The safety of lithium-ion batteries (LIBs) in the battery energy storage station (BESS) is attracting increasing attention. To ensure the safe operation of BESS, it is necessary to detect the battery internal short circuit (ISC) fault which may lead to fire or explosion. This article proposes an early battery ISC fault diagnosis method based on the multivariate multiscale sample entropy (MMSE). The voltage, current, and temperature of the battery are utilized to extract the fault feature. The wavelet denoising method are employed to improve the MMSE performance. The adaptable threshold is proposed to diagnose the early ISC fault and prevent misdiagnosis. Finally, the ISC fault experiment and the battery overcharge experiment are used to validate the efficiency of the proposed early ISC diagnosis method. The real BESS operation data are used to validate the robustness of the proposed method. The results shows that the proposed method is effective in diagnosing the early ISC fault.

Electric vehicles (EVs) have attracted more and more attention around the world due to their excellent carbon reduction capability. However, EVs have always had the challenge of “range anxiety.” One possible way to solve this challenge is the state of charge (SOC) probabilistic estimation. It gives the estimated SOC value, the upper and lower bounds of SOC during the driving. The upper/lower bound reflects the maximum possible upper/lower SOC value, which can help optimistic/prudent users make reasonable decisions. This article has proposed a novel state-of-the-art probabilistic estimation method based on natural gradient boosting (NGBoost). The performance of the proposed method is validated on a public dataset and the self-made experimental datasets, which contain various battery chemistries, operation conditions, and ambient temperatures. For probabilistic estimation, the results show that the estimated intervals can better contain the true values than only the deterministic estimation, giving sufficient consideration and richer information. For deterministic estimation, compared with the existing methods, the proposed method can achieve better mean errors. This work highlights the promise of reducing “range anxiety” based on state-of-charge probabilistic estimation with NGBoost.

The increasing proportion of electronic devices in modern electric power systems inevitably aggravates the hazard caused by harmonics, such that the filter in electric converter substation is essential to eliminate the harmonics. In this work manta ray foraging optimization (MRFO) is employed to optimize the internal capacitance distribution of filtering equipment in the substation with renewable energy integration, which aims to reduce its unbalanced current that is created by the alerted capacitance to protect the equipment. To this end, this paper establishes the internal capacitor H-bridge model, the measured data obtained from ±500kV Yunnan Luquan Converter Station are adopted for simulation. Then, the original data are compared with the data optimized by the MRFO, genetic algorithm (GA), and particle swarm optimization (PSO) respectively. At last, it is validated that MRFO algorithm has higher convergence rate and higher quality results.

Internal short circuit (ISC) faults are a major cause of thermal runaway in lithium-ion batteries, and their occurrence is becoming an increasingly significant global challenge. Early-stage diagnosis of ISC faults can prevent the fault from worsening, thereby extending the lifespan of the battery. ISC faults cause the cell voltage amplitude to remain consistently lower than that of a normal cell. Based on this characteristic, this paper proposes a simple but effective diagnostic method. Specifically, the histogram is used to divide the voltage into several subintervals to calculate the discrete probability distribution, and the Bhattacharyya coefficient is proposed to compare the differences in the discrete probability distributions between normal and ISC cells. The substitute ISC fault experiment first confirms that, under leakage current levels of 1/417 C to 1/71 C, the proposed method can diagnose ISC faults within 1-2 complete charge-discharge cycles. The effectiveness of the proposed method is also validated through a real ISC fault experiment, namely the nail penetration test. Finally, by comparing with the conventional Pearson correlation coefficient, the proposed method’s advantages in sensitivity for early-stage ISC fault diagnosis and robustness against noise are proven.

Lithium-ion battery energy storage systems (BESSs) face significant safety challenges arising from internal short-circuit (ISC) faults, which can ultimately trigger thermal runaway. To address this, this paper proposes an ISC fault diagnosis method based on multivariate information entropy (MIE). The proposed approach fuses voltage and temperature time series from battery cells to extract fault features via MIE. Furthermore, a hierarchical diagnosis framework incorporating statistical confidence intervals is developed to enable robust ISC fault diagnosis. Experiments were conducted on 180 Ah lithium iron phosphate batteries, utilizing external resistors to simulate ISC faults of varying severity. The method was further validated using real-world fault data from an electric vehicle accident. Results demonstrate that the proposed method effectively distinguishes between normal and faulty cells, with MIE values exhibiting a monotonic increase as fault severity intensifies. In the real-world dataset, the method identifies the faulty cell 240 s before a discernible voltage drop, demonstrating its capability for early ISC detection.