Fault prognosis of battery system based on accurate voltage abnormity prognosis using long short-term memory neural networks

State prediction and fault prognosis are generating considerable interest regarding battery system due to the healthy development momentum of electric vehicles. Voltage is one of the main characterisation parameters for various battery faults, so accurate voltage abnormity prognosis is critical to the safe and durable operation of the battery system. A novel deep-learning-enabled method to perform accurate multi-forward-step voltage prediction for battery systems is investigated using long short-term memory(LSTM) recurrent neural network. A high volume of real-world operational data of an electric taxi is acquired from the Service and Management Center for electric vehicles(SMC-EV) in Beijing. To improve the prediction accuracy, a Weather-Vehicle-Driver analysis is implemented to consider the impacts of weather and driver's behaviour on a battery system's performance, and the many-to-one(4-1) model structure using an improved pre-dropout technology and a developed dual-model-cooperation prediction strategy is applied for offline training the LSTM models after all hyperparameters pre-optimized. The results showcase that the proposed method has a powerful prediction ability for battery voltage, and the accuracy and robustness of this method are verified through the comparisons among different hyperparameters and seasons using 10-fold cross-validation. Furthermore, combined with alarm or warning thresholds, the prognosis feasibility, stability, and reliability of the proposed models for various voltage abnormities are also verified through actual operational data, thereby this method can assess the battery safety via predicting voltage to determine the advent of battery faults and mitigate runaway risk. This is the first of its kind to apply the LSTM to voltage prediction and fault prognosis of the battery system.