Chinese highway managers in recent years. Therefore, based on the highway network in Guizhou Province of China, this paper established the semi-rigid asphalt pavement performance multi-output Long Short-Term Memory (LSTM) prediction model with regional applicability, including Pavement Surface Condition Index (PCI), Pavement Rutting Depth Index (RDI), Pavement Riding Quality Index (RQI) and Pavement Skidding Resistance Index (SRI) and the hyperparameters of the model were optimized by bayesian optimization algorithm and the casual analysis of the model was conducted based on SHapley Additive exPlanations (SHAP), providing a reference for China's highway network-level preventive maintenance decisionmaking. The results prove that the established model can effectively predict the performance of the following year through the data of the previous two years. The established model has a better comprehensive prediction effect in comparison to similar multi-output models. The average coefficient of determination (R2) of the four prediction indexes can reach 0.823. Besides, the prediction effect among the indexes is stable and multiple pavement performance indexes can be effectively and reliably predicted at the same time. In addition, the contribution of input feature variables to output is quantified based on SHAP. According to quantified the contribution, the association relationship of the neural network model is set-valued mapped to the causality relationship in the post hoc. The input feature variables with the main contribution are extracted for causal analysis. The causal analysis shows that the degradation of the four pavement performances all have obvious time memory characteristics and have significant differences, but their pavement performance degradation is the result of nonlinear changes caused by the coupling effects of traffic l

关键词： Long Short-Term Memory (LSTM) Highway performance degradation bayesian optimization algorithm SHapley Additive exPlanations (SHAP) Explainable prediction model

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Multi-objective optimization of environmental tax for mitigating air pollution and greenhouse gas

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Journal of Management Science and Engineering 2022年第3期7卷 473-488页

作者： Sijing Li Ning Jia Zhenni Chen Huibin Du Zengkai Zhang Bomin Bian College of Management and Economics Tianjin UniversityTianjin300072China School of Economics and Finance Xi'an Jiaotong UniversityXi'an710061China Institute of Operations Research and Analytics National University of Singapore117602Singapore

Government macro-control through various policies is an important way to mitigate air pollution and greenhouse ***,environmental tax is used worldwide as an important ***,few studies have considered the interaction between carbon and environmental protection ***,different sectors differ in their energy structure,pollution emission intensity,and economic status,and previous studies rarely proposed differentiated environmental tax rates based at the sectoral level.A model framework combining the computable general equilibrium(CGE)model and bayesian optimization(BO)algorithm is proposed to maximize GDP,meet environmental planning objectives,and explore the optimal environmental taxation scheme to realize the multi-objective optimization of the economy and ***,this study compares the different impact mechanisms of environmental protection tax and carbon *** discusses the impacts of differentiated environmental tax rates in different sectors on the environment and *** example,the results show that the coordinated implementation of environmental protection and carbon tax policies and the sectoral differentiated environmental tax rates in China could better balance economic development and environmental ***,the optimal taxation scheme could mitigate air pollution and greenhouse gases,promote economic growth,and realize sustainable economic and environmental ***,the optimized taxation scheme positively affects the energy and industrial structures.

关键词： Computable general equilibrium bayesian optimization algorithm Environmental tax Multi-objective optimization Air pollution Greenhouse gas

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Multi-objective optimization for oil-gas production process based on compensation model of comprehensive energy consumption using improved evolutionary algorithm

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ENERGY EXPLORATION & EXPLOITATION 2021年第1期39卷 273-298页

作者： Liu, Tan Yuan, Qinyun Wang, Lina Wang, Yonggang Zhang, Nannan Shenyang Agr Univ Coll Informat & Elect Engn Shenyang Peoples R China Liaoning Engn Res Control Informat Technol Agr Shenyang Peoples R China China Jiliang Univ Coll Mech & Elect Engn Hangzhou Peoples R China

This paper establishes an error compensation multi-objective optimization model of oil-gas production process for optimizing these production indices, including overall oil production, overall water production and comprehensive energy consumption per ton of oil. In order to reduce the error between the model output and the actual value of comprehensive energy consumption per ton of oil, combining the mechanism model with least squares support vector machine (LS-SVM) error model optimized by bayesian optimization algorithm (BOA), a hybrid model is established to predict the comprehensive energy consumption, in which the mechanism model is used to describe the overall characteristics of oil-gas production process, and LS-SVM error model is established to compensate the mechanism model error. Then, in order to improve the performance of Pareto non-dominated solutions, an improved non-dominated sorting genetic algorithm-II with multi-strategy improvement (IMS-NSGA-II) is proposed to solve the error compensation multi-objective optimization model. Finally, the effectiveness and superiority of the the proposed optimization method are verified by the experiment results on some stand test problems and the optimization problem for the oil-gas production process in a block of an oil production operation area.

关键词： Oil-gas production process LS-SVM error model bayesian optimization algorithm error compensation NSGA-II with multi-strategy improvement (IMS-NSGA-II)

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HTS-SLIM design based on bayesian multi-level, multi-objective optimization and Gaussian process models

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PHYSICA C-SUPERCONDUCTIVITY AND ITS APPLICATIONS 2021年 591卷 1353970-1353970页

作者： Ahmadpour, Ali Dejamkhooy, Abdolmajid Shayeghi, Hossein Univ Mohaghegh Ardabili Dept Elect Engn Ardebil Iran Univ Mohaghegh Ardabili Energy Management Res Ctr Ardebil Iran

The performance of the Linear Induction Motor (LIM) is based on the linear force in the moving direction. However, due to the length limitation of LIMs, the end-effects as the barrier force decrease their performance. On the other hand, the primary width is lower than the secondary that causes the non-homolographic distribution of the air gap flux density. These effects result in the reduction of output thrust. In recent decades, the windings are made of High-Temperature Superconducting (HTS) tapes to reduce their power losses and total weight. However, due to their magnetic losses, the HTS-LIMs generally are low efficiency and need an optimal design. In this paper, an optimal design of an HTS Single-sided LIM (SLIM) is conducted by analyzing the proposed equivalent circuit parameters. Also, the output variables of the HTS-SLIM are calculated using the proposed analysis, which is different from the conventional SLIMs. The bayesian optimization algorithm (BOA) is used to optimize the objective functions, which consists of the efficiency, power factor, braking force due to end effects, and AC losses. Moreover, various models of the Gaussian Process (GP) are employed for modeling the objective functions at each step of the BOA process. Since the problem construction is multi-objective, the optimization algorithm is modified as the Multi-Objective BOA (MO-BOA) to optimize the mentioned objectives simultaneously. The problem is also considered as a multi-level problem owing to the various levels based on the sensitive analysis of HTS-SLIM parameters. For increasing the results accuracy, different effects of SLIM such as end-, skin-, and edge effects are studied in the analytical model. The results of different models of GP and other methods are compared to show the robustness of the proposed method. Also, a 3D model of the motor is built and analyzed with the Finite Element Method (FEM) to verify the results of the analytical approach. The results for both FEM and analyt

关键词： Linear induction motors High-temperature superconducting Multi-objective optimization bayesian optimization algorithm Gaussian proces Multi-level optimization

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