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State of Health Estimation for Lithium-Ion Batteries Using enhanced whale optimization algorithm for Feature Selection and Support Vector Regression Model

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PROCESSES 2025年第1期13卷 158-158页

作者： Wang, Rui Xu, Xikang Zhou, Qi Zhang, Jingtao Wang, Jing Ye, Jilei Wu, Yuping Nanjing Tech Univ Sch Energy Sci & Engn Nanjing 211816 Peoples R China South East Univ Sch Energy & Environm Nanjing 211189 Peoples R China

Evaluating the state of health (SOH) of lithium-ion batteries (LIBs) is essential for their safe deployment and the advancement of electric vehicles (EVs). Existing machine learning methods face challenges in the automation and effectiveness of feature extraction, necessitating improved computational efficiency. To address this issue, we propose a collaborative approach integrating an enhanced whale optimization algorithm (EWOA) for feature selection and a lightweight support vector regression (SVR) model for SOH estimation. Key features are extracted from charging voltage, current, temperature, and incremental capacity (IC) curves. The EWOA selects features by initially assigning weights based on importance scores from a random forest model. Gaussian noise increases population diversity, while a dynamic threshold method optimizes the selection process, preventing local optima. The selected features construct the SVR model for SOH estimation. This method is validated using four aging datasets from the NASA database, conducting 50 prediction experiments per battery. The results indicate optimal average absolute error (MAE) and root mean square error (RMSE) within 0.41% and 0.71%, respectively, with average errors below 1% and 1.3%. This method enhances automation and accuracy in feature selection while ensuring efficient SOH estimation, providing valuable insights for practical LIB applications.

关键词： state of health health feature enhanced whale optimization algorithm feature selection support vector regression model

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A New Nonlinear Fatigue Cumulative Damage Model Based on enhanced whale optimization algorithm and Manson–Halford Model

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Fatigue and Fracture of Engineering Materials and Structures 2025年

作者： Tang, Yuhan Wang, Yuedong Dong, Qi Li, Yonghua Guo, Tao Zhang, Zhiyang Zhan Tianyou College (CRRC College) Dalian Jiaotong University Dalian China Bogie Technology Center CRRC Tangshan Co. Ltd Tangshan China

In the field of modern mechanical engineering, structures often endure multi-level variable stress loading. The nonlinear fatigue cumulative damage process of these structures is highly complex due to the significant influence of loading sequences and interactions, which makes fatigue life prediction difficult. To accurately describe the impacts of these factors on fatigue damage, this paper proposes a nonlinear fatigue cumulative damage model (EWOA-MH) based on the enhanced whale optimization algorithm (EWOA) and the Manson–Halford (M-H) model. This model obtains weight factors through EWOA and incorporates them into the M-H model. Verified by experimental data of multi-level variable stress loading and calculated with a weighted method considering different materials' sample numbers, the prediction accuracy is increased by approximately 43%. Its application to the analysis of high-speed train bogie frames effectively demonstrates the model's effectiveness. The research shows that the EWOA-MH model performs outstandingly in fatigue life prediction and can effectively solve fatigue damage problems under multi-level variable stress loading conditions. © 2025 John Wiley & Sons Ltd.

关键词： enhanced whale optimization algorithm fatigue life prediction Manson–Halford model multi-level variable stress loading nonlinear fatigue cumulative damage

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An effective dynamical evaluation and optimization mechanism for accurate motion primitives learning

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APPLIED INTELLIGENCE 2025年第2期55卷 1-19页

作者： Liu, Chunfang Li, Changfeng Li, Xiaoli Zuo, Guoyu Yu, Pan Beijing Univ Technol Beijing 100102 Peoples R China

Trajectory planning is an important stage in robot operation. Many imitation learning methods have been researched for learning operation skills from demonstrated trajectories. However, it is still a challenge to use the learned skill models to generate motion trajectories suitable for various changing conditions. In this paper, a closed-loop dynamical evaluation and optimization mechanism is proposed for imitation learning model to generate the optimal trajectories that can adapt to multiple conditions. This mechanism works by integrating the following parts: (1) imitation learning based on an improved dynamic motion primitive;(2) constructing the trajectory similarity evaluation function;(3) presenting an enhanced whale optimization algorithm(EWOA) by introducing the piecewise decay rate and inertia weight for avoiding getting stuck in local optima. The EWOA iteratively optimizes the key parameter of the skill learning model based on the cost function of the trajectory similarity evaluation for generating the trajectory with the highest similarity to the teaching trajectory. The effectiveness of the EWOA is validated using 10 functions by comparing with the other two methods. And the feasibility of the dynamical optimization mechanism is proved under different motion primitives and various generation conditions.

关键词： Evaluation mechanism enhanced whale optimization algorithm Operational skill learning Trajectory dynamic optimization

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A new method of health condition detection for hydraulic pump using enhanced whale optimization-resonance-based sparse signal decomposition and modified hierarchical amplitude-aware permutation entropy

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2021年第15期43卷 3360-3376页

作者： Zhou, Fuming Liu, Wuqiang Yang, Xiaoqiang Shen, Jinxing Gong, Peiping Army Engn Univ PLA Field Engn Coll Nanjing 210007 Jiangsu Peoples R China Army Engn Univ PLA Training Base Chongqing Peoples R China

The normal operation of the hydraulic pump is the significant premise for the stable and dependable working of hydraulic equipment. Consequently, this research comes up with a health condition detection method of hydraulic pump. First of all, this approach selects resonance-based sparse signal decomposition (RSDD) to adaptively disintegrate vibration signals. The biggest problem of the RSDD algorithm is the requirement to artificially set a large number of key parameters, such as quality factor Q, weight coefficient A, and Lagrange operator u. The improper parameter settings will seriously affect the decomposition performance. To overcome this shortcoming, an enhanced whale optimization algorithm is presented to search the best parameter combination of the RSDD. The algorithm takes the correlation kurtosis as the optimization objective function to adaptively disintegrate the signal into low and high resonance components. Moreover, on the basis of the modified analytic hierarchy process and the amplitude-aware permutation entropy, the modified hierarchical amplitude-aware permutation entropy is raised for measuring the complexity of the measured time series more accurately and comprehensively. After that, a health condition detection method for hydraulic pump based on enhanced whale optimization-resonance-based sparse signal decomposition and modified hierarchical amplitude-aware permutation entropy is raised. Finally, through the usage of the hydraulic pump vibration data, this method is compared with other approaches. According to the experimental results, the raised method can identify the fault type more effectively, which is capable of offering a feasible idea for the health condition detection of hydraulic equipment.

关键词： Resonance-based sparse signal decomposition enhanced whale optimization algorithm modified hierarchical amplitude-aware permutation entropy hydraulic pump health condition detection

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Bi-level optimization configuration method for microgrids considering carbon trading and demand response

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FRONTIERS IN ENERGY RESEARCH 2024年 11卷

作者： Wang, Lei Zhou, Junping Jiang, Weijian Xu, Yuling Shanghai Univ Elect Power Coll Elect Engn Shanghai Peoples R China Jiaxing Power Supply Co State Grid Zhejiang Elect Power Co Ltd Jiaxing Zhejiang Peoples R China

Under the background of the shortage of traditional energy and the increasingly serious environmental problems, this paper proposes a bi-level optimization configuration method for microgrids based on stepped carbon trading and price-based demand response. The stepped carbon trading mechanism is introduced into the planning model to clarify the impact of construction processes on land carbon emissions. Simultaneously, price-based demand response during system operation is introduced, guiding users to change the electricity consumption strategies to promote the consumption of renewable energy. Then, a bi-level optimal configuration framework is constructed. The upper level is optimized to allocate the capacity of the microgrid, with the lowest annual equivalent comprehensive cost as the objective function. The lower level is optimized for microgrid operation, with the minimum sum of the microgrid operation cost and carbon emission cost as the objective function. In view of the advantages of enhanced whale optimization algorithm (E-WOA) in solving multi-dimensional feature selection problems, E-WOA and CPLEX solver are combined to solve the bi-level optimization model for the first time. Simulation results show that after introducing a stepped carbon trading mechanism, the system carbon emissions decreased by 23.09%. Considering price-based demand response under the premise of introducing a stepped carbon trading mechanism, the total cost decreased by 3.57% and the carbon emissions decreased by 19.83%.

关键词： grid-connected microgrid hydrogen energy storage optimal configuration stepped carbon trading demand-side response enhanced whale optimization algorithm

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Multi-area load frequency regulation of a stochastic renewable energy-based power system with SMES using enhanced-WOA-tuned PID controller

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HELIYON 2023年第9期9卷 e19199页

作者： Gbadega, Peter Anuoluwapo Sun, Yanxia Univ Johannesburg Dept Elect & Elect Engn Sci ZA-2006 Johannesburg South Africa

This paper presents a novel nature-inspired meta-heuristic optimization algorithm known as the enhanced whale optimization algorithm (EWOA), which imitates humpback whales' social behavior to solve the optimization of multi-area automatic load frequency control (LFC) problems of a stochastic renewable energy-based power system with superconducting magnetic energy storage (SMES). An EWOA algorithm is presented in response to the limitations of the conventional WOA algorithm, including its sluggish convergence time, low accuracy, and propensity to easily enter local optimum. The system model investigated includes some physical constraints such as the time delay (TD), generation rate constraint (GRC), reheat turbine (RT), and the dead band (DB). The impacts of these physical constraints on the dynamic performance of the proposed controller were investigated. The EWOA algorithm is utilized to dynamically optimize the parameters of the PID controller for optimal system performance. The effectiveness and dynamic performance of the proposed controller are compared with the conventional WOA using some performance metrics. The system model also includes superconducting magnetic energy storage (SMES) units in both areas and their impacts on the system performances are also investigated. The effects of the changes of two different parameters of the system (frequency bias parameter, B, and the governor speed regulation, R) on the frequency deviation responses and the controller's robustness are examined. It is evident from the results that the dynamic performance of the proposed controller is better than that of the conventional WOA and it is more robust and stable to changes in system loading, parameters, and step load perturbation.

关键词： Automatic generation control Superconducting magnetic energy storage (SMES) Multi -source power generation Renewable energy sources Proportional -integral -derivative (PID) controller enhanced whale optimization algorithm

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A Hierarchical optimization Method for Parameter Estimation of Diesel Generators

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IEEE ACCESS 2020年 8卷 176467-176479页

作者： Huang, Chao-Ming Huang, Yann-Chang Chen, Shin-Ju Yang, Sung-Pei Kun Shan Univ Dept Elect Engn Tainan 710 Taiwan Cheng Shiu Univ Dept Elect Engn Kaohsiung 833 Taiwan Kun Shan Univ Green Energy Technol Res Ctr Tainan 710 Taiwan

Diesel generators (DGs) are used to provide electrical power to consumers because their power outputs can be scheduled, and they offer stable operating characteristics in a standalone or microgrid system. The parameters for DGs are set to ensure reliable and accurate simulation for distributed energy resources (DERs), which increases system reliability, maintains power supply quality and reduces operational costs. There are many parameters that must be estimated for a DG. These parameter settings such as system gains and time constants may vary, as facilities are run for a few days. Parameters for a DG must then be re-estimated to ensure accurate simulation in a microgrid system. The proposed method uses a sensitivity analysis to classify parameters into three different categories. A hierarchical optimization method combined with an enhanced whale optimization algorithm (EWOA) is then used to estimate the parameter settings for a DG using actual measurement data. The proposed method is applied to a practical microgrid system, and the results show that the proposed method determines the optimal parameter settings for a DG that enables accurate simulation and robust implementation for a microgrid system.

关键词： Microgrids Transient analysis Voltage control Parameter estimation Generators Sensitivity analysis Microgrid system parameter estimation hierarchical optimization method enhanced whale optimization algorithm

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