###### Abstract  
State-of-charge (SOC) estimation of lithium-ion battery is a key parameter of the battery management system (BMS). However, SOC cannot be obtained directly. In order to predict SOC accurately, we proposed a recurrent neural network called gated recurrent unit network that is based on genetic algorithm (GA-GRU) in this paper. GA was introduced to optimize the key parameters of the model, which can improve the performance of the proposed network. Furthermore, batteries were tested under four dynamic driving conditions at five temperatures to establish training and testing datasets. Finally, the proposed method was validated on dynamic driving conditions and compared with other deep learning methods. The results show that the proposed method can achieve high accuracy and robustness.  
lithium-ion battery; state-of-charge estimation; battery management system; genetic algorithm; gated recurrent unit neural network +
Footnote †: journal: Applied  
0000-0001-998-2281-228  
These filtering algorithms can update the SOC on the basis of the state-space equations of the model and the covariance matrix. However, this requires a lot of expertise, and the accuracy of SOC estimation is highly dependent on the model. In order to for shortcomings of traditional and model-based methods to be overcome, data-driven methods are applied to estimate SOC.  
Data-based methods can address the strong nonlinear and time-varying issues of SOC estimation that can accurately map the observable parameters of batteries to SOC. [PERSON] et al. [18] proposed a back-propagation neural network (BP) for SOC estimation. In [19], a support vector machine (SVM) was set up to extract model parameters for SOC, obtaining a high level of accuracy. Due to the rapid development of computer hardware, such as graphics processing unit (GPU), deep learning methods are widely used for SOC estimation. [PERSON] et al. [20] proposed a deep convolutional neural network (DCNN) to achieve good estimation results. In consideration of the excellent performance of long short-term memory neural network (LSTM) in time series data, the LSTM-RNN has been introduced to perform SOC estimation [21]. However, due to the complex structure of LSTM, a new recurrent neural network called gated recurrent unit neural network (GRU) was applied for SOC estimation [22]. In [23], a hybrid neural network called convolution gated recurrent unit network (CNN-GRU) was proposed for SOC estimation.  
The methods mentioned above all need to manually set network parameters. In order for the optimal network parameters for improving network performance to be automatically found, the genetic algorithm (GA) was proposed. In this paper, GA was used to optimize the parameters of gated recurrent unit network (GRU). Moreover, the optimized parameters included the number of GRU layers and the fully connected layers (FCL), as well as the number of neurons of the corresponding layer. The rest of this paper is organized as follows. The proposed SOC estimation method is introduced in Section 2. Then, Section 3 establishes the experimental bench for acquiring the dataset. Next, validation of the proposed model and analysis of results are carried out in Section 4. Finally, Section 5 contains the conclusions.