Abstract
The Remaining useful life (RUL) prediction is of great concern for the reliability and safety of lithium-ion batteries in electric vehicles (EVs), but the prediction precision is still unsatisfactory due to the unreliable measurement and fluctuation of data. Aiming to solve these issues, an adaptive sliding window-based gated recurrent unit neural network (GRU NN) is constructed in this paper to achieve the precise RUL prediction of LIBs with the soft sensing method. To evaluate the battery degradation performance, an indirect health indicator (HI), i.e., the constant current duration (CCD), is firstly extracted from charge voltage data, providing a reliable soft measurement of battery capacity. Then, a GRU NN with an adaptive sliding window is designed to learn the long-term dependencies and simultaneously fit the local regenerations and fluctuations. Employing the inherent memory units and gate mechanism of a GRU, the designed model can learn the long-term dependencies of HIs to the utmost with low computation cost. Furthermore, since the length of the sliding window updates timely according to the variation of HIs, the model can also capture the local tendency of HIs and address the influence of local regeneration. The effectiveness and advantages of the integrated prediction methodology are validated via experiments and comparison, and a more precise RUL prediction result is provided as well.
Highlights
As the main energy component, lithium-ion batteries (LIBs) play an important role in the development of hybrid and electric vehicles (EVs) and other electronic industry, owing to the advantages of high energy density, low-emission, lightweight, etc. [1]
There are many data-driven approaches, with emphasis on artificial intelligence being increasingly applied to remaining useful life (RUL) estimations, such as a relevance vector machine (RVM) [13], a support vector machine (SVM) [17], an artificial neural network (ANN) [18,19]
To verify the validity of the RUL prognostic model proposed in this manuscript, several experiments and comparisons are performed here
Summary
As the main energy component, lithium-ion batteries (LIBs) play an important role in the development of hybrid and electric vehicles (EVs) and other electronic industry, owing to the advantages of high energy density, low-emission, lightweight, etc. [1]. The data-driven methods can learn the battery degradation trends from battery monitoring data directly, whereby it circumvents the analysis of electrochemical reaction and failure mechanism These kinds of technologies have attracted great interest recently among researchers [9]. There are many data-driven approaches, with emphasis on artificial intelligence being increasingly applied to RUL estimations, such as a relevance vector machine (RVM) [13], a support vector machine (SVM) [17], an artificial neural network (ANN) [18,19] These methods have brought great progress to the field of state prediction, but there are still some issues, including the complex model structure and the low prediction accuracy [20]. An adaptive sliding window-based GRU prediction model is constructed to synchronously learn the long-term dependencies and capture the local regenerations.
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