State of Charge Estimation for Lithium Battery Via Attention and LSTM Algorithm

The use of fossil fuels has the problems of non-renewable and environmental pollution. Lithium battery has the tremendous advantages of low self-discharge rate, extremely high energy density, high working voltage and zero memory impact. The State of Charge (SOC) is circumscribed as the remaining charge of the lithium battery (Li-ion), that indicates the driving range of a pure electric vehicle. Additionally, it is the basis for SOH and fault state prediction. Nevertheless, the SOC is incapable of measuring directly. In this paper, an LSTM + Attention network framework is proposed. The LSTM algorithm is accustomed to present the timing information and past state information of the lithium battery data. The Attention algorithm is used to extract the global information of features and solve the problem of long-term dependency. To ensure the diversity of feature extraction, the Attention algorithm in this paper uses multi-headed self-attentiveness. The CACLE dataset from the University of Maryland is used in this paper. Through the training of the model and the comparison, it is concluded that the LSTM + Attention algorithm networks proposed in this article can predict the value of SOC well. Meanwhile, this paper compares the LSTM + Attention algorithm with the LSTM algorithm, and also compares the LSTM + Attention algorithm with the Attention algorithm. Finally, it is concluded that the accomplishment of the network framework contrived is superior to the performance of these two algorithms alone. Finally, the algorithm has good engineering practice implications. The algorithm proposed provides a better research direction for future parameter prediction in the field of lithium batteries. It has a better theoretical significance.