Battery fault diagnosis is crucial for stable, reliable, and safe operation of electric vehicles, especially the thermal runaway early warning. Developing methods for early failure detection and reducing safety risks from failing high energy lithium-ion batteries has become a major challenge for industry. In this article, a real-time early fault diagnosis scheme for lithium-ion batteries is proposed. By applying both the discrete Fréchet distance and local outlier factor to the voltage and temperature data of the battery cell/module that measured in real time, the battery cell that will have thermal runaway is detected before thermal runaway happens. Compared with the widely used single parameter based diagnosis approach, the proposed one considerably improve the reliability of the fault diagnosis and reduce the false diagnosis rate. The effectiveness of the proposed method is validated with the operational data from electric vehicles with/without thermal runaway in daily use.

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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.@en

It is vital to detect the safety state and identify faults of the battery pack for the safe operation of electric vehicles. The voltage faults such as over-voltage and under-voltage imply more serious battery faults including short-circuit and thermal runaway. The voltage abnormal fluctuation is a warning signal of short-circuit, over-voltage and under-voltage. This paper proposes a scheme of three-layer fault detection method for lithium-ion batteries based on statistical analysis. The first layer fault detection is based on the thresholds of over-charge and over-discharge of a battery pack. In the second layer, confidence interval estimation is applied to identify risky cells. In the third layer, correlation and variability of all cells in one battery pack are analyzed by using an improved K-means method to identify abnormal voltage fluctuation over a certain period. The validity and feasibility of the proposed method are verified by real vehicle data from the National Big Data Alliance of New Energy Vehicles.

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