Lithium-ion batteries are widely used in many fields, and accurate prediction of their remaining useful life (RUL) was crucial for effective battery management and safety assurance. In order to solve the problem of reduced RUL prediction accuracy caused by the local capacity regeneration phenomenon during battery capacity degradation, this paper proposed a novel RUL prediction method, which combined complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) technique with an innovative hybrid prediction strategy that integrated the support vector regression (SVR) and the long short-term memory (LSTM) networks. First, CEEMDAN was used to decompose the battery capacity data into high-frequency and low-frequency components, thereby reducing the impact of capacity regeneration. Subsequently, the SVR model predicted the low-frequency component that characterized the main degradation trend, and the high-frequency component that contained capacity regeneration features was predicted using an LSTM network optimized by the sparrow search algorithm (SSA). Finally, the final RUL prediction was obtained by combining the predictions of the two models. Experimental results on NASA public datasets showed that the proposed hybrid method significantly outperformed existing methods: the RMSE of the methods proposed in this paper were all less than 0.0086 Ah, the MAE were all less than 0.0060 Ah, the R² values were all higher than 0.96, and the RUL prediction errors were controlled within one cycle. This method gave full play to the complementary advantages of SVR and LSTM and provided an accurate and reliable solution for RUL prediction of lithium-ion batteries.

In this study, we investigate the challenges associated with dynamic time slot server selection in mobile edge computing (MEC) systems. This study considers the fluctuating nature of user access at edge servers and the various factors that influence server workload, including offloading policies, offloading ratios, users’ transmission power, and the servers’ reserved capacity. To streamline the process of selecting edge servers with an eye on long-term optimization, we cast the problem as a Markov Decision Process (MDP) and propose a Deep Reinforcement Learning (DRL)-based algorithm as a solution. Our approach involves learning the selection strategy by analyzing the performance of server selections in previous iterations. Simulation outcomes show that our DRL-based algorithm surpasses benchmarks, delivering minimal average latency.

Circulating current suppression can effectively improve the reliability and redundancy of parallel inverter systems. The mechanism and influencing factors of the low- and high-frequency zero-sequence circulating current (ZSCC) are analyzed in this study. Based on a mechanism analysis and the built mathematical model, the composite control strategy of zero-sequence voltage difference (ZSVD) compensation and virtual inductance is proposed to suppress the low- and high-frequency ZSCC. In the composite control strategy, the low-frequency ZCSS (LF-ZCSS) is suppressed by the ZSVD compensation method, and the HF-ZCSS is suppressed by the virtual inductance method. The ZSVD compensation is incorporated into the SVPWM as an input to regulate the modulation and suppress the LF-ZCSS, and virtual inductance is introduced into the current control loop to suppress the HF-ZCSS. A parallel system prototype composed of two 6 kW inverters is constructed. The experiments show that the ZSCC of the parallel system is reduced by 45%, which verifies the effectiveness of the proposed composite control strategy.

With the development of power conversion systems or bidirectional grid-connected inverters characterized by high DC voltage, high efficiency, and high-power density, high-switching-frequency SiC power switches are being widely used, and these require a short computational time of control algorithm. Based on the sector judgment of a space voltage vector and the midpoint potential control balancing of a DC bus, an improved finite control set method prediction control (FCS-MPC) method was proposed for a T-type three-level PCS. This improved FCS-MPC first judges the sector of the space voltage vector and locates the position of the corresponding large sector according to phase lock information; then, it analyzes the sampled voltage of the upper and lower bus capacitors to obtain the midpoint potential situation and selects appropriate small vectors based on the midpoint potential situation. This simple improved strategy can reduce the computational complexity of traditional MPC for rolling optimization, resulting in a reduction in the vectors from 27 to 8. A SiC-based 10 kW bidirectional PCS prototype verified the improved FCS-MPC, and the computational time was reduced about by 56% in comparison to traditional FCS-MPC.

In this paper, we study the dynamic time-slot scheme server selection problem in mobile edge computing (MEC) system, which takes into account the dynamic access of users at edge servers and introduces the dynamically changing factors affecting the workload of edge servers, such as offloading policies, offloading ratios, user’s transmitting power, and reserved capacity of the servers. However, the above factors make it tricky to achieve long-term optimization in the edge server selection process. To cope with the above challenges, we model the server selection problem faced as a Markov Decision Process (MDP) and propose a Deep Reinforcement Learning (DRL) based algorithm to solve it. Selection strategy learning is performed based on the observed server selection performance in previous phases. Simulation results show that the DRL-based algorithm proposed in this paper has the lowest average latency compared to benchmark algorithms.