Prognostics Comparison of Lithium-Ion Battery Based on the Shallow and Deep Neural Networks Model

Abstract

Prognostics of the remaining useful life (RUL) of lithium-ion batteries is a crucial role in the battery management systems (BMS). An artificial neural network (ANN) does not require much knowledge from the lithium-ion battery systems, thus it is a prospective data-driven prognostic method of lithium-ion batteries. Though the ANN has been applied in prognostics of lithium-ion batteries in some references, no one has compared the prognostics of the lithium-ion batteries based on different ANN. The ANN generally can be classified to two categories: the shallow ANN, such as the back propagation (BP) ANN and the nonlinear autoregressive (NAR) ANN, and the deep ANN, such as the long short-term memory (LSTM) NN. An improved LSTM NN is proposed in order to achieve higher prediction accuracy and make the construction of the model simpler. According to the lithium-ion data from the NASA Ames, the prognostics comparison of lithium-ion battery based on the BP ANN, the NAR ANN, and the LSTM ANN was studied in detail. The experimental results show: (1) The improved LSTM ANN has the best prognostic accuracy and is more suitable for the prediction of the RUL of lithium-ion batteries compared to the BP ANN and the NAR ANN; (2) the NAR ANN has better prognostic accuracy compared to the BP ANN.

Multi-Level Model Reduction and Data-Driven Identification of the Lithium-Ion Battery

“…where V s = 4πR 3 s /3, dV s = 4πr 2 dr, subscript avg means volume-average. Further, substituting Equations (2) and (15) into Equation (25) yields:…”

“…They build the battery models based entirely on the experimental data instead of physicochemical theory. Many kinds of empirical models have been proposed using different techniques, such as statistical analysis [10,11], support vector [12], big data [13,14], and deep neural networks [15,16]. The empirical battery models mainly deal with the battery properties that related to capacity [17], State-of-Charge [18] and State-of-Health [19,20].…”

Multi-Parameter Optimization Method for Remaining Useful Life Prediction of Lithium-Ion Batteries

“…The RUL prediction methods of lithium-ion batteries can be divided into model-based prediction methods and datadriven prediction methods [1]. Model-based methods can perform detailed physical and chemical analysis of the battery aging process [13][14][15][16]. But the models are based on specific environmental conditions and charging, discharging conditions.…”

A Novel Hybrid Prognostic Approach for Remaining Useful Life Estimation of Lithium-Ion Batteries

“…It can simulate the implicit relationship between the observations and the objective quantities by extracting valid information from the available data. Data-driven methods contain Wiener Process (WP) [16], neural network (NN) [17][18][19][20], support vector machine (SVM) [21], relevance vector machine (RVM) [22], machine learning (ML) [23], deep learning (DL) [24], autoregressive sliding model (AR) [25], and the Gaussian Process regression (GPR) [26]. For example, in [16], first, the authors introduce the Reproductive Useful Time (RUT).…”

“…The experimental results show that this method has, not only high RUL prediction accuracy, but is also less affected by different prediction starting points. Although, the data-driven approaches show good prognosis effects, the prediction accuracy of such methods depends on a large amount of training data, which makes the cost of such methods relatively high [19]. Considering the advantages and disadvantages of model-based methods and data-driven methods, the combination of these methods has received more attention from more researchers, namely the hybrid model [3,[27][28][29][30][31][32][33][34][35].…”