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.
Highlights
Gated Recurrent Unit NeuralEnvironmental problems caused by fuel vehicles has become an inevitable issue [1].this issue has stimulated the rapid development of new energy vehicle technology.As a typical representative of new energy vehicles, electric vehicles can effectively reduce exhaust emissions
These four dynamic driving cycles were different temperatures (0 °C, 10 °C, 20 °C, 30 °C, and 45 °C); (4) the fourth battery was the Beijing Dynamic Stress Test (BJDST), the Federal Urban Driving Schedule (FUDS), the tested under US06 at five different temperatures (0 °C, 10 °C, 20 °C, 30 °C, and 45 °C)
In order to make an assessment of the superiority of the proposed method, we compared the state of charge (SOC) estimation results of recurrent neural network (RNN), long short-term memory neural network (LSTM), and genetic algorithm (GA)-gated recurrent unit neural network (GRU) under different dynamic test conditions and different temperatures, namely, DST at 0 ◦ C, FUDS at 10 ◦ C, BJDST at 20 ◦ C, DST at 30 ◦ C, and US06 at 30 ◦ C
Summary
Environmental problems caused by fuel vehicles has become an inevitable issue [1]. this issue has stimulated the rapid development of new energy vehicle technology. The battery must be rest for a long time period to achieve accurate open circuit voltage. All in all, both methods are simple and easy to implement, but they all have low precision. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations These filtering algorithms can update the SOC on the basis of the state-space equations of the model and the covariance matrix. 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. In [23], a hybrid neural network called convolution gated recurrent unit network (CNN-GRU) was proposed for SOC estimation.
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