Electric energy, as an economical and clean energy, plays a significant role in the development of science and technology and the economy. The motor is the core equipment of the power station; therefore, monitoring the motor vibration and predicting time series of the bearing vibration can effectively avoid hazards such as bearing heating and reduce energy consumption. Time series forecasting methods of motor bearing vibration based on sliding window forecasting, such as CNN, LSTM, etc., have the problem of error accumulation, and the longer the time-series forecasting, the larger the error. In order to solve the problem of error accumulation caused by the conventional methods of time series forecasting of motor bearing vibration, this paper innovatively introduces Informer into time series forecasting of motor bearing vibration. Based on Transformer, Informer introduces ProbSparse self-attention and self-attention distilling, and applies random search to optimize the model parameters to reduce the error accumulation in forecasting, achieve the optimization of time and space complexity and improve the model forecasting. Comparing the forecasting results of Informer and those of other forecasting models in three publicly available datasets, it is verified that Informer has excellent performance in time series forecasting of motor bearing vibration and the forecasting results reach ${10}^{-2}$∼${10}^{-6}$.

Dual active bridge (DAB) converters either adopt single-degree-of-freedom (SDOF) modulation or multiple-degree-of-freedom modulation. The SDOF single phase shift (SPS) modulation has low power transmission efficiency under light load, and the multiple-degree-of-freedom modulation has high requirements for model accuracy and controller design. Focusing on these limitations, this paper proposes an SDOF hybrid modulation method to optimize transmission efficiency, switching device current stress, and zero-voltage-switching (ZVS) implementation under the light-load condition. To this end, first, the SDOF SPS, primary-side internal phase shift (PS-IPS), and secondary-side internal phase shift (SS-IPS) modulation methods are comprehensively analyzed in terms of current stress, ZVS feature, and transmission power. Second, by comparing the current stress, ZVS, and loss features of SDOF modulation methods under the light-load condition, the case-dependent optimal modulation method is determined. On this basis, an SDOF hybrid modulation strategy within the light-load range is developed by resorting to a proper switching rule, which judges the voltage ratio between the two ends of the inductor and the size of the transmission power. Finally, a 600 W experimental prototype is built, and the results show that the proposed strategy effectively improves the transmission efficiency of the converter under the light-load condition.

In recent years, more and more attention has been paid to wind energy throughout the world as a kind of clean and renewable energy. Due to doubts concerning wind power and the influence of natural factors such as weather, unpredictability, and the risk of system operation increase, wind power seems less reliable than traditional power generation. An accurate and reliable prediction of wind power would enable a power dispatching department to appropriately adjust the scheduling plan in advance according to the changes in wind power, ensure the power quality, reduce the standby capacity of the system, reduce the operation cost of the power system, reduce the adverse impact of wind power generation on the power grid, and improve the power system stability as well as generation adequacy. The traditional back propagation (BP) neural network requires a manual setting of a large number of parameters, and the extreme learning machine (ELM) algorithm simplifies the time complexity and does not need a manual setting of parameters, but the loss function in ELM based on second-order statistics is not the best solution when dealing with nonlinear and non-Gaussian data. For the above problems, this paper proposes a novel wind power prediction method based on ELM with kernel mean p-power error loss, which can achieve lower prediction error compared with the traditional BP neural network. In addition, to reduce the computational problems caused by the large amount of data, principal component analysis (PCA) was adopted to eliminate some redundant data components, and finally the efficiency was improved without any loss in accuracy. Experiments using the real data were performed to verify the performance of the proposed method.

Lithium-ion battery state of health (SOH) estimation technology is an important part of the design of a battery monitoring system (BMS) for electric vehicles. People often use battery capacity and internal resistance as SOH estimation indicators. However, due to actual working conditions, it is difficult for electric vehicles to achieve complete charge and discharge, so the battery capacity and internal resistance cannot be monitored online. In view of the above questions, this article proposes an indirect SOH estimation method for online EV lithium-ion batteries based on arctangent function adaptive genetic algorithm combination with back propagation neural network (ATAGA-BP). Firstly, constant current drop time (CCDT), constant current drop capacity (CCDC) and maximum constant current drop rate (MCCDR) in constant voltage charging stage are used as health indicator (HI) to evaluate battery SOH in order to indirectly quantify the degradation process of lithium-ion batteries. Error point optimization and correlation verification are also carried out. Secondly, an ATAGA-BP algorithm is proposed to establish the relationship between HI and available battery capacity, and SOH estimate is made for lithium-ion batteries according to the proposed algorithm. Finally, simulation results with NASA data show the correlation between the proposed HI and lithium-ion battery capacity is above 85%, the error of SOH estimation method proposed is 3.7% 3.715%, and the iteration efficiency is increased by 17.8% 17.78%.

EV drivers have experienced a charging inconvenience due to a limited number of charging facilities and mileage anxiety due to the limited driving distance for a single full charge. This paper developed a user-friendly online EV charging guidance algorithm to cope with the two aforementioned issues using multi-agent deep reinforcement learning. First, three models, i.e., the traffic network model, charging station model, and EV driver model, are established, respectively, considering the traffic condition, the potential competition of future charging demand at charging stations, and the drivers’ mileage anxiety. Second, the charging guidance process is modeled as a Markov decision process, and charging stations are taken as agents. The attentional multi-agent actor-critic algorithm based on the centralized training with decentralized execution framework is built. Finally, compared to the comparison algorithm, the performance does not diminish with the increase in the number of agents, indicating that the approach has the scalability to be applied to large-scale agent systems. The model still has the generalization in extreme scenarios such as traffic road and charger failures. The testing time within various numbers of charging stations is about 23ms per EV, which is sufficient to apply the proposed model to real-time decision-making and online recommendation.