Bidirectional Operation Research Articles

Research on the High-Efficiency Wireless Bidirectional Charging and Discharging System for Electric Vehicles Based on the LCC-S Compensation Network

In response to the existing issues of poor anti-offset performance and insufficient width of the matchable voltage range at the DC end in the current bidirectional wireless power transfer (BWPT) system for electric vehicles, an efficient wireless bidirectional charging and discharging system for electric vehicles based on the LCC-S compensation network is proposed. Compared with the topological structure of the traditional two-stage wireless bidirectional charging and discharging system, this system adds a DC-DC module in the on-board equipment, while the BWPT link adopts the LCC-S compensation topology. Moreover, this system realizes DC side voltage matching and transmission power regulation through the DC converter on the vehicle side. Under the premise of no control-level data communication between the primary and secondary sides, it achieves efficient, stable and reliable wireless bidirectional charging and discharging control. Finally, the working characteristics of the proposed system are tested through experiments. The results indicate that the system can maintain efficient, stable and reliable bidirectional operation under the conditions of a large offset range and wide voltage range at the DC side

Wireless microwave-to-optical conversion via programmable metasurface without DC supply

Microwave-optical interaction and its effective utilization are vital technologies at the frontier of classical and quantum sciences for communication, sensing, and imaging. Typically, state-of-the-art microwave-to-optical converters are realized by fiber and circuit approaches with multiple processing steps, and external powers are necessary, which leads to many limitations. Here, we propose a programmable metasurface that can achieve direct and high-speed free-space microwave-to-laser conversion. Moreover, it supports reverse conversion, achieving bidirectional operations. The programmable metasurface converter is realized by integrating subwavelength microwave resonant structures, MS junction and photoelectric PN junction components together, without connecting any direct-current supplies to provide driving bias. We further demonstrate the enormous potentials of the metasurface converter in cross-media links and develop a full-duplex air-water wireless communication system. Experimental results show that the bidirectional real-time data transmissions and exchanges are established through the air-water boundary. This work represents a decisive step towards microwave-optical interconversion on wireless and battery-free interfaces.

Research on flow characteristics of discharge pipe during reverse operation of bidirectional axial flow pump

Bidirectional axial flow pumps are of growing importance in flood control and irrigation. Reverse operation is a common concern during the operation of bidirectional pumps. Therefore, this paper focuses on studying the flow state in the discharge pipe of a bidirectional pump operating in reverse at various flow rates, utilizing model testing and numerical simulation methods. Research shows that the spiral flow in the discharge pipe leads to the high head measurements. Moreover, reverse operation generates vortices in the discharge pipe, with greater vortex intensity and range occurring at lower flow rates, causing poor velocity distribution uniformity. The concentration of vortex kinetic energy and energy loss in the discharge pipe is primarily within a range of twice the impeller diameter. Furthermore, as the flow rate decreases, the pressure pulsation in the discharge pipe becomes unstable. At design and large flow rates, the pressure pulsations are mainly due to impeller rotation; however, running at a small flow rate results in low-frequency fluctuations in the discharge pipe, occurring at a cycle time 3.5 times the rotation frequency. This research holds both theoretical and practical significance for enhancing the operational stability and efficiency of bidirectional axial flow pumps.

Study on Soil Vibration Propagation Characteristics Caused by Subway near Foundation Pit

Abstract Based on the Shanghai Metro Line 12, a three-dimensional finite element calculation model is established to study the influence of adjacent foundation pits on subway train vibration propagation. A comparative analysis is conducted on the vibration response and propagation characteristics of the subway during single and bidirectional metro operation under free field and adjacent foundation pit conditions. The effects of subway train speed and tunnel depth on vibration propagation are discussed in detail. It is indicated that as the train speed increases, the ground vibration increases, and the increasing trend of vertical acceleration gradually slows down. The deeper the tunnel is buried, the smaller the ground vibration response. The variation of tunnel burial depth has no effect on the attenuation law of vertical vibration.

Kerr-lens mode-locked Yb:YAG ring-cavity thin-disk oscillator switchable between unidirectional and bidirectional operations

Femtosecond ring cavity thin disk oscillators combine high power with the flexibility switching output either unidirectionally and bidirectionally. Here, we report a Kerr lens mode-locked Yb:YAG ring cavity thin disk oscillator switchable between unidirectional and bidirectional operations by adjusting the position and parameters of the device in the cavity. The average power and pulse width of these are closing to 20 W and below 400 fs, respectively. Additionally, we present a numerical simulation to provide guidance for finding mode-locked regions and measure the repetition rates of bidirectional pulses, exploring the mechanism of the difference in repetition rates in the ring cavity. These results lay the foundation for generating a high-power near-infrared double optical frequency comb, as well as for the development of a mid-infrared optical frequency comb.

Trajectory Control Approach for Single-Stage Soft-Switching Grid-Tied Inverters

This paper presents a trajectory control model using finite state machines for a single-stage soft-switching grid-tied inverter designed with a fast dynamic response. The targeted application is a module-integrated inverter for a single photovoltaic (PV) panel which interfaces distributed energy sources with the grid. To minimize switching lossd provide advanced grid-connected functionality, the soft-switching operation is achieved through a resonant filter using a trajectory control scheme. In recent years, controllers based on digital signal processing platforms have been able to handle complex and high-speed control algorithms with precision for real-time control. In real-time control applications, the finite state machine (FSM) approach enhances responsiveness by minimizing latency with limited memory resources by executing rapid state transitions. The proposed model effectively manages the switching states of the single-stage soft-switching inverters during complex DC/AC bidirectional operations. By directly controlling the energy within the series resonant circuit, the model delivers a fast transient response while minimizing switching actions across all quadrants of operation. The control scheme has been digitally implemented on a Texas Instruments (TI) digital signal processor and validated through Hardware-In-the-Loop (HIL) testing.

Vector Reconfiguration on a Bidirectional Multilevel LCL-T Resonant Converter

With the development of distributed energy technology, the establishment of the energy internet has become a general trend, and relevant research about the core component, energy router, has also become a hotspot. Therefore, the bidirectional isolated DC–DC converter (BIDC) is widely used in AC–DC–AC energy router systems, because it can flexibly support the DC bus voltage ratio and achieve bidirectional power flow. This paper proposes a novel vector reconfiguration on a bidirectional multilevel LCL-T resonant converter in which an NPC (neutral-point clamped) multilevel structure with a flying capacitor is introduced to form a novel active bridge, and a coupling transformer is specially added into the active bridge to achieve multilevel voltage output under hybrid modulation. In addition, an LCL-T two-port vector analysis is adopted to elaborate bidirectional power flow which can generate some reactive power to realize zero-voltage switching (ZVS) on active bridges to improve the efficiency of the converter. Meanwhile, due to the symmetry of the LCL-T structure, the difficulty of the bidirectional operation analysis of the power flow is reduced. Finally, a simulation study is designed with a rated voltage of 200 V on front and rear input sources which has a rated power of 450 W with an operational efficiency of 93.8%. Then, the feasibility of the proposed converter is verified.

Investigation of the TaOx unipolar switching memory on high efficiency computing

Memristor has shown great potential application for data storage, neuromorphic computing chip, and memory logic, but its bi-directional operation from setting in positive bias voltage region to resetting in a negative bias voltage region would introduce a complex control circuit. Herein, a memristor with the structure of Au/TaOx/F-doped SnO2 presents unipolar switching behavior, enabling the memristor to operate the set and reset processing in a single voltage direction. By comparison with bipolar switching memristor, using this unipolar switching memristor to operate the logic expression of the letter “D” based on American standard code for information interchange (ASCII) can reduce power consumption by 55.56 % while switching the logic state from “0” to “1” and then back to “0” can save up to 48 % of power consumption. This work provides a feasible method for the low power consumption computing as well as a road to simplify circuit design.

Analysis of the influence of different support types on the operation performance of bidirectional shaft tubular pump

Abstract Because of the particularity of the pump’s bidirectional operation, and the difference of the flow channels on both sides of the pump. The device has a mature impeller and guide vane combination when it is running forward. And when the device runs in reverse, the support on the shaft side acts as the rear guide vane of the impeller, which has great influence on the performance of the device. However, due to the few operating conditions in reverse operation, the importance of support is often ignored. In this paper, the influence of different support types on the bidirectional flow pattern and performance of the device is analyzed by numerical simulation. The results indicate that by optimizing the variation trend of the edge arc radius of the supporting baffle section, it is possible to effectively control the section with poor flow distribution, thereby reducing hydraulic losses. The support adopts three partitions arranged at an interval of 120°, which has little difference in performance during forward operation compared with other schemes. But during reverse operation, it reduces the impact and friction losses of water flow. And it fully utilizes the direct guide vane function of recovering vorticity, resulting in a significant increase in efficiency. At the same time, the accuracy of numerical simulation results is verified by model tests. The stability of positive and negative operating pressure pulsation supporting the inlet and outlet under the optimal scheme is analyzed.

Hydraulic dual-module hybrid driving system with adjustable waste energy recovery for industrial vehicles

The energy dissipation of industrial vehicle during hydraulic cylinder operations is significant, and the limitations of energy recovery and reuse further exacerbate the issue of energy waste and low energy efficiency. To address these challenges, we propose a hydraulic dual module hybrid driving system (DHDS) for cylinder. This system incorporates a Main drive module with a Power-assisting module, enabling the recovery and reuse of gravitational energy to assist in cargo lifting through the bidirectional operation of the Power-assisting module. Based on the DHDS, we design a multi-mode control strategy (MMCS) with adjustable power distribution capability, allowing for the optimization of recovered energy utilization under various conditions. Furthermore, experiments and simulations are conducted to evaluate the ability of the MMCS to redistribute recovered energy and assess the energy efficiency of the DHDS. Using a DHDS-equipped forklift as an example, simulation platform and test bench are constructed to demonstrate its performance in several designed working cycles. Compared to a 1.2-t electric forklift, the DHDS-equipped forklift with MMCS achieves a battery saving efficiency of 12.70%–40.53 % and a gravitational energy recovery efficiency of 24.80%–62.85 %.

Prediction of protein secondary structure by the improved TCN-BiLSTM-MHA model with knowledge distillation

Secondary structure prediction is a key step in understanding protein function and biological properties and is highly important in the fields of new drug development, disease treatment, bioengineering, etc. Accurately predicting the secondary structure of proteins helps to reveal how proteins are folded and how they function in cells. The application of deep learning models in protein structure prediction is particularly important because of their ability to process complex sequence information and extract meaningful patterns and features, thus significantly improving the accuracy and efficiency of prediction. In this study, a combined model integrating an improved temporal convolutional network (TCN), bidirectional long short-term memory (BiLSTM), and a multi-head attention (MHA) mechanism is proposed to enhance the accuracy of protein prediction in both eight-state and three-state structures. One-hot encoding features and word vector representations of physicochemical properties are incorporated. A significant emphasis is placed on knowledge distillation techniques utilizing the ProtT5 pretrained model, leading to performance improvements. The improved TCN, achieved through multiscale fusion and bidirectional operations, allows for better extraction of amino acid sequence features than traditional TCN models. The model demonstrated excellent prediction performance on multiple datasets. For the TS115, CB513 and PDB (2018–2020) datasets, the prediction accuracy of the eight-state structure of the six datasets in this paper reached 88.2%, 84.9%, and 95.3%, respectively, and the prediction accuracy of the three-state structure reached 91.3%, 90.3%, and 96.8%, respectively. This study not only improves the accuracy of protein secondary structure prediction but also provides an important tool for understanding protein structure and function, which is particularly applicable to resource-constrained contexts and provides a valuable tool for understanding protein structure and function.

Operational optimisation of a microgrid using non-stationary hybrid switched model predictive control with virtual storage-based demand management

Demand-shaping mechanisms are a key component of modern energy management systems, although not unproblematic. The degree of social acceptance of interference with demand or generation and the ease of integration of various types of non-critical loads depends on the method of their implementation. In addition, the critical load pool typically includes devices with different response times. The energy management systems currently in use often cannot meet user expectations. Especially when considering other vital aspects, such as energy market spread, storage wear, or connection to the utility grid and neighbouring microgrids. The authors adopted an approach of unifying demand side management and response in the form of virtual energy storage. Said store allows for the accommodation of loads operating under differing scheduling horizons. Such a new concept allows management not only in terms of quantity but also in terms of time. The storage is the focal point of a comprehensive energy management system based on switched model predictive control. The receding horizon algorithm relies on a non-stationary hybrid microgrid model. The study compares the operating costs of microgrids with virtual storage, allowing only demand postponement, preponement or bidirectional operation. The energy management system is also examined for sensitivity to changes in the weight matrices of the cost function, horizon length and forecast inaccuracy. Introducing virtual energy storage reduces microgrid operating costs by up to 16%. The decrease in control performance is proportional to the prediction accuracy, and the sensitivity allows for customisation.

Predicting mechanical responses of additively manufactured metamaterials with computational efficiency

Lattice metamaterials, a class of structures utilized for geometric lightweighting and enhancing structural integrity, feature interconnected elements arranged in specific patterns. These patterns confer unique properties beneficial for improving stiffness, damping, energy absorption, and strength across various applications. However, computational simulation of lattice structures for design and optimization presents significant challenges due to nonlinear and elastic/inelastic effects like instabilities, contacts, rate-dependence, and plasticity. Current methods heavily rely on finite element analysis (FEA), yet they entail high computational complexity and do not fully align with experimental observations. Moreover, additive manufacturing (AM) technologies introduce additional computational complexity due to process-induced anisotropic behaviors. This paper proposes a computational model to construct an effective descriptor by integrating metamaterial topological information with AM conditions. This descriptor serves as a surrogate for FEA models. Additionally, a database is established to correlate the descriptor with experimentally acquired mechanical responses of additively manufactured metamaterials. The bidirectional operation of the envisaged descriptor fulfills two objectives: informing the mechanical response of novel metamaterial models and guiding design and AM processes based on desired outcomes. Model validation demonstrates significant concurrence between predicted and experimental results, evidencing the model's capability to capture inherent nonlinearity in both design and process.

Filtering airborne LiDAR data based on multi-view window and multi-resolution hierarchical cloth simulation

ABSTRACT Ground filtering is a fundamental step in airborne LiDAR data processing toward a variety of applications. However, existing algorithms remain tremendously challenging in complex environments, e.g. steep hillsides, ridges, valleys, discontinuities, and numerous objects. We presented a new ground filtering algorithm that can handle various landscapes. First, the multi-view window is developed to increase the number of ground seeds on the various terrains. Second, multi-resolution hierarchical cloth simulation is used to rapidly construct the high-resolution reference terrain, and bidirectional internal force operation is proposed to improve the accuracy of reference terrain by smoothing the spikes in cloth. Finally, ground and non-ground points are classified based on the height differences between points and the reference terrain. The proposed algorithm was validated not only in the International Society for Photogrammetry and Remote Sensing (ISPRS) but also karst datasets, where particularly complex environments is contained. Results showed that the proposed algorithm outperformed the existing algorithms, with the lowest average total error of 3.85% and the highest average kappa coefficient of 87.75%. Moreover, the proposed algorithm can completely preserve complex terrain, e.g. extremely steep hillsides, and sharp ridges. This study had great potential to provide a useful tool for LiDAR data processing.

Machine-learning-assisted long-term G functions for bidirectional aquifer thermal energy storage system operation

Optimization of aquifer thermal energy storage (ATES) performance in a building system is an important topic for maximizing the seasonal offset between energy demand and supply and minimizing the building's primary energy consumption. To evaluate ATES performance with bidirectional operation, this study develops an analytical solution-based model to simulate the spatiotemporal thermal response in an aquifer. The model consists of three temperature response functions, similar to the G functions in borehole thermal energy storage (BTES), to estimate the transient temperature profile in the aquifer during seasonally varying injection and extraction of hot/cold water. Applying machine learning (ML) based data classification and regression techniques to the results of a series of finite element (FE) benchmark simulations of typical ATES configurations, model input parameters are linked to the subsurface thermal, hydrogeological, and ATES operational properties. Compared to the benchmark simulation results, the errors of the proposed model in estimating the annual energy storage and locating the thermally affected area are about 3 % and 1 %, respectively. The model was applied to a previous short-term case study, and the error in the transient production temperature estimation is about 1 %. The long-term heat recovery ratio estimated from the model also compares well to those calculated from the previous study and the validated numerical model. Because of its fast computation, the proposed model can be coupled with the individual building system simulation and used for preliminary ATES design, and this will allow for greater exploration of ATES operational space and, therefore, better choices of ATES operating conditions. The proposed model can also be coupled with the district heating and cooling network simulation for computationally efficient city-scale long-term ATES potential assessment.

Partial-Power Conversion for Increased Energy Storage Capability of Li-Ion Battery Energy Storage System

Full-power converters are used in battery energy storage systems (BESS) because of their simple structure, high efficiency and relatively low cost. However, cell-to-cell variation, including capacity, state of charge (SOC), and internal resistance, will decrease the available capacity of serially connected battery packs, thereby negatively affecting the energy utilization rate (EUTR) of BESS. This article proposes a novel BESS scheme that combines a modular converter with partial power conversion architecture to make a modular partial power converter (MPPC) that addresses the issue. The MPPC consists of input-serial and output-paralleled (ISOP) isolated phase-shift full-bridge (PSFB) sub-modules. It processes only the serial compensation power, about 1/3 of the full power. Consequently, the MPPC shrinks the converter capacity, which can reduce the cost and power loss. Furthermore, this article develops a BESS model considering cell-to-cell variations to analyze the energy storage capability of the MPPC-BESS compared with the existing full-power BESS. To test the model, we run a simulation using parameter values from 100 real retired batteries. Our simulation results show that the MPPC can significantly alleviate the reduction of EUTR as the voltage level increase. Finally, we construct a 36V/720W MPPC-BESS prototype with two battery packs and PSFB sub-modules to verify the bidirectional operating stability and energy storage capability.

A versatile plasma generation power supply featuring a multilevel converter for arbitrary waveforms generation

PurposePlasma technology has become of great interest in a wide variety of industrial and domestic applications. Moreover, the application of plasma in the domestic field has increased in recent years due to its applications to surface treatment and disinfection. In this context, there is a significant need for versatile power generators able to generate a wide range of output voltage/current ranging from direct current (DC) to tens of kHz in the range of kVs. The purpose of this paper is to develop a highly versatile power converter for plasma generation based on a multilevel topology.Design/methodology/approachThis paper proposes a versatile multilevel topology able to generate versatile output waveforms. The followed methodology includes simulation of the proposed architecture, design of the power electronics, control and magnetic elements and test laboratory tests after building an eight-level prototype.FindingsThe proposed converter has been designed and tested using an experimental prototype. The designed generator is able to operate at 10 kVpp output voltage and 10 kHz, proving the feasibility of the proposed approach.Originality/valueThe proposed converter enables versatile waveform generation, enabling advanced studies in plasma generation. Unlike previous proposals, the proposed converter features bidirectional operation, allowing to test complex reactive loads. Besides, complex waveforms can be generated, allowing testing complex patterns for optimized cold-plasma generation methods. Besides, unlike transformer- or resonant-network-based approaches, the proposed generator features very low output impedance regardless the operating point, exhibiting improved and reliable performance for different operating conditions.

An efficient minimum cost battery charging and discharging scheduling scheme for electric vehicle using hybrid energy valley-pathfinder algorithm

ABSTRACT Emission pollution is one of the major issues in many growing cities. To overcome the problem and to prevent pollution, “Electric Vehicle (EV)” is one of the best solutions. Moreover, it has both economic and environmental benefits. To increase network reliability and quality, distribution companies contribute effective assistance. The major challenge met by this network is choosing an applicable connecting point for the bi-directional operation in a distribution network. Other issues the network faces are the performance and quality of the distribution network, cost reduction, and geographic location. The system’s stability is affected by connecting to the grid for charging EVs. Therefore, the optimal charging process is needed for the EVs. This paper proposes developing an optimal charging system without damaging the stability of the power system. This optimization is carried out by combining two algorithms and developing a new algorithm called “Final Position of Energy Valley and Pathfinder Algorithm (FP-EVPA).” This algorithm minimizes overall system operating costs. Also, it helps to reduce pollution emissions, grid losses, environmental pollution, nodal offset voltage, peak valley difference of load, charging, and operating costs. The optimized method maintains the quality of the power grid and system stability. It is also helpful for the users for minimal charging costs. The empirical findings of the model are better than 0.19%, 0.2%, 0.18%, and 0.07% of GEO, EOO, EVO, and PFA algorithms in terms of median. The developed FP-EVPA was compared with other traditional algorithms with different test functions to examine the effective achievement of the developed method.

Outcome after extracorporeal membrane oxygenation therapy in Norwood patients before the bidirectional Glenn operation.

Patients after the Norwood procedure are prone to postoperative instability. Extracorporeal membrane oxygenation (ECMO) can help to overcome short-term organ failure. This retrospective single-centre study examines ECMO weaning, hospital discharge and long-term survival after ECMO therapy between Norwood and bidirectional Glenn palliation as well as risk factors for mortality. In our institution, over 450 Norwood procedures have been performed. Since the introduction of ECMO therapy, 306 Norwood operations took place between 2007 and 2022, involving ECMO in 59 cases before bidirectional Glenn. In 48.3% of cases, ECMO was initiated intraoperatively post-Norwood. Patient outcomes were tracked and mortality risk factors were analysed using uni- and multivariable testing. ECMO therapy after Norwood (median duration: 5 days; range 0-17 days) saw 31.0% installed under CPR. Weaning was achieved in 46 children (78.0%), with 55.9% discharged home after a median of 45 (36-66) days. Late death occurred in 3 patients after 27, 234 and 1541 days. Currently, 30 children are in a median 4.8 year (3.4-7.7) follow-up. At the time of inquiry, 1 patient awaits bidirectional Glenn, 6 are at stage II palliation, Fontan was completed in 22 and 1 was lost to follow-up post-Norwood. Risk factor analysis revealed dialysis (P < 0.001), cerebral lesions (P = 0.026), longer ECMO duration (P = 0.002), cardiac indication and lower body weight (P = 0.038) as mortality-increasing factors. The 10-year mortality probability after ECMO therapy was 48.5% (95% CI 36.5-62.9%). ECMO therapy in critically ill patients after the Norwood operation may significantly improve survival of a patient cohort otherwise forfeited and give the opportunity for successful future-stage operations.

Bidirectional quantum operation teleportation with two four-qubit cluster states

Bidirectional quantum operation teleportation with two four-qubit cluster states