Dam breach has catastrophic consequences for human lives and economy. In previous studies, empirical models are often, to a limited extent, due to the inadequacy of historical dam breach events. Physical models, which focus on simulating human behavior during floods, are not suitable for fast analysis of a large number of dams due to the complexities of many key parameters. Therefore, this paper proposes a method for fast evaluation of potential consequences of dam breach. Eight main indices, i.e., capacity of reservoir (C_R), dam height (H_D), population at risk (P_R), economy at risk (E_R), understanding of dam breach (U_B), industry type (T_I), warning time (T_W), and building vulnerability (V_B), are selected to establish an evaluation index system. A catastrophe evaluation method is introduced to establish an evaluation model for potential consequences of dam breach based on the indices which are divided into five grades according to the relevant standards and guidelines. Validation of the method by twelve historical dam breach events shows a good accuracy. The method is applied to evaluate potential consequences of dam breach of Jiangang Reservoir in Henan Province, China. It is estimated that loss of life in the worst scenario is between that of Hengjiang Reservoir and that of Shimantan Reservoir dam breach, of which fatalities are 941 and 2717, respectively, showing that risk management measures should be taken to reduce the risk of potential loss of life.

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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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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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Risk consequences analysis of dam breach is fundamental to risk assessment of reservoir dams. Multiple influencing factors and complex mechanisms lead to great differences between the analysis results of different research methods and the actual consequences. It is suggested that risk consequences can be divided into three basic categories: loss of life, economic loss and environmental impact, based on their definitions. According to the analysis of research achievements and applications abroad, the development trend that physical model based on disaster-causing mechanism takes place of empirical model based on historical data is clarified. Based on the analysis of research status of dam breach risk consequences assessment in China, both advantages and disadvantages of three main research methods, i.e. analysis based on empirical model and disaster-causing mechanism, semi-quantitative evaluation and superposition calculation of regional losses, are clarified in terms of accuracy and practicability. It is proposed that the risk consequences research and practical application can be improved in four aspects, i.e., strengthening the analysis of disaster-causing mechanism, paying attention to the combination of accuracy and practicability, emphasizing the important roles of non-engineering measures and improving the research constantly during application.@en