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To realize the online rapid prediction of the remaining useful life (RUL) of electric vehicle (EV) power battery under direct current (DC) fast charging conditions and reduce the influence of complex health indicator (HI) on the prediction accuracy, an adaptive prediction method of the RUL of EV power battery based on whale swarm algorithm–long short-term memory (WSA-LSTM) algorithm is proposed in this paper. Firstly, a complete set of HI is built based on a dynamic data-driven application system (DDDAS) to ensure the real-time combination of condition monitoring data and simulation systems. Then, combined with the calculation of Pearson and Spearman correlation coefficients and the entropy weight method, the main features that affect the battery capacity are screened out to reduce the interference of secondary HI on RUL prediction. Thirdly, the whale swarm algorithm is used to globally optimize the hyperparameters of the long short-term memory to achieve rapid RUL prediction. Finally, we conduct simulation experiments based on real-time data under EV DC charging conditions. The simulation results show that the HI screening method proposed in this paper is effective, and the RUL prediction method can achieve a fast and accurate prediction of DC fast charging conditions.

The performance of pollutant removals, tetracycline (TC) and norfloxacin (NOR) removals, membrane fouling mitigation and the microbial community of three Anoxic/Oxic membrane bioreactors (AO-MBRs), including a moving bed biofilm MBR (MBRa), a fixed biofilm MBR (MBRb) and an AO-MBR (MBRc) for control, were compared in treating antibiotic-containing synthetic mariculture wastewater. The results showed that MBRb had the best effect on antibiotic removal and membrane fouling mitigation compared to the other two bioreactors. The maximum removal rate of TC reached 91.65% and the maximum removal rate of NOR reached 45.46% in MBRb. The addition of antibiotics had little effect on the removal of chemical oxygen demand (COD) and ammonia nitrogen (NH4+-N)—both maintained more than 90% removal rate during the entire operation. High-throughput sequencing demonstrated that TC and NOR resulted in a significant decrease in the microbial diversity and the microbial richness MBRs. Flavobacteriia, Firmicutes and Azoarcus, regarded as drug-resistant bacteria, might play a crucial part in the removal of antibiotics. In addition, the dynamics of microbial community had a great change, which included the accumulation of resistant microorganisms and the gradual reduction or disappearance of other microorganisms under antibiotic pressure. The research provides an insight into the antibiotic-containing mariculture wastewater treatment and has certain reference value.