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State of health estimation of lithium-ion batteries based on the kepler optimization algorithm-multilayer-convolutional neural network

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JOURNAL OF ENERGY STORAGE 2025年 122卷

作者： Wu, Muyao Zhang, Xi Wang, Zhongchun Tan, Changpeng Wang, Yuqing Wang, Li Hefei Univ Technol Sch Automot & Transportat Engn Hefei 230009 Peoples R China Jilin Univ Coll Automot Engn Changchun 130015 Peoples R China Hefei Univ Technol Sch Mech Engn Hefei 230009 Peoples R China

Lithium-ion batteries are widely used in electric vehicles (EVs), and accurate SOH estimation is essential for ensuring EV safety. This paper proposes a novel SOH estimation method based on the kepler optimization algorithm-multilayer-convolutional neural network. Firstly, the extracted health indicators (HIs) are filtered, and highly correlated, continuous HIs are identified using Pearson correlation coefficient and scatter plots. Subsequently, the ReliefF algorithm is employed for further dimensionality reduction. Subsequently, a multilayerconvolutional neural network is constructed for SOH estimation, with the kepler optimization algorithm (KOA) for hyperparameter optimization, a novel application according to the authors' knowledge. The SOH estimation results demonstrate that, a deeper CNN does not necessarily yield better results and the KOA-2-layerCNN performs the best. Additionally, compared with the 2-layer-CNN without hyperparameters optimization, the mean absolute error (MAE), the root mean square error (RMSE), maximum absolute error (Max-AE) of the KOA2-layer-CNN are decreased by 58.97 %, 53.33 %, 39.05 %, respectively. Moreover, compared with commonly used SOH estimation methods based on feature engineering, the KOA-2-layer-CNN also achieves accurate SOH estimation results, with significantly smaller MAE, RMSE, and Max-AE.

关键词： lithium-ion batteries State of health Health indicators Multilayer-convolutional neural network kepler optimization algorithm

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CGKOA: An enhanced kepler optimization algorithm for multi-domain optimization problems

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2024年 425卷

作者： Hu, Gang Gong, Changsheng Li, Xiuxiu Xu, Zhiqi Xian Univ Technol Dept Appl Math Xian 710054 Peoples R China Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China

kepler optimization algorithm (KOA) is a physically based meta-heuristic algorithm inspired by kepler's laws to simulate planetary motions, KOA shows strong performance on different test sets as well as various optimization problems. However, it also suffers from imbalanced exploration and exploitation, delayed convergence, and insufficient convergence accuracy in dealing with high-dimensional and complex applications. To address these shortcomings, this paper proposes an enhanced kepler optimization algorithm called CGKOA with stronger performance by combining adaptive function, sinusoidal chaotic gravity, lateral crossover, and elite gold rush strategies. Firstly, the adaptive function and sinusoidal chaotic gravity are adjustments to the internal structure of KOA algorithm, which successfully balances the exploration and exploitation, and increases the population diversity. Secondly, the lateral crossover strategy strengthens the spatial exploration ability of the algorithm, eliminates the poor quality individuals and accelerate the output of high-quality population, and finally, the proposed elite gold rush strategy provides an in-depth and rational exploration of elite groups from multiple perspectives, improves solving accuracy and accelerates the convergence speed. Experimental comparisons of CGKOA with a variety of state-of-the-art and high-performance algorithms on different dimensions of the 2017 and 2020 test sets are conducted, and the experimental results show the superiority and robustness of CGKOA algorithm. In addition, the effectiveness and practicability of CGKOA for real problems are verified by solving 50 complex engineering applications. Last, the algorithm is applied to the difficult problems in the fields of path planning, job-shop scheduling, variant travelers, robot machining trajectory planning, and complex truss topology optimization, and the excellent results obtained by CGKOA demonstrate its applicability and development potential for op

关键词： kepler optimization algorithm Adaptive function Sinusoidal chaotic gravity Lateral crossover Elite gold rush Realistic constraint problem Path planning

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Fault diagnosis in hydropower units based on chaotic kepler optimization algorithm-enhanced BiLSTM model

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ENERGY REPORTS 2024年 12卷 5163-5176页

作者： Chen, Yujia Wang, Jianlan Chen, Weidong Dao, Fang Zeng, Yun Lv, Shunli Hubei Technol Innovat Ctr Smart Hydropower Wuhan 430000 Peoples R China Kunming Univ Sci & Technol Fac Met & Energy Engn Kunming 650093 Peoples R China

Safe and stable operation of hydropower units is the cornerstone of the whole hydroelectric power generation system. This paper proposes a deep learning model based on the chaotic kepler optimization algorithm (CKOA) for fault diagnosis of hydropower units. The Tent chaotic function is introduced to initialize the initial population of KOA, which accelerates the convergence speed of the KOA algorithm and improves the global search capability by improving the uniformity and uncertainty of the population. CKOA is used to optimize the hyperparameters of Bidirectional Long Short-term Memory (BiLSTM) to improve the robustness and generalization ability of the model, making it suitable for dealing with complex nonlinear fault signals of hydropower units. The experimental results show that the training accuracy and testing accuracy of the CKOA-BiLSTM model are 97.6 % and 98.3%, respectively, which are better than those of the LSTM, BiLSTM, and KOA-BiLSTM models. Meanwhile, diagnosing faults from the acoustic point of view, the accuracy of impact faults in hydropower units is much higher than that of wear and tear faults. This study can serve as a valuable supplement to the existing hydropower units condition monitoring and fault diagnosis system.

关键词： Hydropower unit kepler optimization algorithm Fault diagnosis BiLSTM

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A kepler optimization algorithm improved using a novel Levy-Normal mechanism for optimal parameters selection of proton exchange membrane fuel cells: A comparative study

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ENERGY REPORTS 2024年 11卷 6109-6125页

作者： Abdel-Basset, Mohamed Mohamed, Reda Sallam, Karam M. Alsekait, Deema Mohammed Abdelminaam, Diaa Salama Zagazig Univ Fac Comp & Informat Zagazig 44519 Egypt Univ Canberra Fac Sci & Technol Sch IT & Syst Canberra Australia Princess Nourah Bint Abdulrahman Univ Appl Coll Dept Comp Sci & Informat Technol POB 84428 Riyadh 11671 Saudi Arabia Appl Sci Private Univ Appl Sci Res Canter Amman Jordan Benha Univ Fac Comp & Artificial Intelligence Informat Syst Dept Banha 13511 Egypt Middle East Univ MEU Res Unit Amman 11831 Jordan Misr Int Univ Fac Comp Sci Cairo 11800 Egypt

Proton exchange membrane fuel cells (PEMFCs) are considered a promising renewable energy source and have sparked a lot of interest over the last few years due to their robust efficiency, low operating temperature, and longevity. The PEMFC's electrochemical model has seven unknown parameters, which are not given in the manufacturer's datasheets and need to be accurately estimated to present a more accurate model, leading to improved efficiency and performance of the PEMFC systems. The estimation of those unknown parameters has been dealt with as a complex and non-linear optimization problem that needs a powerful optimization algorithm to solve it. The existing optimization algorithms still have some disadvantages, such as falling into local minima and low convergence speed, which make them ineligible to solve this complicated problem with acceptable accuracy and low computational cost. Therefore, this study presents a new parameter estimation technique for estimating the unknown parameters of the PEMFC model more accurately, thereby achieving precise modeling of PEMFCs. This technique called IKOA is based on integrating the kepler optimization algorithm (KOA) with a novel Levy-Normal (LN) mechanism to strengthen its exploration and exploitation capabilities against this multimodal optimization problem. The Levy flight in this mechanism aims to improve the KOA's exploitation operator to accelerate the convergence speed toward the near-optimal solution, thus minimizing the computational cost;meanwhile, the normal distribution is used to strengthen its exploration operator, thereby aiding in the escape of local minima. The proposed IKOA and KOA are herein evaluated against several rival algorithms using six well-known commercial PEMFC stacks to highlight their efficiency and effectiveness. Key performance metrics such as computational cost, fitness measures, and statistical validation through the Wilcoxon rank-sum test are herein used to high

关键词： kepler optimization algorithm Proton exchange membrane fuel cell Modeling Levy-Normal improvement strategy

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Maximum Power Point Tracking Based on Improved kepler optimization algorithm and Optimized Perturb&Observe under Partial Shading Conditions

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Energy Engineering 2024年第12期121卷 3779-3799页

作者： Zhaoqiang Wang Fuyin Ni School of Electrical and Information Engineering Jiangsu University of TechnologyChangzhou213001China Jiangsu Key Laboratory of Power Transmission&Distribution Equipment Technology JiangsuUniversity of TechnologyChangzhou213001China

Under the partial shading conditions(PSC)of Photovoltaic(PV)modules in a PV hybrid system,the power output curve exhibits multiple *** often causes traditional maximum power point tracking(MPPT)methods to fall into local optima and fail to find the global *** address this issue,a composite MPPT algorithm is *** combines the improved kepler optimization algorithm(IKOA)with the optimized variable-step perturb and observe(OIP&O).The update probabilities,planetary velocity and position step coefficients of IKOA are nonlinearly and adaptively *** adaptation meets the varying needs of the initial and later stages of the iterative process and accelerates *** stochastic exploration,the refined position update formulas enhance diversity and global search *** improvements in the algorithmreduces the likelihood of falling into local *** the later stages,the OIP&O algorithm decreases oscillation and increases *** with cuckoo search(CS)and gray wolf optimization(GWO),simulation tests of the PV hybrid inverter demonstrate that the proposed IKOA-OIP&O algorithm achieves faster convergence and greater stability under static,local and dynamic shading *** results can confirm the feasibility and effectiveness of the proposed PV MPPT algorithm for PV hybrid systems.

关键词： PV hybrid inverter kepler optimization algorithm maximum power point tracking perturb and observe

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An Improved Hybrid CNN-LSTM-Attention Model with kepler optimization algorithm for Wind Speed Prediction

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ENGINEERING LETTERS 2024年第10期32卷 1957-1965页

作者： Huang, Yuesheng Li, Jiawen Li, Yushan Lin, Routing Wu, Jingru Wang, Leijun Chen, Rongjun Guangdong Polytech Normal Univ Sch Comp Sci Guangzhou 510665 Peoples R China

The flexibility and intermittency of wind speed is a crucial factor for its prediction. Research in this domain necessitates integrating diverse data to enhance model precision and reliability to meet the growing demand for wind energy in both the energy sector and the global economy. To this end, this work adopts the kepler optimization algorithm (KOA), which utilizes the position of each planet as a candidate solution. The optimization process then involves random updates to these candidate solutions, aiming to enhance wind speed prediction using the CNN-LSTM-Attention model. Specifically, it enhances the interpretability of the KOA for the global optimization problem. By using 1800 time moments of wind speed data with multidimensional features, the proposed hybrid model with KOA has been shown to outperform other optimization methods, demonstrating impressive performances in terms of mean absolute percentage error (MAPE), mean absolute error (MAE), runtime, root mean square error (RMSE), and R2. 2 . Hence, these results indicate its potential for improving wind speed prediction, which could open a novel direction in this field.

关键词： Deep learning kepler optimization algorithm CNN-LSTM-Attention Hybrid model Wind speed prediction

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A kepler optimization algorithm-Based Convolutional Neural Network Model for Risk Management of Internet Enterprises

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第7期15卷 210-210页

作者： Liu, Bin Zhou, Fengjiao Jiang, Haitong Ma, Rui Chengdu Univ Tradit Chinese Med Sch Management Chengdu Peoples R China Univ Sains Malaysia Sch Social Sci Gelugor Pinang Malaysia Sichuan Vocat Coll Culture & Commun Sch Cultural Management Chengdu 611230 Peoples R China Chengdu Univ Tradit Chinese Med Hlth & Rehabil Coll Chengdu Peoples R China

Internet enterprises, as the representative enterprises of technology-based enterprises, contribute more and more to the growth of the world economy. To ensure the sustainable development of enterprises, it is necessary to predict the risks in the operation of Internet enterprises. An accurate risk prediction model can not only safeguard the interests of enterprises but also provide certain references for investors. Therefore, this study designed a Convolutional Neural Network (CNN) model based on the kepler optimization algorithm (KOA) for risk prediction of Internet enterprises, aiming to maximize the accuracy of the prediction model, and to help Internet enterprises carry out risk management. Firstly, we select the indicators related to the financial risk of Internet enterprises, and predict the risk based on the traditional statistical analysis of Logistic regression model. On this basis, KOA was improved based on evolutionary strategies and fish foraging strategies, and the improved algorithm was applied to optimize CNN. Based on improved KOA and CNN algorithms, an IKOA-CNN risk prediction model is proposed. Finally, by comparing traditional statistical analysis-based models and other learning-based models, the results show that the IKOA-CNN algorithm proposed in this study has the highest prediction accuracy.

关键词： Risk management kepler optimization algorithm Convolutional Neural Network Internet enterprises

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Enhanced Binary kepler optimization algorithm for effective feature selection of supervised learning classification

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JOURNAL OF BIG DATA 2025年第1期12卷 1-67页

作者： Abd El-Mageed, Amr A. Abohany, Amr A. Hosny, Khalid M. Sohag Univ Dept Informat Syst Sohag 82511 Egypt Damanhour Univ Fac Comp & Informat Damanhour Egypt Zagazig Univ Fac Comp & Informat Dept Informat Technol Zagazig 44519 Egypt

This study proposes an Enhanced Binary kepler optimization algorithm (BKOA-MUT) improves feature selection (FS) by integrating kepler's planetary motion laws with DE/rand and DE/best Mutation Approach. BKOA-MUT balances exploration and exploitation, effectively guiding search for optimal feature subsets. BKOA-MUT was evaluated using k-Nearest Neighbors (k-NN) and Support Vector Machine (SVM) on 25 UCI benchmarks, including three large-scale ones. It outperformed recent Meta-heuristic algorithms (MHAs) in accuracy, feature reduction, and computational efficiency. The algorithm showed rapid convergence, minimal feature selection, and scalability, making it a robust and adaptable tool for enhancing FS in machine learning, validated through the Wilcoxon rank-sum test.

关键词： Feature selection Meta-heuristic algorithms kepler optimization algorithm DE/rand and DE/best Mutation Approach Machine learning classification

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Improved kepler optimization algorithm Based on Mixed Strategy 15th

Improved Kepler Optimization Algorithm Based on Mixed Strate...

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15th International Conference on Advances in Swarm Intelligence (ICSI)

作者： Li, Jiacheng Noto, Masato Zhang, Yang Kanagawa Univ Dept Appl Syst & Math Yokohama Kanagawa Japan Kanagawa Univ Dept Comp Sci Yokohama Kanagawa Japan

ISBN: (纸本)9789819771806;9789819771813

The original kepler optimization algorithm (KOA) is characterized by slow convergence speed, weak global search capability, low solution accuracy, and susceptibility to local optima. In this paper, we propose MSKOA, a hybrid strategy designed to improve the features of the original KOA. Specifically, we adopt a Sobol sequence to initialize the population, aiming to achieve a more uniform distribution of initial solutions across the solution space, and integrate a sine-cosine algorithm with mutation opposition-based learning to enhance both the global search and local exploitation capabilities. The results of experimental comparative analysis on ten benchmark test functions demonstrate that the improved kepler optimization algorithm based on a mixed strategy exhibits notable improvements in both convergence speed and solution accuracy.

关键词： kepler optimization algorithm Sobol Sequence Sine-Cosine algorithm Opposition-based Learning

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Optimal Location of TCSC for Reactive Power Dispatching using kepler optimization algorithm 2

Optimal Location of TCSC for Reactive Power Dispatching usin...

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2nd International Conference on Electrical Engineering and Automatic Control (ICEEAC)

作者： Benhabsa, Imadeddine Kouadri, Ramzi Slimani, Linda Univ Ferhat Abbas Setif 1 Dept Elect Engn Setif Algeria

ISBN: (纸本)9798350349740;9798350349757

The main contribution of this work is to present an approach for optimizing reactive power dispatch (ORPD) conditions to determine the optimal location and sizing of Thyristor Controlled Series Capacitor (TCSC) in power system operation in normal conditions. This issue was formulated to determine the optimal placement and dimensions of the TCSC by optimizing the objective function using a new metaheuristic algorithm, called the kepler optimization algorithm (KOA). The proposed algorithm is evaluated and tested on the standard IEEE 30-bus system. The selected objective to be improved is active power loss. The results obtained are compared with other methods such as Quadratic Interpolation optimization (QIO), Gold Rush Optimizer (GRO), and other methods in the literature to establish the validity and efficiency of the proposed algorithm. The results of this study demonstrate the superior performance of the KOA algorithm to solve the ORPD problem considering the TCSC device compared to these recent metaheuristic methods.

关键词： Optimal reactive power dispatch thyristor-controlled series capacitor Active power loss kepler optimization algorithm

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