High-performance carbon-coated mesoporous LiMn₂O₄ cathode materials have been synthesized from a novel hydrated layered-spinel lithium manganate composite.

The state of charge (SOC) is a core parameter in the battery management system for LMFP batteries. Accurate SOC estimation is crucial for ensuring the safety and reliability of energy storage applications and new energy vehicles. In order to achieve better SOC estimation accuracy, this article proposes an adaptive whale optimization algorithm (WOA) with chaotic mapping to improve the BP neural network (BPNN) model. The SOC estimation accuracy of the BPNN model was improved by utilizing WOA to find the optimal target weight values and thresholds. Comparative simulation experiments (including constant current and working condition discharge experiments) were conducted in Matlab/Simulink R2018a to verify the proposed algorithm and the other four algorithms. The experimental results show that the proposed algorithm had higher SOC estimation accuracy than the other four algorithms, and its prediction errors were less than 1%. This indicates that the proposed SOC estimation method has better prediction accuracy and stability, and has certain theoretical research significance.

A novel Si/C composite exhibits enhanced lithium storage performance benefiting from the combination of outermost carbon coating and a recrystallized NaCl-derived porous structure.

Condition monitoring and fault diagnosis in braking system of urban rail vehicles have important practical value, and can directly improve the stable operation of equipment and passenger comfort. In this study, a data integration system was established to consistently acquire accurate online information, and then the operating conditions were extracted from a massive historical dataset. Kernel principal component analysis (KPCA) is widely used for fault detection and diagnosis, however, the approach is limited by the high computational cost and memory requirements of large-scale training datasets. Herein, the reduced KPCA (RKPCA) approach is developed. The proposed method reduced redundant data in the training dataset by 32.84%. Furthermore, fault detection performance of RKPCA is superior to those of principal component analysis and KPCA.