Abstract
Effective prognosis of lithium-ion batteries involves the inclusion of the influences of uncertainties that can be incorporated through random effect parameters in a nonlinear mixed effect degradation model framework. This study is geared towards the estimation of the reliability of lithium-ion batteries, using parametric effects determination involving uncertainty, using a multiphase decay patterned sigmoidal model, experimental data and the Weibull distribution function. The random effect model, which uses Maximum Likelihood Estimation (MLE) and Stochastic Approximation Expectation Maximization (SAEM) algorithm to predict the parametric values, was found to estimate the remaining useful life (RUL) to an accuracy of more than 98%. The State-of-Health (SOH) of the batteries was estimated using the Weibull distribution function, which is found to be an appropriate formulation to use.
Highlights
Monitoring the State-of-Health (SOH) of lithium-ion batteries by means of remaining useful life (RUL) estimation and the measurement of other health indicators, such as the State-of-Charge (SOC), are vital for intelligent battery management systems [1]
Incorporating the uncertainties in the modeling will result in the implementation of a nonlinear mixed effect model; Equation (1) will be modeled with consideration of the fixed and random effects, to cater for the inherent uncertainties in the battery charge capacity decay
The probability density function plots (Figure 6) show that the variance of the batteries with shorter lifecycle durations is smaller than those with longer lifecycle durations. It can be deduced from the figure that the rate of charge capacity decay is expected to be higher as the age of the battery increases [11,39], due to problems associated with lithium corrosion, surface area degradation of the electrodes and local lithium plating that results in a faded power output [38]
Summary
Monitoring the State-of-Health (SOH) of lithium-ion batteries by means of remaining useful life (RUL) estimation and the measurement of other health indicators, such as the State-of-Charge (SOC), are vital for intelligent battery management systems [1]. Another factor to consider is that of time-dependent uncertainties in the components of lithium-ion batteries that may originate and propagate during the operational life of the battery due to the unwarranted responses and the transient nature of the operating and environmental conditions To this end, the best approach to predictive estimation of the RUL and SOH of lithium-ion batteries involves the consideration of uncertainties, through the random effect model framework [14,15]. It is important to note that the prognostic studies on Li-ion batteries carried out by most of the reviewed papers here, did not account for uncertainties in the charge decay patterns and their impact on the prediction accuracy This makes it imperative that the effects of uncertainties be considered in the right way, so as to understand the influences of these random factors on the RUL estimation. Function for estimating the expected characteristic life at a given EOL threshold will give an indication of the RUL and the SOH of the battery at a given cycle
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