Large-capacity lithium iron phosphate (LFP) batteries dominate energy storage systems, but their degradation characteristics differ from small-capacity cells. Most existing life prediction methods require complete voltage–current time-series data, which is hard to obtain in practical operation. This paper proposes two life prediction methods: a data-driven method and an empirical-data hybrid method. The data-driven method adopts Summed Voltage Falloff (SVF) extracted from partial voltage segments as the health indicator, which removes the dependence on full charge–discharge waveform data and enhances engineering practicability. It uses a unified numerical fitting framework with Gaussian process regression (GPR) residual correction, with tailored fitting strategies for 320 Ah and 298 Ah battery datasets. The hybrid method integrates the Arrhenius model with Kalman filtering for closed-loop online prediction correction. Both methods are validated using 281 cycles of 320 Ah battery data, and the data-driven method is further verified with 150 cycles of 298 Ah battery data. Results show that the data-driven method performs better with limited data, suitable for offline one-time inspection scenarios; the hybrid model achieves higher accuracy with sufficient data, applicable to long-term online remaining useful life monitoring. The data-driven method yields a worst-case cycle life of 3304 cycles at 2σ confidence level, and the hybrid model maintains error below 5% when forecasting 1000 cycles with 200 cycles of training data.

Dissolved gas analysis (DGA) is an essential technique for the fault diagnosis and condition monitoring of oil-immersed power transformers. Among various characteristic gases, acetylene (C₂H₂) is a key indicator of high-energy discharge and arc faults. In this work, a high-sensitivity dissolved acetylene detection system is developed based on clamp-type quartz-enhanced photoacoustic spectroscopy (QEPAS). A specially designed clamp-type quartz tuning fork (Clamp-type QTF) is employed as the acoustic transducer to improve acoustic coupling efficiency and optical alignment tolerance. Compared with conventional standard quartz tuning forks, the clamp-type structure exhibits enlarged acoustic interaction volume, lower damping loss, and higher signal collection capability. A near-infrared distributed feedback (DFB) laser operating at 1531.6 nm is used as the excitation source. The dissolved gas is extracted from transformer oil using a headspace degassing module and introduced into the QEPAS cell for real-time measurement. Experimental results showed that the developed system achieves a 1σ-based SNR-estimated detection limit of 17 ppb at a 50 s integration time, derived from the continuous measurement of 0.75 ppm C₂H₂, with excellent linearity in the concentration range from 100 ppm to 500 ppm. The measured concentration of dissolved acetylene in transformer oil is in good agreement with gas chromatography (GC), validating the effectiveness and practical applicability of the proposed system.

Consistency monitoring is essential for safe battery energy storage system operation, yet practical data often exhibit asynchronous sampling. This study proposes a tick-driven multi-scale consistency analysis and unsupervised detection method for energy storage batteries. Actual cell-voltage update instants are used as analysis ticks, while high-frequency string-level variables are aggregated over adjacent tick intervals to describe operating conditions. Voltage-dispersion features are then used for anomaly scoring and cell-level localization. The method was evaluated using two-day station data and a controlled 20 Ah 16-series module experiment. In the station dataset, 190 effective ticks were extracted, and a transient consistency deterioration at 24,023 s showed a voltage range of 0.049 V and a standard deviation of 0.0057 V. In the controlled experiment, 185 ticks were obtained from 166,797 voltage samples after 900 s batch resampling, reducing cell-level evaluation instances by over 99%; cell #13 was identified as the dominant high-response cell. The method provides an interpretable framework for consistency monitoring and abnormal-cell localization under asynchronous sampling.