Abstract
For most deep learning practitioners, recurrent networks are often used for sequence modeling. However, recent researches indicate that convolutional architectures may be used to optimize recurrent networks on some machine translation tasks. Problems here are which architecture we should use for a new sequence modeling. By integrating and systematically evaluating the general convolution and recurrent architecture used for sequence modeling, a convolution gated recurrent unit (CNN-GRU) network is proposed for the state-of-charge (SOC) estimation of lithium-ion batteries in this paper. Deep-learning models are well suited for SOC estimation because a battery management system is time-varying and non-linear. The CNN-GRU model is trained using data collected from the battery-discharging processes, such as the dynamic stress test and the federal urban driving schedule. The experimental results show that the proposed method can achieve higher estimation accuracy than two commonly used deep learning models (recurrent neural network and gated recurrent unit) and two traditional machine learning approaches (support vector machine and extreme learning machine) for SOC estimation of lithium-ion batteries.
Highlights
With the continuous development of electric vehicles, lithium-ion batteries have become the mainstream of energy storage systems with their high energy, high power density and long life [1]
Experimental results show that the proposed network outperforms popular deep-learning methods like GRU and recurrent neural networks (RNN) and traditional machine learning methods such as extreme learning machine (ELM) and support vector machine (SVM) in terms of estimation errors like root mean square error (RMSE) and mean absolute error (MAE), with all models well trained on lithium-ion battery datasets
convolution gated recurrent unit (CNN-GRU) can directly map battery measurement signals such as voltage, current, and temperature to SOC, avoiding complex model construction processes and computational inference algorithms such as Kalman filters used in traditional SOC estimation
Summary
With the continuous development of electric vehicles, lithium-ion batteries have become the mainstream of energy storage systems with their high energy, high power density and long life [1]. Z. Huang et al.: CNN-GRU—Recurrent Neural Network for SOC Estimation of Lithium-Ion Batteries dynamic circuit model. Huang et al.: CNN-GRU—Recurrent Neural Network for SOC Estimation of Lithium-Ion Batteries dynamic circuit model This model does not consider the effect of temperature on OCV-SOC [7]. Lipu et al proposed a recurrent nonlinear autoregressive with exogenous input neural network model for SOC estimation of battery [15]. A convolutional gated recurrent unit (CNNGRU) is proposed for SOC estimation of lithium-ion batteries. Experimental results show that the proposed network outperforms popular deep-learning methods like GRU and RNN and traditional machine learning methods such as extreme learning machine (ELM) and support vector machine (SVM) in terms of estimation errors like RMSE and MAE, with all models well trained on lithium-ion battery datasets.
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