Next Article in Journal
Power Flow Tracing for Active Congestion Management in Modern Power Systems
Previous Article in Journal
Development and Performance Evaluation of Solid-Free Drilling Fluid for CBM Reservoir Drilling in Central Hunan
Open AccessArticle

State-Of-Health Prediction for Lithium-Ion Batteries Based on a Novel Hybrid Approach

School of Instrument Science and Engineering, Southeast University, No. 2 Sipailou, Nanjing 210096, China
Author to whom correspondence should be addressed.
Energies 2020, 13(18), 4858;
Received: 29 July 2020 / Revised: 7 September 2020 / Accepted: 14 September 2020 / Published: 16 September 2020
Generally, the State-of-Health (SOH) monitoring and Remaining Useful Life (RUL) prediction and assessment of lithium-ion (Li-ion) batteries need to use sensors to obtain the degradation test data of the same type of batteries and establish the degradation model for reference. However, when the battery type is unknown, a usable reference model cannot be obtained, so its prediction and evaluation may be relatively inconvenient. In this paper, the State of-Health prediction for lithium-ion batteries based on a novel hybrid scheme is proposed. Firstly, historical charge/discharge time series and capacity series are extracted to analyze and construct Health Indicators, then using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to decompose the Health Indicator series into the trend and non-trend terms. Among them, the relatively smooth trend item data series uses the Autoregressive Integrated Moving Average model (ARIMA) for prediction; when dealing with the data series of non-trend items which are obviously non-smooth and seemingly random, the residuals predicted by ARIMA and the non-trend items obtained by CEEMDAN decomposition are combined into new non-trend items; then the least square support vector machine (LSSVM) is introduced to build a nonlinear prediction model and make predictions. Finally, combining the prediction results of the trend item data series and the non-trend item data series as a reference for the assessment of the state of health and remaining useful life. The 13 experimental results of 3 batteries verify the effectiveness of the scheme. View Full-Text
Keywords: batteries; CEEMDAN; LSSVM; ARIMA; State-of-Health; Time Health Indicator batteries; CEEMDAN; LSSVM; ARIMA; State-of-Health; Time Health Indicator
Show Figures

Graphical abstract

MDPI and ACS Style

Yun, Z.; Qin, W.; Shi, W.; Ping, P. State-Of-Health Prediction for Lithium-Ion Batteries Based on a Novel Hybrid Approach. Energies 2020, 13, 4858.

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 Access Map by Country/Region

Search more from Scilit
Back to TopTop