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Aiming at the problem that the vibration signals of rolling bearings working in a harsh environment are mixed with many harmonic components and noise signals, while the traditional sparse representation algorithm takes a long time to calculate and has a limited accuracy, a bearing fault feature extraction method based on the ensemble empirical mode decomposition (EEMD) algorithm and improved sparse representation is proposed. Firstly, an improved orthogonal matching pursuit (adapOMP) algorithm is used to separate the harmonic components in the signal to obtain the filtered signal. The processed signal is decomposed by EEMD, and the signal with a kurtosis greater than three is reconstructed. Then, Hankel matrix transformation is carried out to construct the learning dictionary. The K-singular value decomposition (K-SVD) algorithm using the improved termination criterion makes the algorithm have a certain adaptability, and the reconstructed signal is constructed by processing the EEMD results. Through the comparative analysis of the three methods under strong noise, although the K-SVD algorithm can produce good results after being processed by the adapOMP algorithm, the effect of the algorithm is not obvious in the low-frequency range. The method proposed in this paper can effectively extract the impact component from the signal. This will have a positive effect on the extraction of rotating machinery impact features in complex noise environments.

Effective balanced management of battery packs can not only increase the available capacity of a battery pack but reduce attenuation and capacity loss caused by cell inconsistencies and remove safety hazards caused by abnormal use such as overcharge and over-discharge. This research considers both the equilibration period and the battery operating current. The State of Charge (SOC), current, and equalization current of batteries are all limited. Based on the existing multi-layer equalization model, the equalization current of the equalizer was tuned with restrictions. It can equalize multiple batteries simultaneously and ensure the normal operation of the batteries. A layered control strategy was then found to solve the optimal equalization current of the equalizer layer by layer. The proposed control method reduces computation time and guarantees that the equalization approach can be employed in practice. Finally, through MATLAB simulation analysis, this technique can limit the cell current to (−3 A, 3 A), which improves the balancing efficiency by 23.55% compared with the balancing of adjacent cells.