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Towards enhanced remaining useful life prediction of lithium-ion batteries with uncertainty using optimized deep learning algorithm

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JOURNAL OF ENERGY STORAGE 2024年 98卷

作者： Reza, M. S. Hannan, M. A. Mansor, M. Ker, Pin Jern Rahman, S. A. Jang, Gilsoo Mahlia, T. M. Indra Univ Tenaga Nas Dept Elect & Elect Engn Kajang 43000 Malaysia Sunway Univ Sch Engn & Technol Bandar Sunway 47500 Malaysia Korea Univ Sch Elect Engn Seoul 136701 South Korea Univ Technol Sydney Sch Civil & Environm Eng Ultimo NSW 2007 Australia

An essential component of assessing lithium-ion battery (LIB) performance, reliability, and administration in the application of battery health monitoring and management is determining the battery's Remaining Useful Life (RUL). However, existing RUL prediction approaches have difficulties with variability and nonlinearity that occur during battery degradation, data extraction, feature extraction, hyperparameters optimization, and prediction model uncertainty. To address these problems, this paper introduces a novel hybrid approach for RUL prediction, combining a Lightning Search algorithm (LSA) with a Long-Short Term Memory (LSTM) deep learning model. At first, the hybrid LSA + LSTM model is trained using a comprehensive framework comprising 31 data features, utilizing a mathematical systematic sampling (SS) approach. This sampling technique enables the identification of 10 related data features including temperature, voltage, and current, recorded during each charging cycle from the LIB parameters. Moreover, the LSA optimization technique is introduced to optimally determine the LSTM deep neural model hyperparameters including the number of hidden neurons, learn rate, epoch, learn rate drop factor, learn rate drop period, and gradient decay factor. The effectiveness of the proposed LSA + LSTM model is assessed using battery aging data from the NASA dataset. In addition, to validate the prediction performance of the proposed LSA + LSTM model, extensive comparisons are performed with other popular optimization-based deep learning methods including artificial bee colony (ABC) based LSTM (ABC + LSTM), gravitational search algorithm (GSA) based LSTM (GSA + LSTM), and particle swarm optimization (PSO) based LSTM (PSO + LSTM) model using different error matrices. The robustness of the proposed method is further validated with existing literature as well as with another battery dataset obtained from the MIT Stanford dataset. The RUL prediction results with uncertainty quan

关键词： Lithium-ion battery Remaining useful life prediction Systematic sampling Uncertainty parameters optimization algorithm Deep learning

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MBB-MOGWO: Modified Boltzmann-Based Multi-Objective Grey Wolf Optimizer

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SENSORS 2024年第5期24卷 1502页

作者： Liu, Jing Liu, Zhentian Wu, Yang Li, Keqin Inner Mongolia Univ Hohhot Coll Comp Sci Hohhot 010021 Peoples R China SUNY Coll New Paltz Dept Comp Sci New York NY 12561 USA

The primary objective of multi-objective optimization techniques is to identify optimal solutions within the context of conflicting objective functions. While the multi-objective gray wolf optimization (MOGWO) algorithm has been widely adopted for its superior performance in solving multi-objective optimization problems, it tends to encounter challenges such as local optima and slow convergence in the later stages of optimization. To address these issues, we propose a Modified Boltzmann-Based MOGWO, referred to as MBB-MOGWO. The performance of the proposed algorithm is evaluated on multiple multi-objective test functions. Experimental results demonstrate that MBB-MOGWO exhibits rapid convergence and a reduced likelihood of being trapped in local optima. Furthermore, in the context of the Internet of Things (IoT), the quality of web service composition significantly impacts complexities related to sensor resource scheduling. To showcase the optimization capabilities of MBB-MOGWO in real-world scenarios, the algorithm is applied to address a Multi-Objective Problem (MOP) within the domain of web service composition, utilizing real data records from the QWS dataset. Comparative analyses with four representative algorithms reveal distinct advantages of our MBB-MOGWO-based method, particularly in terms of solution precision for web service composition. The solutions obtained through our method demonstrate higher fitness and improved service quality.

关键词： Boltzmann selection multi-objective grey wolf optimizer optimization algorithm web service composition

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Frigatebird optimizer: a novel metaheuristic algorithm

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PHYSICA SCRIPTA 2024年第12期99卷 125233-125233页

作者： Wang, Xiaowei Huangshan Univ Sch Tourism Huangshan Anhui Peoples R China

This paper proposes a new swarm intelligence optimization algorithm- the Frigate Bird Optimizer ( FBO ) . The algorithm is inspired by the unique fl ight and foraging behaviors of frigatebirds. Its optimization process is divided into two stages: The fi rst stage simulates frigatebirds harassing other seabirds to snatch food, and the search direction and radius of individuals have randomness and uncertainty, which is conducive to global exploration;The second stage simulates frigatebirds observing large fi sh driving small fi sh to leap out of the water and preying on them, and individuals tend to gather in the optimal search direction. By simulating the behaviors of frigatebirds under different survival strategies, the algorithm achieves extensive global search in the fi rst stage and fi ne- tuned local optimization by learning information in the second stage. To evaluate its performance, 46 functions in the CEC2014 and CEC2017 benchmark test sets are selected as objective functions and compared with 9 state-of-the-art meta-heuristic algorithms. The results show that the FBO algorithm has higher performance, excellent iterative optimization ability and strong robustness, and can be applied to different optimization domains.

关键词： swarm intelligence frigatebird metaheuristic optimization exploration and exploitation optimization algorithm engineering

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Low Frequency Hierarchical Cooperative Impulse Control for Gravitational Wave Detector Formation Keeping

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2024年第6期47卷 1167-1181页

作者： Xie, Xuan Jiang, Fanghua Li, Junfeng Tsinghua Univ Sch Aerosp Engn Beijing 100084 Peoples R China

Configuration stability is crucial for the detection accuracy of space-based gravitational wave (GW) detectors. This paper proposes a low-frequency hierarchical cooperative impulse control strategy and a high-fidelity orbit optimization method to achieve a formation of GW detectors with high detection performance. The control strategy employs a hierarchical structure to divide the configuration control into position and shape layers. In the upper layer, the position layer, a state transition tensor method is employed to analyze the sensitivity and feasible range of the formation center position on the configuration stability. In the lower layer, the shape layer, a novel consistency protocol cooperatively uses the states of all individuals in the formation to determine the control expectations. The constraint on formation position is relaxed to reduce the number of impulses, achieving a low-frequency impulse control. The optimization method, characterized by a careful selection of the optimization variables and a design of adaptive model continuation from a low-fidelity model to a high-fidelity model, is proposed to reduce the problem-solving difficulty. The proposed strategy and method are applied to the Laser Interferometer Space Antenna (LISA) mission, and the results demonstrate that the configuration stability is effectively enhanced with several low-frequency impulses.

关键词： Deci Hertz Interferometer Gravitational Wave Observatory optimization algorithm Planets Dynamic Modelling Energy Economics Control optimization Monte Carlo Simulation Spacecraft Formation Near Earth Orbit Particle Swarm optimization

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Electrochemical Parameter Identification for Lithium-Ion Battery Sources in Self-Sustained Transportation Energy Systems

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2024年第1期60卷 1240-1254页

作者： Gu, Yuxuan Wang, Jianxiao Chen, Yuanbo Zheng, Kedi Deng, Zhongwei Chen, Qixin Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Peking Univ Natl Engn Lab Big Data Anal & Applicat Beijing 100871 Peoples R China Chongqing Univ Coll Mech & Vehicle Engn Chongqing 400044 Peoples R China

Lithium-ion battery (LIB) sources have played an essential role in self-sustained transportation energy systems and have been widely deployed in the last few years. To realize reliable battery maintenance, identifying its electrochemical parameters is necessary. However, the battery model contains many parameters while the measurable states are only the current and voltage, inducing the identification inherently an ill-conditioned problem. A parameter identification approach is proposed, including the experiment, model, and algorithm. Electrochemical parameters are first grouped manually based on the physical properties and assigned to two sequenced tests for identification. The two tests named the quasi-static test and the dynamic test, are compressed on time for practical implementation. Proper optimization models and a sensitivity-oriented stepwise (SSO) optimization algorithm are developed to search for the optimal parameters efficiently. Typically, the Sobol method is applied to conduct the sensitivity analysis. Based on the sensitivity indexes, the SSO algorithm can decouple the mixed impacts of different parameters during the identification. For validation, numerical experiments on a typical NCM811 battery at different life stages are conducted. The proposed approach saves about half the time finding the proper parameter value. The identification accuracy of crucial parameters related to battery degradation can exceed 95%. Case study results indicate that the identified parameters can not only improve the accuracy of the battery model but also be used as the indicator of the battery SOH.

关键词： Batteries Electrodes Electrolytes optimization Heuristic algorithms Battery charge measurement Voltage measurement Electro-chemical model global sensitivity analysis lithium-ion battery optimization algorithm parameter identification

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Maximizing Resource Efficiency in Cloud Data Centers through Knowledge-Based Flower Pollination algorithm (KB-FPA)

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Computers, Materials & Continua 2024年第6期79卷 3757-3782页

作者： Nidhika Chauhan Navneet Kaur Kamaljit Singh Saini Sahil Verma Kavita Ruba Abu Khurma Pedro A.Castillo University Institute of Computing Department Chandigarh UniversityPunjab140413India Department of Computer Science and Engineering Chandigarh UniversityPunjab140413India Universidade Federal do Piauí TeresinaPiauí64049-550Brazil MEU Research Unit Faculty of Information TechnologyMiddle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan Department of Computer Engineering Automatics and RoboticsUniversity of GranadaGranada18071Spain

Cloud computing is a dynamic and rapidly evolving field,where the demand for resources fluctuates *** paper delves into the imperative need for adaptability in the allocation of resources to applications and services within cloud computing *** motivation stems from the pressing issue of accommodating fluctuating levels of user demand *** adhering to the proposed resource allocation method,we aim to achieve a substantial reduction in energy *** reduction hinges on the precise and efficient allocation of resources to the tasks that require those most,aligning with the broader goal of sustainable and eco-friendly cloud computing *** enhance the resource allocation process,we introduce a novel knowledge-based optimization *** this study,we rigorously evaluate its efficacy by comparing it to existing algorithms,including the Flower Pollination algorithm(FPA),Spark Lion Whale optimization(SLWO),and Firefly *** findings reveal that our proposed algorithm,Knowledge Based Flower Pollination algorithm(KB-FPA),consistently outperforms these conventional methods in both resource allocation efficiency and energy consumption *** paper underscores the profound significance of resource allocation in the realm of cloud *** addressing the critical issue of adaptability and energy efficiency,it lays the groundwork for a more sustainable future in cloud computing *** contribution to the field lies in the introduction of a new resource allocation strategy,offering the potential for significantly improved efficiency and sustainability within cloud computing infrastructures.

关键词： Cloud computing resource allocation energy consumption optimization algorithm flower pollination algorithm

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A Novel Near-Field Calibration Method for Linear Array Based on Plane Wave Expansion

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2024年第11期72卷 8173-8183页

作者： Tang, Yuanhua Wang, Zhengpeng Fan, Wei Chen, Xiaoming Loh, Tian-Hong Luo, Zhiming Gao, Steven Beihang Univ Dept Elect & Informat Engn Beijing 100191 Peoples R China Southeast Univ Sch Informat Sci & Engn Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Jiaotong Univ Sch Informat & Commun Engn Xian 710049 Peoples R China Natl Phys Lab Engn Electromagnet & Electrochem Technol Dept Teddington England Chinese Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

This article presents a novel near-field calibration method for linear arrays based on plane wave expansion. The proposed method enables the calibration of an antenna under test (AUT) with a compact measurement distance and a probe scanning aperture as small as 1/10 of the AUT aperture. The calibration measurement can be implemented by moving a probe antenna in a sparse grid along a line segment parallel to the AUT and collecting the AUT array element response. In the proposed method, an ideal virtual linear array is employed to model the AUT, where the virtual AUT possesses the same configuration as the AUT and consists of array elements with consistent radiation characteristics. The virtual AUT is determined by minimizing the difference between the response of the virtual and actual AUT, where the virtual AUT response can be constructed based on plane wave expansion. The least square (LS) method is applied to minimize the difference, while an optimization algorithm is employed to solve the radiation characteristics and the initial excitations of the virtual AUT. In the validation measurement, a nonuniform linear array (NULA) is calibrated in the near field using the proposed method with a probe scanning aperture of only 1/10 of the AUT aperture and a measurement distance of 4.33 wavelengths at 2.6 GHz. Based on the obtained results, +/- 0.41 dB in amplitude and +/- 4.7 degrees in phase calibration accuracy are achieved, respectively, demonstrating the effectiveness of the proposed method.

关键词： Probes Calibration Antenna measurements Phased arrays Linear antenna arrays Antenna radiation patterns Phase measurement Linear array near-field calibration optimization algorithm plane wave expansion

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Robust Design for Multi-Carrier Multi-Relay Assisted Over-the-Air Computation Networks With Bounded Channel Uncertainties

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IEEE WIRELESS COMMUNICATIONS LETTERS 2024年第8期13卷 2290-2294页

作者： Hu, Changjie Li, Quanzhong Zhang, Qi Li, Qiang Sun Yat Sen Univ Sch Elect & Informat Technol Guangzhou 510006 Peoples R China Peng Cheng Lab Dept Broadband Commun Shenzhen 518055 Peoples R China Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China

Over-the-air computation (AirComp) has attracted vast attention due to its fast aggregation capacity for large scale distributed data. In order to further improve the computation performance of AirComp, we consider a multi-relay assisted AirComp network over a multi-carrier broadband channel, which can benefit from the multi-carrier diversity. Moreover, we take into account a more practical scenario with bounded channel uncertainties, and the worst-case computational mean square error (CMSE) is minimized by jointly designing the transmit and receiving scaling factors, as well as the amplification coefficient of each relay under the transmit power constraints. To handle the intractable non-convex robust optimization problem, we develop an efficient alternating optimization algorithm based on the cutting-set method (CSM). Numerical results are provided to validate the effectiveness of our proposed schemes.

关键词： Relays Sensors Uncertainty Data communication Atmospheric modeling optimization Wireless networks Over-the-air computation (AirComp) robust transceiver design optimization algorithm cooperative relay multi-carrier transmission

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Modeling Laminar Separation Bubbles by Field Inversion Method

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AIAA JOURNAL 2024年第1期62卷 175-192页

作者： Longobardo, Giosue Catalano, Pietro Quagliarella, Domenico Tognaccini, Renato Univ Napoles Federico II Ind Engn Dept Piazzale Tecchio 80 Naples Italy Italian Aerosp Res Ctr Fluid Mech Unit Capua Italy

This paper presents the application of a data-driven method for turbulence modeling. The main aim is to enhance the prediction capabilities of an existing Reynolds-averaged Navier-Stokes (RANS) model for laminar separation bubbles. The recently developed field inversion method has been chosen to infer corrections to the shear stress transport kappa-omega-gamma turbulence model. An innovative approach has been proposed to reduce the computational cost of the modeling procedure. The objective function makes use of the friction coefficient to compare data coming from the RANS model with reference large-eddy simulation data available in the literature. The performed test cases responded well to the optimization procedure which provided a correction field that boosts the turbulent kinetic energy within the bubble. The proposed procedure is the necessary building block to define a new turbulence model able to overcome the present limitations in simulating laminar separation bubbles.

关键词： Laminar Separation Bubble Computational Fluid Dynamics Reynolds Averaged Navier Stokes Spalart Allmaras Turbulence Model optimization algorithm Eppler Airfoil Large Eddy Simulation

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End-to-end inverse modeling and optimization for filters based on artificial neural network

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INTERNATIONAL JOURNAL OF RF AND MICROWAVE COMPUTER-AIDED ENGINEERING 2022年第11期32卷 e23365-e23365页

作者： Huang, Hao Liang, Xiuye Guan, Fang Zi, Jian Fudan Univ Inst Nanoelect Devices & Quantum Comp State Key Lab Surface Phys Shanghai 200433 Peoples R China Fudan Univ Dept Phys State Key Lab Surface Phys Shanghai Peoples R China Fudan Univ Zhangjiang Fudan Int Innovat Ctr Shanghai Peoples R China

In this letter, we propose an end-to-end inverse modeling and optimization method for microwave filter designs based on the data-augmentation learning strategy. Because of the non-uniqueness of solutions, it is difficult to achieve good convergence with artificial neural networks for inverse designs when the parameter space is very large. We prove that the accuracy of inverse predictions can be significantly improved using the network's self-generated data and the optimization can be greatly accelerated with the help of the inverse network. The predicted structural parameters can be used as initial values for optimization, which reduces the number of iterations and avoids falling into local optima. This method is applied to designs of fourth-order interdigital cavity filters. The measurement and simulation agree well.

关键词： artificial neural network microwave filter optimization algorithm

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