Next Article in Journal
Electric Car Chassis for Shell Eco Marathon Competition: Design, Modelling and Finite Element Analysis
Previous Article in Journal
Charge for Less: An Analysis of Hourly Electricity Pricing for Electric Vehicles
Article Menu
Issue 1 (March) cover image

Export Article

Open AccessArticle

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

1
College of Electrical Engineering, Jilin Engineering Normal University, Changchun 130052, China
2
College of Food Engineering, Jilin Engineering Normal University, Changchun 130052, China
*
Author to whom correspondence should be addressed.
World Electr. Veh. J. 2019, 10(1), 7; https://doi.org/10.3390/wevj10010007
Received: 17 October 2018 / Revised: 18 January 2019 / Accepted: 18 January 2019 / Published: 28 January 2019
  |  
PDF [1409 KB, uploaded 29 January 2019]
  |     |  

Abstract

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. View Full-Text
Keywords: lithium battery; cycle life; probability prediction; uncertainty; residual life; wavelet neural network lithium battery; cycle life; probability prediction; uncertainty; residual life; wavelet neural network
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Wang, X.; Gao, C.; Sun, M. Probabilistic Prediction Algorithm for Cycle Life of Energy Storage in Lithium Battery. World Electr. Veh. J. 2019, 10, 7.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
World Electr. Veh. J. EISSN 2032-6653 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top