Fault diagnosis accuracy in lithium-ion battery-based energy storage systems is significantly constrained by the limited availability of fault-specific datasets. This study addresses this critical issue by proposing a diffusion-based data augmentation methodology tailored explicitly for battery fault diagnosis scenarios. The proposed conditional diffusion model leverages transfer learning and attention-enhanced fine-tuning strategies to generate high-quality synthetic fault data, ensuring targeted representation of rare fault conditions. By integrating condition-aware sampling strategies, the approach effectively mitigates mode collapse issues frequently encountered in adversarial generative methods, thus substantially enriching the diversity and quality of fault representations. Comprehensive evaluation using statistical similarity measures and downstream classification tasks demonstrates notable improvements. After the integration of attention mechanisms, the Pearson correlation coefficient between the synthetic and real samples increases from 0.29 to 0.91. In downstream diagnostic tasks, models trained on augmented datasets exhibit substantial gains in regards to the recall and F1-score, which increase from near-zero levels to values exceeding 0.91 for subtle overcharge and overdischarge faults. These results confirm the effectiveness and practical utility of the proposed augmentation approach in enhancing diagnostic performance under data-scarce conditions.

Due to the increasing penetration of renewable power generation, the decreasing inertia of power system incurs frequent frequency fluctuation. Considering the limited performance of traditional thermal generator and insufficient reserve capacity, frequency regulation cannot be effectively addressed. In such a case, owing to the ability of fast response, energy storage system (ESS) is regarded as a feasible way to work with traditional thermal generator to promote the total performance. In this paper, a novel control strategy of hybrid energy storage system (HESS) is presented aiming to improve the AGC response speed and precision of HESS-generator system. Case studies are carried out to demonstrate the performance of the proposed method.

Pseudorandom sequence (PRS) offers a rapid impedance measurement solution for lithium-ion batteries. However, PRS suffers from limited accuracy in the mid-to-low frequency range due to the low power content and sparse distribution of harmonics. Motivated by this, a parallel quadratic residue ternary signal with an almost theoretical minimum measurement time is proposed. By enhancing the power in mid-to-low frequency band, the first few low-frequency impedance points can be accurately obtained without any additional filters. Moreover, a two-stage filter with an iterative procedure is introduced, which eliminates outliers using a predefined threshold and obtains the filtered impedance through Fourier series fitting. Experimental validation proves that the proposed method achieves a max normalized root means square error of 0.59% and max absolute residual of 0.48% under various states of charge, states of health and temperatures, confirming its effectiveness for impedance measurement.