Abstract
Lithium-ion battery on-line remaining useful life (RUL) prediction has become increasingly popular. The capacity and internal resistance are often used as the batteries’ health indicator (HI) for quantifying degradation and predicting the RUL. However, the capacity and internal resistance are too difficult to measure on-line due to the batteries’ internal state variables being inaccessible to sensors under operational conditions. In addition, measuring these variables requires accurate measurement devices, which can be expensive, and have limited applicability in practice. In this paper, a novel HI is extracted from the operating parameters of lithium-ion batteries for degradation models and RUL prediction. Moreover, the Box–Cox transformation is applied to improve the correlation between the extracted HI and the battery’s real capacity. Then, Pearson and Spearman correlation analyses are utilized to assess the similarity between the real capacity and the estimated capacity derived from the HI. An optimized gray model GM(1,1) is employed to predict the RUL based on the presented HI. The experimental results show that the proposed method is effective and accurate for battery degradation modeling and RUL prediction.
Highlights
Lithium-ion batteries are playing an increasingly significant role in more aspects of our daily lives, such as in portable communication devices including smart phones and laptops, portable medical information devices, and electrified transportation systems
Zhang et al [7] review various aspects of lithium-ion battery prognostics and health monitoring, and summarized the techniques, algorithms, and models used for state of charge (SOC) estimation, current/voltage estimation, capacity estimation, and remaining remaining useful life (RUL)
In order to show the validity of the results in detail, the Spearman correlation coefficient is used to evaluate the linear relationship between the time interval of equal discharging voltage difference (TIEDVD) and the transformed capacity with different parameters λ.21As shown in Figure 5, the Pearson correlation coefficient is at maximum at λ = 1.5, 11 which
Summary
Lithium-ion batteries are playing an increasingly significant role in more aspects of our daily lives, such as in portable communication devices including smart phones and laptops, portable medical information devices, and electrified transportation systems. Zhang et al [7] review various aspects of lithium-ion battery prognostics and health monitoring, and summarized the techniques, algorithms, and models used for state of charge (SOC) estimation, current/voltage estimation, capacity estimation, and remaining RUL prediction. We should point out that most of the related research on lithium-ion battery RUL estimation is mainly focused on developing various algorithms to improve an estimation’s accuracy and efficiency These methods often utilize capacity or internal resistance as the health indicator to build the degradation model and make an RUL prediction. Propose an intelligent prognostic for battery health based on sample entropy features of discharging voltage, which can provide computational means for assessing the predictability of a time series, and can quantify the regularity of a data sequence This technique is time-consuming and requires a capacity parameter in evaluating the sample entropy indicator.
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