Remaining useful life prediction based on denoising technique and deep neural network for lithium-ion capacitors

Yang, Hao; Wang, Penglei; An, Yabin; Shi, Changli; Sun, Xianzhong; Wang, Kai; Zhang, Xiong; Wei, Tongzhen; Ma, Yanwei

doi:10.1016/j.etran.2020.100078

Cited by 74 publications

(23 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the current technology is not satisfied with only one method—there are many hybrid deep-learning methods. A hybrid neural network was proposed in the literature that combines a convolutional neural network (CNN) and bidirectional long short-term memory network (Bi-LSTM) [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Updating High-Order Extended Kalman Filtering Method Based on Fixed-Step Life Prediction for Vehicle Lithium-Ion Batteries

Wang

Wen

2022

Sensors

View full text Add to dashboard Cite

Lithium-ion batteries have become an important power source in low-carbon transportation energy, and the safe operation and remaining useful life prediction are of great significance. Aiming at the shortcomings of existing methods, such as low prediction accuracy and a short prediction period, this paper proposes a real-time update high-order extended Kalman filter method based on fixed-step life prediction for vehicle lithium batteries based on the principle of combining models and data. First, the state model describing the parameters in the dynamic energy attenuation model is established, and the energy attenuation model is regarded as the observation model of the system to meet the requirements of establishing the Kalman filter. Secondly, the multi-step prediction equation of the state model is established by iterative recursion. At the same time, the multi-step prediction equation between the existing energy output value and the future output value is established based on the multi-dimensional Taylor network (MTN). The multiplicative noise term introduced in the dynamic modeling process is regarded as the hidden variable of the system to meet the requirements of establishing the multi-step linear predictive Kalman filter. Finally, the effectiveness of the new method is verified by digital simulation examples.

show abstract

Section: Introductionmentioning

confidence: 99%

Real-Time Updating High-Order Extended Kalman Filtering Method Based on Fixed-Step Life Prediction for Vehicle Lithium-Ion Batteries

Wang

Wen

2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Relationships between HFs and the SoH can be performed with data-driven models. Deep neural network (DNN)-based approaches (Yang et al, 2020;Bhattacharya et al, 2021) extracted the features from raw charging curve data to obtain the SoH as an output. Recurrent neural networks (RNNs) were adopted to process an input time sequence and obtain the nominal capacity (Ansari et al, 2021;Cheng et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Intelligent Online Health Estimation for Lithium-Ion Batteries Based on a Parallel Attention Network Combining Multivariate Time Series

et al. 2022

View full text Add to dashboard Cite

With the development of cloud and edge computing, data-driven methods for estimating a Li-ion battery’s state of health are becoming increasingly attractive. However, existing data-driven estimation methods have problems of low accuracy and weak robustness that need to be solved. Focusing on these points, this paper proposes a parallel attention network combining multivariate time series to extract the mapping relationship between the selected health features and the state of health. First, multivariate time series are extracted, which can describe battery aging characteristics at different scales. Then, a novel parallel learning framework is designed by integrating long short-term memory cells and an attention mechanism, which can make full use of the health features and help to solve the challenging issues of estimation accuracy and robustness. Experimental results show that the proposed model is able to obtain estimation results for different batteries with a mean absolute percentage error of less than 1%. Compared with existing methods, the maximum error of the proposed model is 38% lower on average. Furthermore, even under measurement noise injections of 50 dB, a preferable estimation result (maximum error of 3.36%) can still be achieved.

show abstract

“…Lisha [6] mentioned adaptive Gauss genetic algorithm-autoregressive integrated moving average method and combined with proportional FR to propose a reliability evaluation method which is more suitable for small samples to analyze the overall quality of meters from different suppliers. Yang [7] proposed a method to establish a comprehensive life model for the meter that can describe different stress ratios based on reliability physics and big data analysis by using a large number of abnormal data and maintenance data generated by the meter during field operation.…”

Section: Introductionmentioning

confidence: 99%

Research on the Life Prediction Method of Meters Based on a Nonlinear Wiener Process

et al. 2022

View full text Add to dashboard Cite

Due to the high reliability of present meters, it is difficult to obtain the failure time of meters through accelerated life tests. Based on the failure data of the accelerated life test, this paper studies the mathematical model based on the Wiener process and establishes the degradation model of the instrument by the maximum likelihood to estimate the parameters of the Wiener model. With full consideration of the possible nonlinear effects in modeling, the time scale transformation method is used to study and obtain the reliability life prediction model of smart meters based on nonlinear data. Finally, the reliability life prediction model of meters is verified and evaluated through the example data of the accelerated life test of smart meters. Compared with the conventional method, this method has less error in calculating the reliability, greatly saves test time, and has a higher accuracy than estimating the lifetime model using the Wiener process directly.

show abstract

Remaining useful life prediction based on denoising technique and deep neural network for lithium-ion capacitors

Cited by 74 publications

References 40 publications

Real-Time Updating High-Order Extended Kalman Filtering Method Based on Fixed-Step Life Prediction for Vehicle Lithium-Ion Batteries

Real-Time Updating High-Order Extended Kalman Filtering Method Based on Fixed-Step Life Prediction for Vehicle Lithium-Ion Batteries

Intelligent Online Health Estimation for Lithium-Ion Batteries Based on a Parallel Attention Network Combining Multivariate Time Series

Research on the Life Prediction Method of Meters Based on a Nonlinear Wiener Process

Contact Info

Product

Resources

About