An Accurate State of Health Estimation for Retired Lithium-ion Batteries Based on Electrochemical Impedance Spectroscopy

Li, Xuefeng; Li, Yichao; Gu, Pingwei; Zhang, Ying; Duan, Bin; Zhang, Chenghui

doi:10.23919/ccc55666.2022.9901759

Cited by 15 publications

(11 citation statements)

References 10 publications

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“…BP is one of the most well‐researched algorithms for neural network studies and is mainly divided into two stages: forward propagation of signals and BP of errors. It sequentially adjusts the weight and bias from the hidden layer to the output layer, as well as the weight and bias from the input layer to the hidden layer 31 . A three‐layer BP neural network can accurately approximate any nonlinear function by using a simple gradient descent method to minimize the overall error of network computation.…”

Section: Proposed Methodsmentioning

confidence: 99%

State of charge estimation for lithium‐ion battery pack based on real vehicle data and optimized backpropagation method by adaptive cross mutation sparrow search algorithm

Feng,

Cai,

et al. 2023

Energy Science & Engineering

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In response to the issues of traditional backpropagation (BP) neural networks in state of charge (SOC) estimation, including easy convergence to local optima, slow convergence speed, and low accuracy, this paper proposes a novel adaptive crossover mutation strategy and dynamic sparrow search algorithm to optimize BP networks' initial values and thresholds (ACMSSA‐BP). The proposed method is based on the sparrow search algorithm, where the number of producers and scroungers is adjusted through an adaptive factor. This improvement effectively transitions the search process from extensive full exploration to localized fine‐tuning search. In the position update phase of the producers, crossover mutation and dynamic search strategies are introduced to increase the diversity of good populations, prevent the algorithm from converging to local optima, and maintain its local search capability in the later stage. Using real transportation data from coal mining flame‐proof tracked vehicles, we applied correlation theory to extract model feature parameters and constructed a training data set to estimate the SOC. The results of both static and dynamic validation experiments have indicated that the ACMSSA‐BP method has delivered impressive performance in predicting SOC, as reflected in the mean absolute error, root mean squared error, and mean absolute percentage error values of less than 1.5%, 1.5%, and 1.6%, respectively. Compared with BP, SSA‐BP, CMSSA‐BP, PSO‐BP, and NARX_NN methods, the ACMSSA‐BP approach demonstrates enhanced accuracy in estimation, significant robustness, and impressive generalization capabilities.

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Section: Proposed Methodsmentioning

confidence: 99%

State of charge estimation for lithium‐ion battery pack based on real vehicle data and optimized backpropagation method by adaptive cross mutation sparrow search algorithm

Feng,

Cai,

et al. 2023

Energy Science & Engineering

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“…With the wide application of cloud computing, cloud storage, big data, and other emerging technologies, the status of ANN-based methods is becoming more significant. Different ANN-based methods have different effects on estimating battery health state, and it is necessary to discuss the research directions and future trends of health state estimation approaches [159], [160].…”

Section: ⅳ Comparative Analysis Of Health State Effects Using Differe...mentioning

confidence: 99%

Critical Review on Improved Electrochemical Impedance Spectroscopy-Cuckoo Search-Elman Neural Network Modeling Methods for Whole-Life-Cycle Health State Estimation of Lithium-Ion Battery Energy Storage Systems

Xiong,

Wang,

Takyi-Aninakwa

et al. 2024

Prot. Control Mod. Power Syst.

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“…Also, they can adopt a battery management algorithm based on dynamic redundancy. The output voltage can be stabilized, and the working time of the battery can be extended by dynamic adjustment of the redundant battery under various working conditions. , At the same time, the combination of the field of acoustics and the field of education needs better media, such as artificial intelligence, to add more new elements to music education, so as to promote the integration of the two fields more closely. , …”

Section: Conclusion and Prospectsmentioning

confidence: 99%

Application of Nanogenerators in the Field of Acoustics

Yu,

Shang,

Zheng

et al. 2023

ACS Appl. Electron. Mater.

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The nanogenerator is a device that can effectively convert the extra mechanical energy released by the environment or the human body into electrical energy. The device collects energy from the environment, such as wind, sound, and small mechanical energy, efficiently and with a low threshold. Because of their unique advantages and excellent output performance, nanogenerators have received wide attention in the field of energy harvesting. Coupled with its high flexibility, low power consumption, and the need for external energy storage devices to achieve complete self-powered characteristics, nanogenerators in other fields, such as acoustics, have high research and application value. The nanogenerators can not only greatly improve the flexibility, portability, and efficiency of the acoustic devices but also act as acoustic devices such as microphones and speakers. The structure and working principle of the triboelectric nanogenerators, piezoelectric nanogenerators, and pyroelectric nanogenerators and the successful combination of various nanogenerators with acoustic devices are reviewed. The feasibility of the application of the nanogenerator technology in the field of acoustics and the flexibility of the combination of nanogenerators and acoustic equipment are described, and the advantages of the equipment produced in combination of the two fields are briefly introduced. Finally, the development prospect and challenges of the nanogenerators in the field of acoustics are discussed, and personal suggestions are put forward.

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An Accurate State of Health Estimation for Retired Lithium-ion Batteries Based on Electrochemical Impedance Spectroscopy

Cited by 15 publications

References 10 publications

State of charge estimation for lithium‐ion battery pack based on real vehicle data and optimized backpropagation method by adaptive cross mutation sparrow search algorithm

State of charge estimation for lithium‐ion battery pack based on real vehicle data and optimized backpropagation method by adaptive cross mutation sparrow search algorithm

Critical Review on Improved Electrochemical Impedance Spectroscopy-Cuckoo Search-Elman Neural Network Modeling Methods for Whole-Life-Cycle Health State Estimation of Lithium-Ion Battery Energy Storage Systems

Application of Nanogenerators in the Field of Acoustics

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