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Health-Aware Battery-Fast-Charging Strategy Using Thermal-Aging Cell Model and whale optimization algorithm

ENERGY TECHNOLOGY 2024年第1期12卷 2300861-2300861页

作者： Bose, Bibaswan Teja, Saladi Sairam Garg, Akhil Gao, Liang Li, Wei Singh, Surinder Babu, B. Chitti Indian Inst Technol Ctr Automot Res & Tribol Delhi India Huazhong Univ Sci & Technol Sch Mech Sci & Engn Huazhong Peoples R China Hefei Univ Technol Sch Mech Engn Hefei 230009 Peoples R China Swinburne Univ Technol Fac Sci Engn & Technol Dept Mech Engn & Prod Design Engn Hawthorn Australia Indian Inst Informat Technol Design & Mfg Dept Elect & Commun Engn Chennai Tamil Nadu India

In this article, developing an optimized health-aware battery-fast-charging strategy is proposed using multistep constant-current constant-voltage (MSCCCV)-charging technique. First, the thermal-aging cell model (TACM) is formulated utilizing a 1D radial heat-conduction phenomenon. The utilization of this model facilitates the generation of a simulated cell model, thereby mitigating the necessity for conducting multiple experimental assays. A cycle life predictor based on multi-input elastic net regression is then developed, which forecast cell's cycle life based on inputs from TACM. The accuracy of the projected life is 87% which is validated using APR18650M1B cell cycling dataset. This is then utilized to devise an adaptive MSCCCV-charging strategy with four-step constant-current (CC), which is optimized using whale optimization algorithm, have C-rate of 5.2C, 4.4C, 5.2C, and 4.52C. An alternate cell (LGEBM26R) is used to validate the reproducibility of the proposed charging method. The second cell charging is optimized using the aforesaid steps and four-step CC, thus obtained have C-rate of 1.3C, 1.95C, 2.23C, and 1.24C. Compared to 1C-CCCV charging, the algorithm increases LGEBM26R cell cycle life by 17.23% and APR18650M1B cell cycle life by 28%. The MSCCCV technique's superiority is demonstrated through a comparison with benchmark techniques considering charging time and cycle life as performance parameters. In this article, an optimized health-aware battery-fast-charging approach is proposed utilizing multistep constant-current constant-voltage technology. Initially, thermal-aging cell model (TACM) is developed using a 1D-radial heat-conduction phenomena to create a simulated cell model. A multi-input elastic net regression cycle life predictor is developed using TACM inputs. An adaptive four-step-CCCV-charging technique is devised, which is optimized utilizing whale optimization algorithm. Hypothesis is validated by optimizing charging strategy of alternalte

关键词： cycle life predictions elastic net regressions MSCCCVs thermal-aging cell models whale optimization algorithm

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PI Parameters Tuning for Frequency Tracking Control of Wireless Power Transfer System Based on Improved whale optimization algorithm

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IEEE ACCESS 2024年 12卷 13055-13069页

作者： Yang, Xiong Guan, Jiamin State Grid Jiangsu Elect Power Co Ltd Distribut Network Technol Dept Res Inst JSEPRI Nanjing 211103 Peoples R China Nanjing Inst Technol Sch Mech Engn Nanjing 211167 Peoples R China

For the efficiency optimization problem of wireless power transfer system, the most important thing is that the frequency tracking control is needed to make the system work in a resonant state. The parameters of the frequency tracking controller directly determine the control performance. In this paper, we consider using an improved whale optimization algorithm to complete the PI parameter tuning of the frequency tracking controller as a way to save personnel's effort and time on the controller design. This improved whale optimization algorithm mainly has the following improvements: firstly, it uses Kent chaotic mapping to initialize the whale population, which improves the variegation of the initial solution;secondly, it introduces adaptive weight coefficients and nonlinearly improves the convergence factor, which achieves a balance between the algorithm's global and local searching ability;finally, it introduces sine-cosine algorithmic strategy and Cauchy reverse learning strategy, which avoids the problem that the algorithm is easy to mature prematurely. The superiority of the improved algorithm is verified through the testing of eight benchmark functions and the Wilcoxon rank sum test method. Finally, the algorithm is applied to the PI parameter optimization of frequency tracking control, and the phase difference curves under the control of different PI parameters are compared by simulation. The results show that the improved algorithm proposed in this paper has a better performance for the PI parameter tuning of frequency tracking control in wireless power transfer system, which can effectively help reduce the investment of human resources.

关键词： Resonant frequency whale optimization algorithms Tuning Heuristic algorithms Convergence Statistics Social factors Wireless power transfer PI control Energy efficiency frequency tracking control whale optimization algorithm

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Enhanced GRU-based regression analysis via a diverse strategies whale optimization algorithm

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SCIENTIFIC REPORTS 2024年第1期14卷 1-19页

作者： Lin, Zesheng FoShan Open Univ Foshan Peoples R China

Taking into account the whale optimization algorithm's tendency to get trapped in local optima easily and its slow convergence rate, this paper proposes a diverse strategies whale optimization algorithm (DSWOA) and uses it to optimize the parameters of GRU, thereby achieving better regression prediction effects. First, an innovative t-distribution perturbation is used to perturb the optimal whale to expand the optimization space of the optimal whale. Secondly, in the random search stage, we perform a Cauchy walk on the whale's position and then use reverse learning to enable the algorithm to effectively navigate away from the local optimum. Finally, we adopt a horizontal learning strategy for all whales and use two random whales to determine the current whale's position. Updated, the results suggest that DSWOA is highly effective in global optimization. By utilizing DSWOA, the parameters of GRU were fine-tuned. The experimental findings reveal that GRU produces promising outcomes on multiple datasets, making it a more effective tool for regression prediction tasks.

关键词： whale optimization algorithm Gated recurrent unit T-distribution perturbation Cauchy walk Reverse learning Horizontal crossover strategy

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Privacy-preserving edge computing offloading scheme based on whale optimization algorithm

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JOURNAL OF SUPERCOMPUTING 2023年第3期79卷 3005-3023页

作者： Liu, Zhenpeng Wang, Jingyi Gao, Zilin Wei, Jianhang Hebei Univ Sch Elect Informat Engn Baoding 071002 Peoples R China Hebei Univ Informat Technol Ctr Baoding 071002 Peoples R China Xinjiang Univ Sci & Technol Network & Expt Management Ctr Korla 841000 Peoples R China

Aiming at the problem of user's task offloading in mobile edge computing and the potential leakage of location privacy during the offloading process, a privacy-preserving computing offloading scheme based on whale optimization algorithm is proposed. Using differential privacy technology to obfuscate the user's location information, the user can make task offloading decisions according to the obfuscated distance. Considering the delay, energy consumption, and their weighted sum, the offloading problem is modeled as a convex optimization problem. Then, the whale optimization algorithm is adopted to solve this optimization problem to achieve a balance between privacy protection and resource consumption. Experiments are conducted to verify the relationship between the degree of privacy leakage, the computation-offloading cost and real distance, privacy-preserving impact factor, the respective weights of time delay and energy consumption The experimental results show that the offloading scheme proposed in this paper has good performance in terms of cost and privacy protection.

关键词： Edge computing Computing offloading Differential privacy whale optimization algorithm

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An Improved whale optimization algorithm for Global optimization and Realized Volatility Prediction

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Computers, Materials & Continua 2023年第12期77卷 2935-2969页

作者： Xiang Wang Liangsa Wang Han Li Yibin Guo School of Civil Engineering and Architecture Zhengzhou University of AeronauticsZhengzhou450015China School of Civil and Architectural Engineering Jiangsu University of Science and TechnologyZhenjiang212003China

The original whale optimization algorithm(WOA)has a low initial population quality and tends to converge to local optimal *** address these challenges,this paper introduces an improved whale optimization algorithm called OLCHWOA,incorporating a chaos mechanism and an opposition-based learning *** algorithm introduces chaotic initialization and opposition-based initialization operators during the population initialization phase,thereby enhancing the quality of the initial whale ***,including an elite opposition-based learning operator significantly improves the algorithm’s global search capabilities during *** work and contributions of this paper are primarily reflected in two ***,an improved whale algorithm with enhanced development capabilities and a wide range of application scenarios is ***,the proposed OLCHWOA is used to optimize the hyperparameters of the Long Short-Term Memory(LSTM)***,a prediction model for Realized Volatility(RV)based on OLCHWOA-LSTM is proposed to optimize hyperparameters *** evaluate the performance of OLCHWOA,a series of comparative experiments were conducted using a variety of advanced *** experiments included 38 standard test functions from CEC2013 and CEC2019 and three constrained engineering design *** experimental results show that OLCHWOA ranks first in accuracy and stability under the same maximum fitness function calls ***,the China Securities Index 300(CSI 300)dataset is used to evaluate the effectiveness of the proposed OLCHWOA-LSTM model in predicting *** comparison results with the other eight models show that the proposed model has the highest accuracy and goodness of fit in predicting *** further confirms that OLCHWOA effectively addresses real-world optimization problems.

关键词： whale optimization algorithm chaos mechanism opposition-based learning long short-term memory realized volatility

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An efficient facial emotion recognition using convolutional neural network with local sorting binary pattern and whale optimization algorithm

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INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS 2024年 1-16页

作者： Aghabeigi, Fereshteh Nazari, Sara Eraghi, Nafiseh Osati Islamic Azad Univ Dept Comp Engn & Informat Technol Arak Branch Arak Iran

Facial emotion recognition is one of the fields of machine learning and pattern recognition. Facial expression recognition is used in a variety of applications. For robust automatic facial emotion recognition, feature extraction from input image data is challenging. To address this issue, we propose an emotion recognition system based on a new feature extraction method, whale optimization algorithm, and convolutional neural network. In the feature extraction phase, a new efficient human face descriptor is expressed using a local sorting binary pattern and a convolutional neural network. Also, the hyperparameters of the convolutional neural network are optimized using the whale optimization algorithm. Then, the convolutional neural network is applied for classification. The performance of the proposed method is evaluated using three well-known face databases CK+ (extended Cohn-Kanade) with facial expressions (happiness, sadness, fear, anger, disgust, contempt, and surprise), JAFFE (Japanese female facial expression) with (happiness, sadness, anger, fear, neutral, disgust, and surprise), and MMI (MMI facial expression database) with facial emotions (happiness, sadness, anger, fear, disgust, and surprise). The accuracies with CK+, JAFFE, and MMI are 100%, 99.93%, and 99.83%, respectively. Experimental results demonstrate that the proposed model can provide better performance compared to alternative methods.

关键词： Feature extraction Convolutional neural network Local sorting binary pattern Facial emotion recognition Deep learning whale optimization algorithm

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Application of Adaptive whale optimization algorithm Based BP Neural Network in RSSI Positioning

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Journal of Beijing Institute of Technology 2024年第6期33卷 516-529页

作者： Duo Peng Mingshuo Liu Kun Xie School of Computer and Communication Lanzhou University of TechnologyLanzhou 730050China

The paper proposes a wireless sensor network(WSN)localization algorithm based on adaptive whale neural network and extended Kalman filtering to address the problem of excessive reliance on environmental parameters A and signal constant n in traditional signal propagation path loss *** algorithm utilizes the adaptive whale optimization algorithm to iteratively optimize the parameters of the backpropagation(BP)neural network,thereby enhancing its prediction *** address the issue of low accuracy and large errors in traditional received signal strength indication(RSSI),the algorithm first uses the extended Kalman filtering model to smooth the RSSI signal values to suppress the influence of noise and outliers on the estimation *** processed RSSI values are used as inputs to the neural network,with distance values as outputs,resulting in more accurate ranging ***,the position of the node to be measured is determined by combining the weighted centroid *** simulation results show that compared to the standard centroid algorithm,weighted centroid algorithm,BP weighted centroid algorithm,and whale optimization algorithm(WOA)-BP weighted centroid algorithm,the proposed algorithm reduces the average localization error by 58.23%,42.71%,31.89%,and 17.57%,respectively,validating the effectiveness and superiority of the algorithm.

关键词： wireless sensor network received signal strength neural network whale optimization algorithm adaptive weight factor extended Kalman filter

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whale optimization algorithm BP Neural Network with Chaotic Mapping Improving for SOC Estimation of LMFP Battery

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ENERGIES 2024年第17期17卷 4300页

作者： Ouyang, Jian Lin, Hao Hong, Ye Guangdong Polytech Normal Univ Ind Training Ctr Guangzhou 510665 Peoples R China Guangdong Polytech Normal Univ Sch Automat Guangzhou 510665 Peoples R China

The state of charge (SOC) is a core parameter in the battery management system for LMFP batteries. Accurate SOC estimation is crucial for ensuring the safety and reliability of energy storage applications and new energy vehicles. In order to achieve better SOC estimation accuracy, this article proposes an adaptive whale optimization algorithm (WOA) with chaotic mapping to improve the BP neural network (BPNN) model. The SOC estimation accuracy of the BPNN model was improved by utilizing WOA to find the optimal target weight values and thresholds. Comparative simulation experiments (including constant current and working condition discharge experiments) were conducted in Matlab/Simulink R2018a to verify the proposed algorithm and the other four algorithms. The experimental results show that the proposed algorithm had higher SOC estimation accuracy than the other four algorithms, and its prediction errors were less than 1%. This indicates that the proposed SOC estimation method has better prediction accuracy and stability, and has certain theoretical research significance.

关键词： LMFP battery SOC estimation BP neural network whale optimization algorithm chaotic mapping improving

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Localization of a radioactive source with the predictive range-whale optimization algorithm method

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NUCLEAR ENGINEERING AND TECHNOLOGY 2025年第8期57卷

作者： Hong, Zongheng Cong, Feiyun Yang, Bo Chen, Zhangyu Niu, Yunlong Zhejiang Univ State Key Lab Fluid Power & Mechatron Syst Hangzhou 310027 Peoples R China Shanghai Inst Special Equipment Supervis & Inspect Shanghai 200062 Peoples R China Zhejiang Ecol Environm Co Ltd Hangzhou 311121 Peoples R China Zhejiang Univ Taizhou Inst Hangzhou 318000 Peoples R China

Heuristic algorithms can effectively improve the accuracy and efficiency of estimating the status of radioactive sources based on the maximum likelihood estimation method. However, heuristic algorithms often converge prematurely at local optima, compromising stability and making them unsuitable for prolonged monitoring tasks. To address this problem, the Predictive Range-whale optimization algorithm (PR-WOA) method was proposed in this paper. Initially, the predictive location and intensity ranges of the radioactive source were determined by integrating historical prediction results. Subsequently, the initial population was more concentrated within the predictive range to improve the algorithm's capability for local optimization. Finally, an inertia weight was introduced to adaptively adjust the search step, consequently improving its global searching capability and efficiency. The performance of the PR-WOA method was evaluated with the simulation and experimental data. Comparative studies demonstrated that the proposed method significantly improves accuracy and stability in predicting the status of the radioactive source. In the 2022 radioactive source localization experiment in Hangzhou, PR-WOA method achieved an average localization accuracy of 1.30m during the trajectory tracking of a moving radioactive source mounted on the drone. Compared to traditional heuristic algorithms, this method improved localization accuracy by 17.9 % and enhanced localization stability during long-term monitoring by 19.0 %.

关键词： Radioactive source Localization Maximum likelihood estimation whale optimization algorithm

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An Effective Power optimization Approach Based on whale optimization algorithm with Two-Populations and Mutation Strategies

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Chinese Journal of Electronics 2024年第2期33卷 423-435页

作者： Juncai HE Zhenxue HE Jia LIU Yan ZHANG Fan ZHANG Fangfang LIANG Tao WANG Limin XIAO Xiang WANG Hebei Agricultural University Beijing Information Science and Technology University School of Computer Science and Engineering Beihang University School of Electronic and Information Engineering Beihang University

Power is an issue that must be considered in the design of logic circuits. Power optimization is a combinatorial optimization problem, since it is necessary to search for a logical expression that consumes the least amount of power from a large number of Reed-Muller(RM) logical expressions. The existing approach for optimizing the power of multi-output mixed polarity RM(MPRM) logic circuits suffer from poor optimization results. To solve this problem, a whale optimization algorithm with two-populations strategy and mutation strategy(TMWOA) is proposed in this paper. The two-populations strategy speeds up the convergence of the algorithm by exchanging information about the two-populations. The mutation strategy enhances the ability of the algorithm to jump out of the local optimal solutions by using the information of the current optimal solution. Based on the TMWOA, we propose a multi-output MPRM logic circuits power optimization approach(TMMPOA). Experiments based on the benchmark circuits of the Microelectronics Center of North Carolina(MCNC) validate the effectiveness and superiority of the proposed TMMPOA.

关键词： Multi-output mixed polarity Reed-Muller Power optimization Combinatorial optimization problem whale optimization algorithm Two-populations strategy Mutation strategy

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