scholarly journals Probabilistic Prediction Algorithm for Cycle Life of Energy Storage in Lithium Battery

2019 ◽  
Vol 10 (1) ◽  
pp. 7 ◽  
Author(s):  
Xue Wang ◽  
Chunbin Gao ◽  
Meng Sun

Lithium batteries are widely used in energy storage power systems such as hydraulic, thermal, wind and solar power stations, as well as power tools, military equipment, aerospace and other fields. The traditional fusion prediction algorithm for the cycle life of energy storage in lithium batteries combines the correlation vector machine, particle filter and autoregressive model to predict the cycle life of lithium batteries, which are subjected to many uncertainties in the prediction process and to inaccurate prediction results. In this paper, a probabilistic prediction algorithm for the cycle life of energy storage in lithium batteries is proposed. The LS-SVR prediction model was trained by a Bayesian three-layer reasoning. In the iterative prediction phase, the Monte Carlo method was used to express and manage the uncertainty and its transitivity in a multistep prediction and to predict the future trend of a lithium battery’s health status. Based on the given failure threshold, the probability distribution of the residual life was obtained by counting the number of particles passing through the threshold. The wavelet neural network was used to study the sample data of lithium batteries, and the mapping relationship between the probability distribution of the residual life of lithium batteries and the unknown values were established. According to this mapping relation and the probability distribution of the residual life of lithium batteries, the health data could be deduced and then iterated into the input of the wavelet neural network. In this way, the predicted degradation curve and the cycle life of lithium batteries could be obtained. The experimental results show that the proposed algorithm has good adaptability and high prediction efficiency and accuracy, with the mean error of 0.17 and only 1.38 seconds by average required for prediction.

2021 ◽  
Author(s):  
Haibo Lv ◽  
Yuheng Kang ◽  
Zhou Shen

Abstract The nonlinear fluctuation and uncertainty that characterize urban traffic flow are well-known. An Improved Cuckoo Search-Wavelet Neural Network (ICS-WNN) prediction model for urban traffic flow is suggested in order to increase the accuracy of traffic flow predictions. After the original traffic flow data have been cleaned up and normalized, the traffic flow prediction network model is built by optimizing the wavelet neural network weights and wavelet shrinkage and translation factors based on the adaptive step size and discovery probability of the cuckoo algorithm, and then adding the neural network momentum factor. The traffic flow prediction network model is built in two stages. The results of the experimental simulations demonstrate that the ICS-WNN prediction algorithm has a better fit and accuracy than numerous common optimization prediction techniques, which is encouraging.


2020 ◽  
Vol 13 (5) ◽  
pp. 1429-1461 ◽  
Author(s):  
Xiaona Li ◽  
Jianwen Liang ◽  
Xiaofei Yang ◽  
Keegan R. Adair ◽  
Changhong Wang ◽  
...  

This review focuses on fundamental understanding, various synthesis routes, chemical/electrochemical stability of halide-based lithium superionic conductors, and their potential applications in energy storage as well as related challenges.


2009 ◽  
Vol 129 (7) ◽  
pp. 1356-1362
Author(s):  
Kunikazu Kobayashi ◽  
Masanao Obayashi ◽  
Takashi Kuremoto

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