The integration of photovoltaic (PV) systems, electric vehicles (EVs), and charging stations (CSs) faces critical challenges, including PV intermittency, uncertain EV charging demand, and inefficient energy management. Existing strategies often overlook the precision of PV generation forecasts, the economic risks of electricity trading, and the diverse demands of EVs, leading to suboptimal performance. To address these limitations, we propose a two-tier management framework for PV-CS-EV systems, optimizing energy storage charging station (ESCS) operations by balancing profit maximization and risk minimization. The first tier employs accurate PV forecasting and power trading strategies between ESCS, PV farms, and the grid to mitigate economic risks from PV intermittency and market fluctuations. The second tier provides diverse charging strategies to maximize user satisfaction and profit. A key challenge lies in the complex interdependencies between the two tiers, requiring simultaneous optimization of power trading and user-specific charging scheduling under uncertainties. To tackle this, we introduce a hierarchical multi-objective reinforcement learning (MORL) algorithm, which efficiently coordinates decision tasks of both tiers through partial environment information interaction. Experimental results demonstrate the framework’s effectiveness in enhancing the economic performance of PV-CS-EV systems.

In recent years, the global adoption of electric vehicles (EVs) has surged, prompting a corresponding rise in the installation of charging stations. This proliferation has underscored the importance of expediting the deployment of charging infrastructure. Both academia and industry have thus devoted to addressing the charging station location problem (CSLP) to streamline this process. However, prevailing algorithms addressing CSLP are hampered by restrictive assumptions and computational overhead, leading to a dearth of comprehensive evaluations in the spatiotemporal dimensions. Consequently, their practical viability is restricted. Moreover, the placement of charging stations exerts a significant impact on both the road network and the power grid, which necessitates the evaluation of the potential post-deployment impacts on these interconnected networks holistically. In this study, we propose CSLens, a visual analytics system designed to inform charging station deployment decisions through the lens of coupled transportation and power networks. CSLens offers multiple visualizations and interactive features, empowering users to delve into the existing charging station layout, explore alternative deployment solutions, and assess the ensuring impact. To validate the efficacy of CSLens, we conducted two case studies and engaged in interviews with domain experts. Through these efforts, we substantiated the usability and practical utility of CSLens in enhancing the decision-making process surrounding charging station deployment. Our findings underscore CSLens's potential to serve as a valuable asset in navigating the complexities of charging infrastructure planning.

This paper proposes a diagnosis method to detect the open-circuit faults of the switching devices in power converters of electric vehicle charging piles based on time-domain feature extraction and Light Gradient Boosting Machine (LightGBM). We find the resonant capacitor voltage is sensitive to the switch faults that occur in the rear-stage resonant converter. Therefore, we measure this voltage together with the three-phase currents of the input side and the output voltage of the DC side. This original signal with five channels is then divided into segments by a sliding window to increase the sampling number but also avoid the diagnostic lag due to long sequence calculation. A series of time-domain features, e.g. mean value, are extracted for each segment to train the fault classifier based on the high efficient LightGBM. Simulation and experiment show that the proposed method can recognize eleven different types of opencircuit faults in either single or multiple switches within half of the fundamental period, achieving a precision rate of 99.94%.

This paper proposes a fault diagnosis method based on channel attention mechanism for identifying switching device open-circuit failures of both the Vienna rectifier and resonant LLC Converter in electric vehicle charging piles. First, five-channel original data of the resonant capacitor voltage together with the output voltage and input currents are measured. They are subsequently segmented by a sliding window whose length is set as half fundamental cycle of the mains to obtain samples. Within each channel of each sample, the features are extracted so that different weights can be assigned by using the channel self-attention mechanism. Then, the enhanced feature map is input into a Multi-layer Perceptron (MLP) model for fault diagnosis. This method considers not only the signal measurability but also the possibility of multiple faults occurring in different switches. Experimental results validate the high diagnosis accuracy of the proposed method on the two-stage charging converters.

The uncoordinated charging of large-scale electric vehicles (EVs) generally deteriorates the peak-valley difference of daily electric demands. To facilitate the operation of charging stations and electric power distributers, this work proposes a charging load prediction algorithm by combining the Variational Mode Decomposition (VMD) and the Long Short-Term Memory (LSTM) methods. The VMD is adopted to extract the EV charging load features at different time scales, obtaining multiple intrinsic mode functions (IMFs). Then the LSTM establishes the dependencies between these IMFs of historical data and the predicted load. To trade-off between the prediction accuracy and the computation overhead, an additional Snake Optimization (SO) technique is applied to adaptively optimize the VMD parameters. Experimental results show that the proposed algorithm outperforms the traditional LSTM alone and the Gate Recurrent Unit alone neural networks in terms of the overall prediction accuracy. The proposed parallel LSTM structure with the optimized VMD further reduces the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE) significantly by 55.1% and 55.9% with respect to the LSTM method with non-optimized VMD.

We study the joint battery charging and replenishment scheduling of a battery swapping charging system (BSCS) considering random electric vehicle (EV) arrivals, renewable generation, and electricity prices. We formulate the problem as a Markov decision process with an objective to minimize the expected sum of the operation cost (battery charging and replenishment cost) and the waiting cost of EV customers. The joint scheduling problem is challenging due to the stochasticity in EV arrivals, renewable generation, and electricity prices, as well as the curse of dimensionality in the system state and action spaces. To reduce the dimension of the action space, we propose to integrate structural properties into BSCS operation, i.e., the threshold-charging (TC) and least demand first (LDF) structures into the charging policy, and the$(s, S)$structure into the replenishment policy (when the number of fully-charged batteries at a battery swapping station is below$s$, the inventory is replenished to a higher threshold$S$). Numerical experiments on real-world data show that the proposed SAC+TC+$(s, S)$approach saves 7.16%-78.61% and 6.53%-93.73% of total average cost resulting from various structural charging and replenishment policies and the vanilla soft actor-critic (SAC) algorithm under different settings.

To eliminate the common-mode voltage (CMV) for matrix converters, this paper proposes a current sensorless model predictive control with reduced calculation overhead. In contrast to other traditional CMV-reducing methods which use all permissible switching configurations, this method synthesizes the output voltage and the input current with only six rotating vectors that lead to zero CMV. The proposed technique does not need to predict future load currents and source currents for those six rotating vectors, which provides another advantage in term of computation efficiency. Additionally, all current sensors are removed by using a Luenberger state observer instead in the control loop for cost reduction. The effectiveness of the proposed method is evaluated through simulation in different operation conditions.

Model Predictive Control (MPC) is becoming more popular than ever as an alternative to conventional modulations such as Space Vector Modulation methods to control matrix converters (MCs). However, the implementation of MPC is computationally expensive, because control objectives are required to evaluate all admissible switching states of the converter. Additionally, a large number of sensors to measure the 3-phase load currents, source currents, source voltages, and input voltages of MCs increases the overall cost. To sort this out, an efficient MPC is proposed for MCs to enable fast computation and low cost. This approach eliminates the calculations of future load currents and source currents for all possible switching states, requiring only two predictions for the calculation of output voltage and input current references. Further, it removes all current sensors by employing a Luenberger observer. A simulation study has demonstrated that the proposed method can reduce the computation overhead and hardware cost dramatically, leading to high-frequency operation and good converter performance.

This paper presents the design and implementation of multicellular isolated bidirectional dual-active-bridge (DAB) DC/DC converters which are the core equipment of the European CleanSkyII Project ASPIRE. Both the primary and the secondary H-bridge circuits use gallium nitride (GaN) devices which enable high frequency operation. Between two H-bridge circuits is a planner transformer which is customized for the frequency range from 100kHz to 300kHz, saving the volume and weight. Three proportional integral controllers in parallel are also proposed to control the power transfer and compensate the DC offset values to the transformer, providing efficient operation in both buck and boost modes, allowing on-fly turn-on, turn-off and fast power reversal. Experiment results validate that the converters satisfy the requirements of the Project ASPIRE for the use in more electric aircrafts.

In this paper the potential of the modulated model predictive control (MMPC) to control a matrix converter (MC) in the linear- and over-modulation zone is investigated. Input and output current references of MC are usually used to define the control objective in MMPC. By considering the predicted input and output currents of MC, a conventional space current vector modulation equation can be formed. As a result, control of the load and supply currents, good steady state performance and fixed switching frequency are achieved in the linear zone. Moreover, the transition time between the linear- and over-modulation modes is minimized by considering a new reference vector through a simple calculation. The feasibility of proposed method is demonstrated by simulation results and proved that the resulted controller includes the advantages of model predictive control (MPC) and space vector modulation (SVM) and effectively working in the different modes of operation.

Owing to the absence of intermediate energy storage elements, the outputs of matrix converters are influenced easily by abnormal input voltages. To this end, this paper proposes a generalized indirect space vector modulation with feedforward compensation capacity based on the space vector representation of the switch-state transfer matrices. To simplify the calculation of the modulation duty-cycle matrix, both the singular value decomposition and the space vector modulation techniques are used to render clear physical meanings to the modulation process, and to synthesize the desired variables without complex computations. Using this approach, the duty cycles for switch combinations can be calculated online to decouple the low order harmonics in the input side from the outputs. Besides, the geometrical perspective provided by this method makes the optimization on the switching sequence convenient. The simulation results verify the validity of the proposed method in maintaining the outputs sinusoidal and balanced disregarding the abnormal input voltages.