Abstract
As the secondary widely used battery, lithium-ion batteries (LIBs) have become the core component of the energy supply for most devices. Accurately predicting the current cycle time of LIBs is of great importance to ensure the reliability and safety of the equipment. In this paper, considering the nonlinear and non-Gaussian capacity degradation characteristics of LIBs, a remaining useful life (RUL) prediction method based on the exponential model and the particle filter is proposed. The cycle life test data of LIBs published by prognostics center of excellence in national aeronautics and space administration were exponentially experiencing the rule of degradation. And then the extrapolation method was used to get the quantitative expression of the uncertainty of life expectancy of LIBs, i.e. the prediction mean and the probability distribution histogram. The prognostic horizon index and the new specific accuracy index were applied to evaluate the prediction performance. Moreover, the prediction error under different prediction starting points is given. Compared with other methods such as the auto-regressive integrated moving average model, the fusion nonlinear degradation autoregressive model and the regularized particle filter algorithm, the proposed algorithm has a better prediction performance. According to the accuracy index, the proposed prediction method has better prediction accuracy and convergence. The RUL prediction for LIBs can provide a better decision support for the maintenance and support systems to optimize maintenance strategies, and reduce maintenance costs.
Highlights
Lithium-ion batteries (LIBs) have been widely used in electric vehicles because of its energy density, small weight, long life, and no memory effect [1]–[4]
A remaining useful life (RUL) prediction is critical to the implementation of condition based maintenance (CBM) and prognostics and health management (PHM) [9]
There are some parameters: b is defined as the unknown parameter of the empirical model in the capacity degradation of LIBs, T is defined as the starting point in the algorithm implementation, N is defined as the number of particles, s is defined as the standard deviation of the measured noise, U is defined as the capacity threshold at the end of the battery life, z is defined as the real value of the capacity degradation of LIBs, x is defined as the capacity prediction output value in the each step iterative process, and cycle is defined as the charge and discharge cycle number
Summary
Lithium-ion batteries (LIBs) have been widely used in electric vehicles because of its energy density, small weight, long life, and no memory effect [1]–[4]. L. Zhang et al.: RUL Prediction for LIBs Based on Exponential Model and PF capacity precisely. A particle filter (PF) is a typical used method to determine the RUL uncertainty of LIBs. From the literature, the life expectancy of the PF has a very good prospect because of its strong nonlinear, non-Gaussian processing capacity [26]. Based on the characteristics of the capacity degradation of Li(NiMnCo)O2 LIBs, Wang, et al [33] proposed a double logarithmic degradation model, and the PF algorithm was used to study the parameters. Gustafsson [34] proposed a novel incremental capacity analysis (ICA) method for state of health (SOH) estimation to optimize the model parameters for better prediction accuracy and enhance its applicability in realistic BMS, and the effectiveness of the proposed model was validated by experimentation.
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