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This study aims to solve the problem of the existing metal foreign object (MFO) detecting systems, which are not sensitive to the small size MFO in wireless charging region of electric vehicle (EV) because of the extremely complex signal noise in the process of wireless charging of EV.

A method for MFO detection based on the principle that MFOs can cause mistuned resonance of detection coil resonant circuit is proposed. The general scheme of detecting system is proposed. The design method for detection coils which is effective to small MFOs detection in large-area region of wireless charging of EV is presented. The design of time-sharing driving circuit and amplifying circuit of high frequency exciting signal for detection coils is introduced. The design scheme of signal processing circuit (including filter and rectifier) of detection coil terminal voltage is also proposed.

The influence of exciting frequency of detection coils on detecting sensitivity and the anti-noise feature of system are analyzed according to the experiment results.

The experiment of MFO detection indicates that the proposed method can effectively detect the coin-sized small MFO in the process of wireless charging of EV.

To simplify the circuit design and control complexity of the magnetic coupling resonant wireless charging system, the radio energy transmission constant current and constant voltage charging is realized.

The purpose of this study is to simplify the circuit design and control complexity of the magnetic coupling resonance wireless charging system, in order to achieve constant current and constant voltage charging for wireless energy transmission. First, the principle of LCC/S-S compensation structure is analyzed, and the equivalent mathematical model is established; then, the system characteristics under constant current and constant voltage mode are analyzed, and the design method of system parameters is given; finally, a simulation and experimental system is built to verify the correctness and feasibility of the theoretical analysis.

The results show that the proposed hybrid topology can achieve a constant current output of 2 A and a constant voltage output of 30 V under variable load conditions, and effectively suppress the current distortion problem under light load conditions. The waveform distortion rate of the inverter current is reduced from 33.97% to 10.45%.

By changing the high-order impedance characteristics of the compensation structure, the distortion of the current waveform under light load is suppressed, and the overall stability and efficiency of the system are improved.

This paper aims to present a fast and modular framework implementation for the thermal analyses of foreign metal objects in the context of wireless power transfer (WPT) to evaluate whether they pose a hazard to the system. This framework serves as a decision-making tool for determining the necessity of foreign object detection in certain applications and at certain transmitted power levels.

To assess the necessity of implementing foreign object detection, the considered WPT system is modeled, and Arnoldi-Krylov-based model order reduction is applied to generate separate reduced models of the ground and vehicle modules of the WPT system. This enables interoperable evaluations to be conducted. Further discussion on the implementation details of the system-level simulations used to evaluate the electrical and thermal characteristics is provided. The resulting modular implementation allows for efficient evaluation of the thermal behavior of the wireless charging system at various transferred power levels and under various boundary conditions.

Based on the transferred power level, the WPT model, the relative positioning between the vehicle and the charging pad and the charging time, it may be necessary to divide the area of the charging pad into multiple regions for the purpose of implementing foreign object detection.

While the tools and fundamentals of thermal analysis are widely known and used, their application to high-power WPT systems for electric vehicles has not yet been thoroughly discussed in this form in the literature. The approach presented in this paper is not limited to the specific WPT model discussed but rather is directly applicable to other WPT models as well.

In this work, a microstrip antenna array for wireless power transfer (WPT) application is reported. The proposed 4 × 4 antenna array operating at 16 GHz is designed using a flexible Kapton polyimide substrate for a far-field charging unit (FFCU).

The proposed antenna is designed using the transmission line model on a flexible Kapton polyimide substrate. The finite element method (FEM) is used to perform the full-wave electromagnetic analysis of the proposed design.

The antenna offers −10 dB bandwidth of 240 MHz with beam width and broadside gain found to be 29.4° and 16.38 dB, respectively. Also, a very low cross-polarization level of −34.23 dB is achieved with a radiation efficiency of 36.67%. The array is capable of scanning −15° to +15° in both the elevation and azimuth planes.

The radiation characteristics achieved suggest that the flexible substrate antenna is suitable for wireless charging purposes.

Magneto-quasi-static fields emanated by inductive charging systems can be potentially harmful to the human body. Recent projects, such as TALAKO and MILAS, use the technique of wireless power transfer (WPT) to charge batteries of electrically powered vehicles. To ensure the safety of passengers, the exposing magnetic flux density needs to be measured in situ and compared to reference limit values. However, in the design phase of these systems, numerical simulations of the emanated magnetic flux density are inevitable. This study aims to present a tool along with a workflow, based on the Scaled-Frequency Finite Difference Time-Domain and Co-Simulation Scalar Potential Finite Difference schemes, to determine body-internal magnetic flux densities, electric field strengths and induced voltages into cardiac pacemakers. The simulations should be time efficient, with lower computational costs and minimal human workload.

The numerical assessment of the human exposure to magneto-quasi-static fields is computationally expensive, especially when considering high-resolution discretization models of vehicles and WPT systems. Incorporating human body models into the simulation further enhances the number of mesh cells by multiple millions. Hence, the number of simulations including all components and human models needs to be limited while efficient numerical schemes need to be applied.

This work presents and compares four exposure scenarios using the presented numerical methods. By efficiently combining numerical methods, the simulation time can be reduced by a factor of 3.5 and the required storage space by almost a factor of 4.

This work presents and discusses an efficient way to determine the exposure of human beings in the vicinity of wireless power transfer systems that saves computer simulation resources and human workload.

High resolution simulations of body-internal electric field strengths induced by magneto-quasistatic fields from wireless power transfer systems are computationally expensive. The exposure simulation can be split into two separate simulation steps allowing the calculation of the magnetic flux density distribution, which serves as input into the second simulation step to calculate the body-internal electric fields. In this work, the magnetic flux density is interpolated from in situ measurements in combination with the scalar-potential finite difference scheme to calculate the resulting body-internal field. These calculations are supposed to take less than 5 s to achieve a near real-time visualization of these fields on mobile devices. The purpose of this work is to present an implementation of the simulation on graphics processing units (GPUs), allowing for the calculation of the body-internal field strength in about 3 s.

This work uses the co-simulation scalar-potential finite difference scheme to determine the body-internal electric field strength of human models with a voxel resolution of 2 × 2 × 2 mm³. The scheme is implemented on GPUs. This simulation scheme requires the magnetic flux density distribution as input, determined from radial basis functions.

Using NVIDIA A100 GPUs, the body-internal electric field strength with high-resolution models and 8.9 million degrees of freedom can be determined in about 2.3 s.

This paper describes in detail the used scheme and its implementation to make use of the computational performance of modern GPUs.

Recently, reverse logistics (RL) has become more prominent due to growing environmental concerns, social responsibility, competitive advantages and high efficiency by customers because of expansion of product selection and shorter product life cycle. However, effective implementation of RL results in some direct advantages, the most important of which is winning customer satisfaction that is vital to a firm's success. Therefore, paying attention to customer feedback in supply chain (SC) and logistics processes has recently increased, so manufacturers have decided to transform their RL into customer-centric RL. Hence, this paper aims to identify the features of a mobile phone which affect consumers’ purchasing behavior and to analyze the causality and prominence relations among them that can help decision-makers, policy planners and managers of organizations to develop a framework for customer-centric RL. These features are studied based on analysis of product review sites. This paper's special focus is on social media (SM) data (Twitter) in an attempt to help the decision-making process in RL through a big data analysis approach.

This paper deals with identifying mobile phone features that affect consumer's mobile phone purchasing decisions. Using the DEMATEL approach and using experts' insights, a cause and effect relationship diagram was generated through which the effect of features was analyzed.

Eighteen features were categorized in terms of cause and effect, and the interrelationships of features were also analyzed. The threshold value is calculated as 0.023, and the values lower than that were eliminated to obtain the digraph. F6 (camera), F13 (price) and F5 (chip) are the most prominent features based on their prominent score. It was also found that the F5 (chip) has the highest driving power (1.228) and acts as a causal feature to influence other features.

The focus of this article is on SM data (Twitter), so that experts can understand the interaction between mobile phone features that affect consumer's decision on mobile phone purchasing by using the results. This study investigates the degree of influence of features on each other and categorizes the features into cause and effect groups. This study is also intended to help organizational decision-makers move toward a reverse customer SC.

The purpose of this paper is to review the advancements in new multi-technology sensor products being developed or already serving the market and to explore such applications. The paper also addresses some hacking problems which may arise.

This paper is a review of published information and papers on multi-technology sensor research as well as contact and discussions with multi-technology sensor researchers and suppliers in this field.

Microelectronics and electrochemical technologies have been major factors in the multi-sensor technology advancements of sensors for a wide range of applications. Sensors are becoming much smarter; solving application problems better than has been previously possible with single-technology sensors. Multi-technology sensors in many cases may offer better resolution and are much more sensitive than single technology sensors in the past.

Readers may be very excited to learn of the many advances in multi-technology sensors which are coming to the sensor field. Applications that were previously served with more than one sensor or were not possible before are now being served by multi-technology sensors. One such application which many readers may not be aware of but may be using is the wearable individual exercise sensor. One such device is the Apple Watch which will be reviewed in some detail later in this paper.

No previous review of multi-technology sensing has been observed.

This paper aims to provide a comprehensive analysis of the state of the art in energy efficiency for autonomous mobile robots (AMRs), focusing on energy sources, consumption models, energy-efficient locomotion, hardware energy consumption, optimization in path planning and scheduling methods, and to suggest future research directions.

The systematic literature review (SLR) identified 244 papers for analysis. Research articles published from 2010 onwards were searched in databases including Google Scholar, ScienceDirect and Scopus using keywords and search criteria related to energy and power management in various robotic systems.

The review highlights the following key findings: batteries are the primary energy source for AMRs, with advances in battery management systems enhancing efficiency; hybrid models offer superior accuracy and robustness; locomotion contributes over 50% of a mobile robot’s total energy consumption, emphasizing the need for optimized control methods; factors such as the center of mass impact AMR energy consumption; path planning algorithms and scheduling methods are essential for energy optimization, with algorithm choice depending on specific requirements and constraints.

The review concentrates on wheeled robots, excluding walking ones. Future work should improve consumption models, explore optimization methods, examine artificial intelligence/machine learning roles and assess energy efficiency trade-offs.

This paper provides a comprehensive analysis of energy efficiency in AMRs, highlighting the key findings from the SLR and suggests future research directions for further advancements in this field.