A Vehicle-Cloud Collaborative Strategy for State of Energy Estimation based on CNN-LSTM Networks

With the current market of electric vehicles (EVs) in full swing, the real-time and accuracy of lithium batteries are getting more and more attention. Due to the EV's complexity and changeable external environment, an accurate energy estimation is difficult to achieve in the vehicle system. Although the machine learning algorithm can significantly improve the accuracy of battery estimation, it cannot be performed on the vehicle control unit as it requires a large amount of data and computing power. This paper proposes a state of energy (SOE) prediction algorithm, which combines long short-term memory (LSTM) and convolutional neural networks (CNN) for EVs based on vehicle-cloud fusion. With the validation of the Center for Advanced Life Cycle Engineering battery data set, the error of the proposed method is kept within 3%, and the feasibility of vehicle-cloud collaboration is promising in future battery management.