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.

Comprehensive and accurate estimation methods of the state of health (SOH) of battery systems play a significant role in online monitoring for the safe and reliable operation of electric vehicles (EVs). Most existing estimation techniques are established on top of data from well-controlled experimental environments and tend to focus on a single health feature, thus not practical for real-world EVs’ SOH monitoring throughout the life cycle. To address this problem, based on real-world EV operation data, a novel SOH estimation model is presented for LiFePO ₄ battery systems in EVs with consideration of degradation mechanisms. The ohmic resistance and the peak value of the incremental capacity (IC) curve are extracted from a large number of electric buses as health features. The influence of temperature on the ohmic resistance is eliminated by exponential fitting, which transforms ohmic resistance into the relative change rate of ohmic resistance. With joint considerations of the two health features and the mechanism of battery aging, three stages of SOH degradation can be quantitively described. The test results show that the proposed model is able to track SOH degradation from 0 to over 300 000 km and reflect the SOH of the battery more comprehensively in the early and late life cycles of the battery compared with single-health-feature methods.

Hundreds of thermal-runaway-induced battery fire accidents have been occurring to real-world electric vehicles (EVs) recent years, exposing life to danger and causing property losses. Timely and fast battery thermal runaway prognosis is essential but restricted by limited parameters and complex influencing factors during real-world operation of EVs, i.e., environment, driving behavior, and weather. To cope with the issue, several data-driven methods are combined and thermal runaway prognosis is realized by two steps, i.e., temperature prediction by the modified extreme gradient boosting (XGBoost) then abnormality detection by principal components analysis (PCA) and density-based spatial clustering of applications with noise (DBSCAN). The XGBoost is modified and trained by data of real-world EVs to couple the influencing factors during real-world operation of EVs. For parameter optimization, the ‘pre-training and adjacent grid optimizing method’(P-AGOM) and ’adjacent grid optimizing method’ (AGOM) are proposed to achieve locally optimal hyperparameters for XGBoost and DBSCAN. Verified results showcase the XGBoost-PCA-DBSCAN achieves accurate 5-minute-forward temperature prediction and the mean-square-errors (MSEs) of four seasons are only 0.0729, 0.0594, 0.0747, 0.0523 respectively. By modification of XGBoost, the MSE of temperature prediction is reduced by 31.2%. In addition, 35-minute-forward thermal runaway prognosis by the XGBoost-PCA-DBSCAN will provide driver sufficient response time to minimize the loss of life and property.

Battery system diagnosis and prognosis are essential for ensuring the safe operation of electric vehicles (EVs). This paper proposes a diagnosis method of thermal runaway for ternary lithium-ion battery systems based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering. Two-dimensional fault characteristics are first extracted according to battery voltage, and DBSCAN clustering is used to diagnose the potential thermal runaway cells (PTRC). The periodic risk assessing strategy is put forward to evaluate the fault risk of battery cells. The feasibility, reliability, stability, necessity, and robustness of the proposed algorithm are analyzed, and its effectiveness is verified based on datasets collected from real-world operating electric vehicles. The results show that the proposed method can accurately predict the locations of PTRC in the battery pack a few days before the thermal runaway occurrence.

Battery voltage is a pivotal parameter for evaluating battery health and safety. The precise prediction of battery voltage and the implementation of anomaly detection are imperative for ensuring the secure and dependable operation of battery systems. Nevertheless, during the actual operation of electric vehicles, battery performance is subject to the influence of the vehicle's operational state and battery characteristic parameters, introducing challenges to safety alerts. In order to address these challenges and achieve precise battery voltage prediction, this paper comprehensively considers the battery characteristics and driving behavior of electric vehicles in both charging and operational states. Mathematical processing, including averaging and variance calculation, is applied to the battery characteristic parameter data and driving behavior data. By integrating historical voltage data and employing a modified gradient boosting decision tree algorithm (GBDT), a fast and accurate online voltage prediction method is proposed. Hyperparameter optimization is employed to minimize prediction voltage errors. The accuracy and timeliness of the predictions are validated through a comprehensive evaluation and comparison of the forecasted voltages. To diagnose anomalies in battery voltage, the paper proposes a fault diagnosis method that combines the Isolation Forest and Boxplot techniques. Finally, utilizing authentic electric vehicle data for validation, the research underscores the capability of the proposed method to achieve accurate voltage predictions six minutes in advance and provide effective fault diagnosis. This investigation carries substantial practical implications for fortifying battery management and optimizing the performance of electric vehicles.

Battery safety issue is developing as one of the main hinders restricting the further application of real-world electric vehicles (EVs). Internal resistance (IR) is one of the important parameters to reflect battery safety, because bigger abnormal IR will cause more heat generation and make the battery easier to cross the critical condition of thermal runway. Safety risk assessment based on abnormal IR can locate this kind unsafe batteries and ensure the safe operation of EVs. Existing studies mostly focus on IR estimation in laboratory, yet is insufficient in real-world abnormal IR detection, which is influenced by random conditions and coupled factors. To cope with this issue, a method is proposed to detect unsafe battery, thereby predicting the thermal runaway. The method can be divided into three parts, i.e., IR estimation, normal IR prediction, and IR evolution law construction and safety risk assessment. First, we analyze the problems of battery IR estimation in real-world scenarios, and propose a robust method to estimate the IR only based on sparse voltage and current. Second, a novel hybrid neural network model is designed and trained to predict normal IRs inputted by temperature, mileage, and state-of-charge. The model combines the advantages of different neural network structures to improve the performance. A double-layer grid searching method is proposed to optimize hyperparameters of the hybrid neural network, aiming to achieve the best accuracy under constructed neural network structure. Finally, the safety boundary indicated by IR is formed by statistic method and the IR evolution law is constructed, then a strategy is proposed to make residual evaluation for safety risk assessment. All the proposed methods are driven by EV historical data to reflect real-world conditions. The results showcase that the method is effective in the cloud for the small IR prediction error with the mean-square-error, mean-relative-error, and max error of 2.45*e-4, 1.78%, and 0.15. In addition, the method can periodically classify the unsafe and normal batteries with 98.6% accuracy for the EVs with the same specification.