The evaluation of lithium battery performance is a complex and very important issue. Generally, manufacturers perform battery burn-in tests and evaluate the performance of lithium batteries based on capacity, internal resistance, voltage, and other parameters in the cycle. However, due to the complexity of practical applications and the difficulty of parameter measurement, it is necessary to evaluate the status of health (SOH) of lithium-ion batteries from the side. Analysis of battery charge and discharge data found that using the charge and discharge time to evaluate the health of the battery is effective and feasible, especially the time during the discharge/charge platform period, and the parameter measurement is more convenient. In this paper, three time-health indicators are constructed and analyzed in detail, and then the health of the battery is evaluated using a simple Bayesian Monte Carlo theory. The experimental results of four batteries show that the scheme is simple and convenient, and can effectively evaluate the SOH of lithium-ion batteries. INDEX TERMS Optimal health indicator, lithium-ion battery, correlation analysis, time difference, Bayesian Monte Carlo, state of health.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.