Deep Learning with Spatial Attention-Based CONV-LSTM for SOC Estimation of Lithium-Ion Batteries
Huixin Tian, Jianhua Chen
Abstract
Accurate estimation of the state of charge (SOC) is an indispensable part of a vehicle management system. The accurate estimation of SOC can ensure the safe and reliable operation of the vehicle management system. With the development of intelligent transportation systems (ITS), vehicles can not only obtain the dynamic changes inside the battery through sensors, but also obtain the traffic information around the vehicle through vehicle–road collaboration. In addition, the development of onboard graphic processing units (GPUs) and Internet of Vehicles (IOV) technology make the computing power of vehicles no longer limited by hardware, which makes neural networks applied to the intelligent control of vehicles. Aiming at the problem that the traditional network cannot effectively obtain the complex spatial information of sample attributes, we developed an attention-based CONV-LSTM module for SOC prediction based on a convolutional neural network (CNN) and a long short-term memory (LSTM) network. Different from the traditional LSTM network, the algorithm not only considers the temporal correlation of the data stream, but also captures the spatial correlation information of the input data by convolution. It then uses the different weights, automatically assigned by the attention mechanism, to correctly distinguish the importance of different input data streams. In order to verify the validity of the model, this paper selects the degradation data set of the aeroengine as the verification data set. Experiments show that the proposed model has achieved good results. Finally, the proposed model is applied to the actual vehicle running data, and the effectiveness of the proposed model is verified by comparing it with the Multi-Layer Perceptron (MLP), LSTM, and CNN-LSTM models.