Method for Estimating the Health Status of Electric Vehicle Power Battery Based on BiLSTM-CNN Network
The estimation method of individual health status of data-driven electric vehicle batteries has become a current research hotspot,but the low quality of sensor data and low sampling frequency of sensors in practical applications can lead to a decrease in model accuracy.A new deep learning algorithm was proposed to accurately extract the aging information of lithium-ion batteries from actual charging curves.Firstly,the capacity increment curve was selected as the input parameter of the model;secondly,a deep learning model was designed that integrated bidirectional long short-term memory network and convolutional neural network;finally,the accuracy of the model was verified by simulating real charging data in the Oxford lithium-ion battery dataset.The experimental results indicate that the proposed model can accurately estimate the health status of batteries based on actual operating conditions,which helps to improve the safety and reliability of electric vehicle battery systems.
electric vehicle batteriesstate of health estimationdeep learning
万方数据
维普
