RUL Prediction of Lithium-Ion Battery Based on Improved DGWO-ELM Method in a Random Discharge Rates Environment

Zhu, Jun; Tan, Tianxiong; Wu, Lifeng; Yuan, Honglin

doi:10.1109/access.2019.2936822

Cited by 47 publications

(22 citation statements)

References 37 publications

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“…Consequently, different models are applied with various methods to predict the RUL to the most accurate level possible. Even though the aging level of a battery cannot be measured directly, the remaining battery life can be estimated, which can then be used to calculate the ageing level of battery [220]. As shown in Figure 10, RUL methodologies can be categorize into four classes: adaptive filter methods, intelligent methods, stochastic methods, and other methods [221].…”

Section: Rul Estimation Methods For Libmentioning

confidence: 99%

A Comprehensive Review of Available Battery Datasets, RUL Prediction Approaches, and Advanced Battery Management

et al. 2021

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Battery ensures power solutions for many necessary portable devices such as electric vehicles, mobiles, and laptops. Owing to the rapid growth of Li-ion battery users, unwanted incidents involving Li-ion batteries have also increased to some extent. In particular, the sudden breakdown of industrial and lightweight machinery due to battery failure causes a substantial economic loss for the industry. Consequently, battery state estimation, management system, and estimation of the remaining useful life (RUL) have become a topic of interest for researchers. Considering this, appropriate battery data acquisition and proper information on available battery data sets may require. This review paper is mainly focused on three parts. The first one is battery data acquisitions with commercially and freely available Li-ion battery data set information. The second is the estimation of the states of battery with the battery management system. And third is battery RUL estimation. Various RUL prognostic methods applied for Li-ion batteries are classified, discussed, and reviewed based on their essential performance parameters. Information on commercially and publicly available data sets of many battery models under various conditions is also reviewed. Various battery states are reviewed considering advanced battery management systems. To that end, a comparative study of Li-ion battery RUL prediction is provided together with the investigation of various RUL prediction algorithms and mathematical modelling.INDEX TERMS Battery datasets, battery data repository, remaining useful life (RUL), battery management, li-ion battery, RUL prediction methods.

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Section: Rul Estimation Methods For Libmentioning

confidence: 99%

A Comprehensive Review of Available Battery Datasets, RUL Prediction Approaches, and Advanced Battery Management

et al. 2021

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“…Then the details of the hybrid algorithm to optimize the weights and thresholds of DNN was described [38].…”

Section:  Generation Of Initial Population  Mutation Operation  Crossover Operation  Selection Operationmentioning

confidence: 99%

Improved Deep Neural Network for OFDM Signal Recognition Using Hybrid Grey Wolf Optimization

Zhang

Liu

et al. 2020

IEEE Access

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Orthogonal Frequency Division Multiplexing (OFDM), as the core technology in mobile communications, is a multi-carrier modulation technology with high frequency spectrum utilization, which has strong anti-multipath interference and anti-fading ability. The significant advantage of OFDM signals lies in the anti-multipath effect, so its application environment is mostly multipath fading channels. Therefore, it is of great significance to study the identification of OFDM signals in multipath channels. Deep learning, with superior big data processing and classification capabilities, is a potential solution to these problems. Based on the problem of OFDM signal recognition in complex signals in multi-path channel, an OFDM signal recognition method based on hybrid grey wolf optimization algorithm to optimize deep neural network model is proposed. Because the basic grey wolf optimization algorithm (GWO) is easy to fall into a stasis state when attacking prey, differential evolution algorithm (differential evolution algorithm) is integrated into GWO to force GWO to jump out of the stasis state with its strong search ability. The convergence speed and recognition performance of the proposed algorithm are greatly improved. The experimental results show that under the condition of low SNR, the recognition accuracy of proposed algorithm is 9.95% higher than the traditional DNN method, and nearly 4.5% higher than the other two intelligent optimization methods, and the values of Precision and Recall increase obviously, which indicates that the hybrid algorithm not only improves the accuracy of recognition, but also makes the search more complete and accurate. Compared with classical particle swarm optimization (PSO) and whale algorithm optimization algorithm (WOA), the hybrid algorithm has strong competitiveness both in recognition performance and optimization stability, which provides a new, simpler and more effective method for modulation recognition of OFDM signals in wireless communications.

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“…Step 5: fitness is the objective function of optimization problem, and the calculation formula of fitness value is shown in equation (20), where fit is the fitness value and f i is the objective function value of the ith solution. In this paper, the objective function represents the mean square error (MSE) of SVR.…”

Section: Artificial Bee Colonymentioning

confidence: 99%

“…erefore, in this paper, we choose the artificial with local and global search ability ABC algorithm to optimize the parameters of SVR. e fitness value is calculated by equation (20), and the MSE is used as the fitness evaluation function. e parameters of ABC algorithm are set as follows: the number of food sources (SN) is 20, the maximum cycle number (MCN) of food sources is set to 50, and the number of end cycles is 50.…”

Section: Model Training and Abc Parameter Settingmentioning

confidence: 99%

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A New Hybrid Model for Underwater Acoustic Signal Prediction

Chang

Yang

2020

Complexity

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The prediction of underwater acoustic signal is the basis of underwater acoustic signal processing, which can be applied to underwater target signal noise reduction, detection, and feature extraction. Therefore, it is of great significance to improve the prediction accuracy of underwater acoustic signal. Aiming at the difficulty in underwater acoustic signal sequence prediction, a new hybrid prediction model for underwater acoustic signal is proposed in this paper, which combines the advantages of variational mode decomposition (VMD), artificial intelligence method, and optimization algorithm. In order to reduce the complexity of underwater acoustic signal sequence and improve operation efficiency, the original signal is decomposed by VMD into intrinsic mode components (IMFs) according to the characteristics of the signal, and dispersion entropy (DE) is used to analyze the complexity of IMF. The subsequences (VMD-DE) are obtained by adding the IMF with similar complexity. Then, extreme learning machine (ELM) is used to predict the low-frequency subsequence obtained by VMD-DE. Support vector regression (SVR) is used to predict the high-frequency subsequence. In addition, an artificial bee colony (ABC) algorithm is used to optimize model performance by adjusting the parameters of SVR. The experimental results show that the proposed new hybrid model can provide enhanced accuracy with the reduction of prediction error compared with other existing prediction methods.

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RUL Prediction of Lithium-Ion Battery Based on Improved DGWO-ELM Method in a Random Discharge Rates Environment

Cited by 47 publications

References 37 publications

A Comprehensive Review of Available Battery Datasets, RUL Prediction Approaches, and Advanced Battery Management

A Comprehensive Review of Available Battery Datasets, RUL Prediction Approaches, and Advanced Battery Management

Improved Deep Neural Network for OFDM Signal Recognition Using Hybrid Grey Wolf Optimization

A New Hybrid Model for Underwater Acoustic Signal Prediction

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