Fault diagnosis for battery systems is essential for ensuring safe operation of electric vehicles (EVs). In this study, a novel model for battery fault diagnosis is established by combining the long short-term memory recurrent neural network (LSTM) and the equivalent circuit model (ECM). The modified adaptive boosting method is utilized to improve diagnosis accuracy, and a prejudging model is employed to reduce computational time and improve diagnosis reliability. Considering the influence of drivers’ behaviors on battery systems, the proposed diagnosis scheme is able to achieve potential failure risk assessment and accordingly to issue early thermal runaway warning. A large volume of real-world operation data is acquired from the National Monitoring and Management Center for New Energy Vehicles (NMMC-NEV) in China to verify its robustness, feasibility and reliability. The results show that the proposed method exhibits superior performance, and can achieve accurate fault prediction for potential battery cell failure and precise locating of thermal runaway cells.

Efficient battery thermal runaway prognosis is of great importance for ensuring safe operation of electric vehicles (EVs). This presents formidable challenges under widely varied and ever-changing driving conditions in real-world vehicular operations. In this article, an enabling thermal runaway prognosis model based on abnormal heat generation (AHG) is proposed by combining the long short-term memory neural network (LSTM) and the convolutional neural network (CNN). The memory cell of the LSTM is modified and the resultant modified LSTM-CNN serves to provide accurate battery temperature prediction. The principal component analysis is used to optimize the model input factors to improve prediction accuracy and to reduce computing time. A random adjacent optimization method is employed to automatically optimize the hyperparameters. Finally, a model-based scheme is presented to achieve AHG-based thermal runaway prognosis. Real-world EV operating data are used to verify the effectiveness and robustness of the proposed scheme. The verification results indicate that the presented scheme exhibits accurate 48-time-step battery temperature prediction with a mean-relative-error of 0.28% and can realize 27-min-ahead thermal runaway prognosis.

Unmanaged cell inconsistency may cause accelerated battery degradation or even thermal runaway accidents in electric vehicles (EVs). Accurate cell inconsistency evaluation is a prerequisite for efficient battery health management to maintain safe and reliable operation and is also vital for battery second-life utilization. This article presents a cell inconsistency evaluation model for series-connected battery systems based on real-world EV operation data. The open-circuit voltage (OCV), internal resistance, and charging voltage curve are extracted as consistency indicators (CIs) from a large volume of electric taxis' operation data. The Thevenin equivalent circuit model is adopted to delineate battery dynamics, and an adaptive forgetting factor recursive least-squares method is proposed to reduce the fluctuation phenomenon in model parameter identification. With a modified robust regression method, the evolution characteristics of the three CIs are analyzed. The Mahalanobis distance in combination with the density-based spatial clustering of applications with noise is employed to comprehensively evaluate the multiparameter inconsistency state of a battery system based on the CIs. The results show that the proposed method can effectively assess cell inconsistency with high robustness and is competent for real-world applications.

The overdischarge can significantly degrade a lithium-ion (Li-ion) battery's lifetime. Therefore, it is important to detect the overdischarge and prevent severe damage of the Li-ion battery. Depending on the battery technology, there is a minimum voltage (cutoff voltage) that the battery is allowed to be discharged in common practice. Once the battery voltage is below the cutoff voltage, it is considered as overdischarge. However, overdischarge will not lead to immediate failure of the battery, and if it is not detected, the battery voltage can increase above the cutoff voltage during charging process. How to detect an overdischarge has happened, while the current voltage is larger than the cutoff voltage, thus becomes very challenging. In this article, a machine learning based two-layer overdischarge fault diagnosis strategy for Li-ion batteries in electric vehicles is proposed. The first layer is to detect the overdischarge by comparing the battery voltage with cutoff voltage, like what is utilized in common practice. If the battery voltage is larger than the cutoff voltage, the second layer, which is a detection approach based on eXtreme Gradient Boosting algorithm, is triggered. The second layer is employed to detect the previous overdischarge. The proposed method is validated by real electric vehicle data.