Charging System Research Articles

Fuzzy logic method for making push notifications on monitoring system of IoT-based electric truck charging

Fuzzy logic method for making push notifications on monitoring system of IoT-based electric truck charging

Hybrid simulation method for agglomerate evolution in driven granular gases

We present a new hybrid simulation method for protoplanetary dust evolution that is efficient and takes into account the complex fragmentation and agglomeration dynamics. We applied it to simulate the evolution of agglomerate size distributions for turbulent, charged systems. The hybrid method combines kinetic Monte Carlo and discrete element simulations in such a way that the expensive latter is only deployed when two agglomerates collide. This method can easily be extended to include additional driving mechanisms, interactions, and the effects of inhomogeneities. Our simulations reveal an emerging steady state in the size distribution. Due to the efficiency of our method, we are able to extend previous results with improved statistics for the size distribution of large agglomerates; in addition to the previously reported power law, we find a regime with an exponential decay.

Transferable machine learning approach for predicting electronic structures of charged defects

The study of electronic properties of charged defects plays a crucial role in advancing our understanding of how defects influence conductivity, magnetism, and optical behavior in various materials. However, despite its significance, research on large-scale defective systems has been hindered by the high computational cost associated with density functional theory (DFT). In this study, we propose HamGNN-Q, an E(3) equivariant graph neural network framework capable of accurately predicting DFT Hamiltonian matrices for diverse point defects with varying charges, utilizing a unified set of network weights. By incorporating background charge features into the element representation, HamGNN-Q facilitates a direct mapping from structural characteristics and background charges to the electronic Hamiltonian matrix of charged defect systems, obviating the need for DFT calculations. We showcase the model's high precision and transferability by evaluating its performance on GaAs systems encompassing diverse charged defect configurations. Furthermore, we predicted the wave function distribution of polarons induced by defects. We analyzed the node features through principal component analysis, providing physical insights for the interpretability of the HamGNN-Q model. Our approach provides a practical solution for accelerating electronic structure calculations of neutral and charged defects and advancing the design of materials with tailored electronic properties.

The Impact of Environmental Fees to Taxes on Total Factor Productivity of Manufacturing Enterprises: Empirical Evidence from China

Abstract Balancing environmental benefits with economic gains is a crucial challenge for developing countries striving for sustainable development in today’s context. On January 1, 2018, China transitioned from a sewage charge system to an environmental protection tax (EPT). This shift aims to determine whether it can incentivize businesses to enhance quality and efficiency, thereby achieving high-quality development while ensuring that both environmental and economic interests can thrive together. This paper examines the transition from environmental protection fees to tax (TEPFT) as a quasi-natural experiment. Utilizing microdata from A-share listed companies between 2013 and 2022, it focuses on manufacturing enterprises—characterized by high inputs, consumption, and pollution—as the experimental group. In contrast, non-manufacturing enterprises in the cleaning industry serve as the control group. Employing both difference-in-differences (DID) and triple-difference models, the study empirically analyzes the impact of the environmental protection fee tax change on the total factor productivity (TFP) of manufacturing firms. The findings indicate that: (1) the TEPFT significantly enhances the TFP of the manufacturing sector by 0.049 units. (2) this improvement in TFP is primarily driven by increased technological innovation within manufacturing enterprises. Additionally, while effective management practices have a positive moderating effect, financing constraints hinder this progress. (3) the impact of the TEPFT on TFP is particularly pronounced in state-owned enterprises and in provinces where tax rates are elevated.

Review on the Applications of Intelligent Algorithm in Wireless Charging System for Electric Vehicles

This paper presents a comprehensive review of the intelligent algorithms (IAs) based optimization of electric vehicle (EV) wireless charging. First, a general overview of EV wireless charging system is introduced in terms of the compensation circuit, coupling coils, and control strategies, followed by the potential application of IAs. Then, the optimization target, parameter optimization, and structure design of the compensation topologies based on IAs are elaborately expounded by citing current research findings. Subsequently, the IAs are deeply explored for optimizing the systematic parameters, structural geometries, and coupling coils. Then, the IA-based research on the control strategies in the wireless charging system is discussed in detail from three aspects, including power controlling, efficiency optimizing, and parameter tracking. Finally, the prospects for the application of IAs to the EV wireless charging system are summarized and delineated. This paper will be highly beneficial to research entities as a ready reference for the wireless charging system of EVs, with information on IA-based system design.

DEVELOPMENT OF A LITHIUM-ION BATTERY CHARGING SYSTEM UNDER CONSTANT CURRENT AND VOLTAGE CONDITIONS USING STM-32 BASED ON FUZZY LOGIC CONTROL

Lithium-ion batteries are electrical energy storage devices often found in portable electronic equipment. Overcharging and discharging the battery will reduce its life and cause severe damage. Constant current and voltage control methods and control algorithms, such as fuzzy logic control, need to be added to avoid this. This research aims to develop a lithium-ion battery charging system using a constant current and voltage method based on fuzzy logic control. A constant current-constant voltage (cc-cv) charging system helps control the charging voltage and current by conditioning the initial charging to use a constant current so as not to overcharge. Constant current uses a buck converter circuit, while constant voltage uses a voltage regulator circuit. The charging system is equipped with a voltage sensor and a current sensor. System control uses fuzzy logic control methods with input variables as errors and delta errors while the output is a duty cycle. The overall system design was carried out at the Measurement, Reliability, Risk, and Safety Laboratory, ITS for 4 months. The test results show that charging the battery produces a voltage of 12.6 Volts and a current of 2.5 Amperes. The battery will be fully charged, and the charging system will stop when the flowing current decreases and the current is cut off at 100 mA.

Adaptive Neuro Fuzzy Inference System (ANFIS)-Based Control for Solving the Misalignment Problem inVehicle-to-Vehicle Dynamic Wireless Charging Systems

Vehicle-to-vehicle dynamic wireless charging (V2V-DWC) represents a modern advancement in electrified transportation, where a specialized charging vehicle delivers power to another vehicle on the move. The rising popularity of this technology can be attributed to the gradual advancements in energy storage technologies and the scarcity of plug-in charging infrastructure. V2V wireless power transfer provides a solution for electric vehicles (EVs) to recharge their batteries while in transit. The existing literature confirms the empirical validation of this concept through analytical and experimental studies, yet the challenge of misalignment remains insufficiently explored. Achieving optimal power transfer in V2V systems necessitates precise alignment of the inductive coils. Lateral misalignment (LTM) occurs due to the deviation of the coils from the proper alignment, leading to significant energy losses. Additionally, the development of effective controllers to address the V2V misalignment problem remains inadequate. This study proposes the development of a neural network-based adaptive fuzzy logic controller (ANFIS) to alleviate the misalignment issues in V2V-DWC systems. A comparative analysis is conducted between the proposed ANFIS controller and the conventional fuzzy logic controller (FLC) to evaluate their performance across various degrees of LTM. The performance of the proposed ANFIS controller is evaluated through simulations in MATLAB/Simulink, supplemented by experimental testing. The results indicate that the proposed ANFIS controller surpasses the FLC in both simulation and experimental contexts in addressing the V2V misalignment challenge.

A high-speed MPPT based horse herd optimization algorithm with dynamic linear active disturbance rejection control for PV battery charging system

This study first proposes an innovative method for optimizing the maximum power extraction from photovoltaic (PV) systems during dynamic and static environmental conditions (DSEC) by applying the horse herd optimization algorithm (HHOA). The HHOA is a bio-inspired technique that mimics the motion cycles of an entire herd of horses. Next, the linear active disturbance rejection control (LADRC) was applied to monitor the HHOA’s reference voltage output. The LADRC, known for managing uncertainties and disturbances, improves the anti-interference capacity of the maximum power point tracking (MPPT) technique and speeds up the system’s response rate. Then, in comparison to the traditional method (perturb & observe; P&O) and metaheuristic algorithms (conventional particle swarm optimization; CPSO, grasshopper optimization; GHO, and deterministic PSO; DPSO) through DSEC, the simulations results demonstrate that the combination HHOA-LADRC can successfully track the global maximum peak (GMP) with less fluctuations and a quicker convergence time. Finally, the experimental investigation of the proposed HHOA-LADRC was accomplished with the NI PXIE-1071 Hardware-In-Loop (HIL) prototype. The output findings show that the effectiveness of the provided HHOA-LADRC may approach a value higher than 99%, showed a quicker rate of converging and less oscillations in power through the detection mechanism.

Improved Efficiency of Weakly Coupled Wireless High‐Power Transfer Systems by Loss‐Separation Strategy

ABSTRACTIn the equivalent T‐model of the loosely coupled transformers, the small mutual inductance can lead to higher conducting currents, which cause high losses in the primary circuit and significantly reduce the overall transfer efficiency under weak‐coupling states. To overcome this challenge, this paper proposes a strategy to separate the primary loss components from the weak‐coupling stage. In the proposed strategy, a gyrator in the form of a double‐resonance T‐block is added just before the weak‐coupling stage to improve the overall efficiency. Then, the strategy is realized by various topologies such as compensating circuits, added coils, isolated transformers, and integrated‐/split‐ inductors/coils. Also, the optimized designs and component selection for resonance and the mathematical derivation for optimal load resistances were investigated. ANSYS comparative analyses of the topologies are presented by considering practical aspects, including component designs, power flows, transfer efficiency, resonance frequency shifting, and optimal loads. Finally, the analyses were validated using the fabricated experimental setups and demonstrated an efficiency improvement of about 5% for a case with a coupling factor of 0.1. The proposed strategy offers suggestions for industrial designs of high‐power wireless battery charging systems using resonant inductive coils.

An Intelligent Electric Vehicle Charging System in a Smart Grid Using Artificial Intelligence

ABSTRACTThe rapid combination of the electric vehicles into the recent transportation prefers very efficient charging systems involved in grid conditions. This increasing adoption of electric vehicles demands a dynamic and intelligent framework to control charging, confirming optimal grid performance, load balancing, and cost efficiency. In this work, we developed an optimized deep learning framework using the combined structure of Whale‐Optimized Neuro‐Fuzzy Classification for controlling electric vehicle charging within the grid. The increasing involvement of electric vehicle's produces new difficulties, involving overload of grid, and energy management, in real‐time and optimized decision making process. The Whale‐Optimized Neuro‐Fuzzy Classification method uses the hybrid abilities of neuro‐fuzzy systems, integrates the capability of the neural networks and also optimize using a Whale Optimization Algorithm for improved accuracy and efficiency. This proposed method maintains the charging and discharging process of electric vehicles, which have several factors like grid load, priorities of the vehicle and preferences of the user. The neuro‐fuzzy system combines deep convolutional neural network and fuzzy logic to predict charging patterns, considering user preferences, grid demand, renewable energy availability, and so on. This method contribute to the enhancement of energy systems, denotes the future requirement of future smart cities. Evaluation metrics included power usage, energy loss, cost, and processing speed. Analysis of experimental results revealed the accuracy of 99% for the proposed method compared to existing techniques.

Electromagnetic properties of Ni–Zn–Cu ferrite as magnetic sheet in wireless charging system

AbstractTo obtain high‐performance magnetic sheet with high initial permeability and low magnetic loss for wireless charging system, Ni–Zn–Cu ferrite ceramics with a chemical formula of Ni0.6 − xZn0.4CuxFe2O4 (x = 0–0.30) were prepared by traditional solid reaction‐sintering method. As expected, the doped Cu2+ ions could effectively reduce the sintering temperature and increase the density of the ferrites. With increasing doping amount of Cu2+, the coercivity, saturation magnetization, and Curie temperature of the resultant ferrites decreased. With x = 0.15, the sample presented the maximum initial magnetic permeability and highest Q‐factor. On the basis of the measured magnetic parameters, the ferrites were examined as magnetic sheet in wireless charging system by digital simulation, while the structural parameters of the system were considered and the coefficients of hysteresis loss, eddy current loss, and excess loss were quantitatively separated under sinusoidal excitation. Specifically, because of its lower core loss, the optimal ferrite (x = 0.15) would perform better with much improved transmission efficiency than that without the doping of Cu2+, especially when the transmitting and receiving coils were settled more apart, which might be a good candidate for wireless charging system, and the proposed simple wireless charging system can be used as a general strategy for the evaluation of magnetic sheets.

Dephasing enabled fast charging of quantum batteries

We propose and analyze a universal method to obtain fast charging of a quantum battery by a driven charger system using controlled, pure dephasing of the charger. While the battery displays coherent underdamped oscillations of energy for weak charger dephasing, the quantum Zeno freezing of the charger energy at high dephasing suppresses the rate of transfer of energy to the battery. Choosing an optimum dephasing rate between the regimes leads to a fast charging of the battery. We illustrate our results with the charger and battery modeled by either two-level systems or harmonic oscillators. Apart from the fast charging, the dephasing also renders the charging performance more robust to detuning between the charger, drive, and battery frequencies for the two-level systems case.

Revenue-Maximizing Shared Parking and Electric Vehicle Charging Management in Multi-Unit Dwellings

In urban areas, searching for parking and electric vehicle (EV) charging can result in cruising, congestion, and environmental externalities. Recognizing the business opportunity of offering private parking and charging infrastructure access within multi-unit dwellings (MUDs) during daytime, we model a shared parking and EV charging management system. We maximize the revenue of MUD charging hubs in mixed land use, catering to public demand. Our approach accounts for the objectives of the two stakeholders involved: a demand model is fitted on the choices of EV charging users, and the supply model optimizes the allocation of parking and charging requests in an MUD parking lot. A binary integer linear programming model for the allocation of parking and charging spaces with a rolling horizon is integrated with matching rules that handle both parking and charging requests. In our numerical experiments in a neighborhood of Chicago, Illinois, we estimate the performance of the MUD parking and charging system with metrics that include revenue, number of matchings, and utilization rates. At any given time, MUDs with lower prices attract more charging requests, particularly those of longer duration, resulting in higher revenue and greater charging utilization. Dynamic pricing facilitates a more equitable distribution of requests; as MUD parking lots reach capacity and their fees increase, other MUDs become more competitive, attracting additional requests. Comparing our method against first-come-first-served and optimal-solution benchmarks, we demonstrate our model’s effectiveness in dynamically managing mixed parking and charging demand in MUD charging hubs.

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

Cell Architecture Design for Fast-Charging Lithium-Ion Batteries in Electric Vehicles

This paper reviews the growing demand for and importance of fast and ultra-fast charging in lithium-ion batteries (LIBs) for electric vehicles (EVs). Fast charging is critical to improving EV performance and is crucial in reducing range concerns to make EVs more attractive to consumers. We focused on the design aspects of fast- and ultra-fast-charging LIBs at different levels, from internal cell architecture, through cell design, to complete system integration within the vehicle chassis. This paper explores battery internal cell architecture, including how the design of electrodes, electrolytes, and other factors may impact battery performance. Then, we provide a detailed review of different cell format characteristics in cylindrical, prismatic, pouch, and blade shapes. Recent trends, technological advancements in tab design and placement, and shape factors are discussed with a focus on reducing ion transport resistance and enhancing energy density. In addition to cell-level modifications, pack and chassis design must be implemented across aspects such as safety, mechanical integrity, and thermal management. Considering the requirements and challenges of high-power charging systems, we examined how modules, packs, and the vehicle chassis should be adapted to provide fast and ultra-fast charging. In this way, we explored the potential of fast and ultra-fast charging by investigating the required modification of individual cells up to their integration into the EV system through pack and chassis design.

An Enhanced Solar Battery Charger Using a DC-DC Single-Ended Primary-Inductor Converter and Fuzzy Logic-Based Control for Off-Grid Photovoltaic Applications

Battery charging systems are crucial for energy storage in off-grid photovoltaic (PV) installations. Since the power generated by a PV panel is conditioned by climatic conditions and load characteristics, a maximum power point tracking (MPPT) technique is required to maximize PV power and accelerate battery charging. On the other hand, a battery must be carefully charged, ensuring that its charging current and voltage limits are not exceeded, thereby preventing premature degradation. However, the voltage generated by the PV panel during MPPT operation fluctuates, which can harm the battery, particularly during periods of intense radiation when overvoltages are likely to occur. To address these issues, the design and construction of an enhanced solar battery charger utilizing a single-ended primary-inductor converter (SEPIC) and soft computing (SC)-based control is presented. A control strategy is employed that integrates voltage stabilization and MPPT functions through two dedicated fuzzy logic controllers (FLCs), which manage battery charging using a three-mode scheme: MPPT, Absorption, and Float. This approach optimizes available PV power while guaranteeing fast and safe battery charging. The developed charger leverages the SEPIC’s notable features for PV applications, including a wide input voltage range, minimal input current ripple, and an easy-to-drive switch. Moreover, unlike most PV charger control strategies in the literature that combine improved traditional MPPT methods with classical proportional integral (PI)-based control loops, the proposed control adopts a fully SC-based strategy, effectively addressing common drawbacks of conventional methods, such as slowness and inaccuracy during sudden atmospheric fluctuations. Simulations in MATLAB/Simulink compared the FLCs’ performance with conventional methods (P&O, IncCond, and PID). Additionally, a low-power hardware prototype using an Arduino Due microcontroller was built to evaluate the battery charger’s behavior under real weather conditions. The simulated and experimental results both demonstrate the robustness and effectiveness of the solar charger.

Development of a novel compact and fast SiPM-based RICH detector for the future ALICE 3 PID system at LHC

A dedicated R&D is ongoing for the charged particle identification system of the ALICE 3 experiment proposed for the LHC Run 5 and beyond.One of the subsystems for the high-energy charged particle identification will be a Ring-Imaging Cherenkov (RICH) detector.The possibility of integrating Cherenkov-based charged particle timing measurements is currently under study.The proposed system is based on a proximity-focusing RICH configuration including an aerogel radiator separated from a SiPM array layer by an expansion gap. A thin high-refractive index window of transparent material, acting as a second Cherenkov radiator, is glued on the SiPM array to enable time-of-flight measurements of charged particles by exploiting the yield of Cherenkov photons in the thin window.We assembled a small-scale prototype instrumented with different Hamamatsu SiPM array sensors with pitches ranging from 1 to 3 mm, readout by custom boards equipped with the front-end Petiroc 2A ASICs to measure charges and times.The primary Cherenkov radiator consisted of a 2 cm thick aerogel tile, while various window materials, including SiO2 and MgF2, were used as secondary Cherenkov radiators.The prototype was successfully tested in a campaign at the CERN PS T10 beam line with pions and protons. This paper summarizes the results achieved in the 2023 test beam campaign.

Analysis and Design of Dual-Mode Wireless Battery Charging System Based on Trajectory Optimization and Matching Network Integration

Analysis and Design of Dual-Mode Wireless Battery Charging System Based on Trajectory Optimization and Matching Network Integration

High-Efficiency Wireless Integrated On-Board Charger System Using Partial Power Conversion

High-Efficiency Wireless Integrated On-Board Charger System Using Partial Power Conversion

A Dual-Frequency Hybrid Wireless Charging System With Anti-Offset Integrated Receiver for Mobile Desktop Charging

A Dual-Frequency Hybrid Wireless Charging System With Anti-Offset Integrated Receiver for Mobile Desktop Charging