In recent years, with the in-depth reform of the electricity market and the continuous improvement of the penetration rate of distributed resources in the distribution network, the energy trading between intelligent buildings with dual attributes of production and consumption has brought new opportunities and challenges to the nearby consumption of distributed energy. However, for the microgrid system with multi-intelligent buildings, there are defects such as large amount of communication information, low robustness and user privacy in the process of power trading. At the same time, it will also be affected by various uncertain factors such as the access of new energy and the lack of timeliness of transactions. In order to solve the above problems, this paper proposes a rolling P2P energy trading optimization strategy based on distributed information interaction for multi-intelligent buildings in microgrid.
Firstly, considering the aggregation characteristics of various flexible resources in intelligent buildings, the prediction interval results of distributed photovoltaic power generation and the feasible range of flexible resources are characterized in the form of aggregation power interval by Minkowski summation theory, and the aggregation interval model of P2P transaction is established. Among them, the distributed photovoltaic prediction interval is modeled by transforming the benchmark output at different confidence levels into the prediction quantile for the feasible region. At the same time, an interval rolling P2P energy trading framework is constructed. During the energy management period, each building participates in the rolling P2P energy trading by combining the aggregated power interval with its own electricity purchase and sale strategy. Secondly, the risk cost brought by the uncertainty of photovoltaic output to P2P transactions is quantified by CVaR, and an economic dispatch model with the minimum total operating cost of microgrid multi-intelligent buildings is established. On this basis, the P2P transaction power between buildings is used as a consistency variable, and the P2P transaction power and transaction price are obtained based on the distributed solution of the information interaction between adjacent buildings, and the energy transaction period is continuously pushed backward until it meets the requirements of all intelligent buildings in the microgrid.
In the case analysis, the scheduling results of different buildings in the microgrid and the optimization results of different algorithms are compared respectively, which verifies the effectiveness of the interval rolling P2P energy trading model proposed in this paper. At the same time, the practicability and solution efficiency of the distributed information interaction algorithm in this paper have also been reflected. Through the example analysis, the following conclusions can be drawn: (1) Participating in the energy transaction between buildings in the form of aggregation interval fully taps the scheduling potential of flexible resources in buildings and improves the flexibility of coordinated scheduling of multi-intelligent buildings in microgrid. (2) Compared with the ordinary P2P trading, the rolling P2P energy trading improves the enthusiasm of intelligent buildings to participate in energy trading and the self-consumption level of distributed energy while taking into account the economy of system operation. (3) The distributed information interaction strategy proposed in this paper makes the multi-intelligent buildings in the microgrid only need to interact with the expected transaction volume information, and at the same time solve their own optimization problems in parallel, which has a good fit with the rolling P2P transaction mode. It avoids the problems of high computational pressure and privacy leakage, and improves the convergence speed of distributed information interaction. It has good scalability and can effectively solve the optimization iteration problem of large-scale intelligent buildings.

Power battery packs are widely used in new energy electric vehicles and are the core components of electric vehicles. Studying the temperature field modeling of the power battery pack is not only beneficial to understanding its temperature field dynamic characteristics, but is also very important for the structural design and health management of the power battery pack. The temperature field of the power battery pack is described by complex partial differential equations. Since a large number of parameters are unknown and many model parameters show strong time variability, traditional physics-based modeling methods are ineffective in achieving online modeling of the temperature field of the power battery pack. Although methods based on deep learning do not rely on physical models, they require a large amount of experimental data during the training process, the model training time is long, and the real-time performance of temperature field prediction is poor. In response to the above problems, this paper proposes a spatio-temporal modeling of the temperature field of power battery packs based on long short-term memory network.
First, the spatio-temporal separation method is used to extract spatial features and time features under offline conditions. Spatial features are continuously updated with the help of incremental learning, and the long short-term memory (LSTM) network is used to model temporal dynamics. Finally, the updated spatial characteristics and time model are integrated to obtain a prediction model of the power battery pack temperature field.
The proposed method was verified on a power battery pack composed of 24 battery cells. Experimental results show that the proposed method can accurately predict the temperature field of the power battery pack regardless of normal conditions or conditions with air flow interference. Without airflow interference, the single-point temperature prediction error of the proposed method is less than 0.4℃, and the root-mean-square error (RMSE) on the test set is 0.095 1℃. In the presence of airflow interference, the single-point temperature prediction error of the proposed method is less than 0.07℃, and the RMSE on the test set is 0.014 7℃.Under the condition of air flow, the modeling error of the proposed method is smaller. This is because under the condition of air flow interference, the spatial gradient of the temperature change of the power battery pack at the same time is smaller, that is, the temperature change is gentler, making the spatial characteristics of the modeling smoother.
The following conclusions can be drawn from the simulation analysis: (1) the proposed method can accurately predict the temperature field of the power battery pack regardless of normal conditions or conditions with air flow interference. (2) The proposed method can update spatial features in real time through incremental learning, thereby reducing the computational complexity of the method. (3) The proposed method is a purely data-driven method that does not rely on accurate partial differential equations and is therefore suitable for application in temperature field modeling of actual power battery packs.

In fuel cell hybrid systems, the degradation processes of fuel cells and power batteries are highly inconsistent. The excessive consumption and premature end of life of one power source can disrupt the balance of the power system, deplete the performance of the other power source, accelerate the aging of the entire power system, and negatively affect vehicle economy and system durability. Consequently, it becomes challenging to achieve optimal fuel economy and system durability simultaneously. To address this issue, an optimization strategy based on condition prediction and coordinated power source life degradation is proposed.
Firstly, to improve prediction accuracy, operating conditions are categorized into three typical states: low-speed, medium-speed, and high-speed. An upper-level Markov Chain Monte Carlo (MCMC) prediction model is established based on historical conditions to predict the tram's operating conditions. This prediction provides more information for the lower-level energy management strategy to optimize system energy distribution. Secondly, in the lower-level energy management strategy, the hydrogen consumption of the fuel cell and the equivalent hydrogen consumption of the auxiliary power source are analyzed. A continuous degradation model for the fuel cell and power battery is established, introducing optimization objectives and adaptively adjusting the weights of each objective online to optimize the multi-objective function. Finally, the proposed strategy is compared with the traditional equivalent consumption minimization strategy (ECMS) and the external energy maximization strategy (EEMS).
Results show that at the end of the entire operating condition, the proposed strategy's hydrogen consumption is 99.61 g, the degradation rate difference between the dual power sources is 0.000 66%, the system efficiency is 81.66%, the power fluctuation range is -800 W to 800 W, and the stress on the power battery and supercapacitor is 117.5 and 176.4 respectively. Compared to the ECMS strategy, with a hydrogen consumption of 115.1 g and system efficiency of 77.64%, the proposed strategy improves fuel economy and system efficiency by 15.6% and 5.2% respectively. Compared to the EEMS strategy, with a dual power source degradation rate difference of 0.014 4% and system efficiency of 79.77%, the proposed strategy reduces the degradation rate difference by 21.82 times and improves system efficiency by 2.4%. Additionally, the power fluctuation range under the proposed strategy is significantly reduced compared to the -1 000 W to 1 000 W range under both the ECMS and EEMS strategies, resulting in a smoother power source power curve. Under the ECMS strategy, the stress on the power battery and supercapacitor is 156.6 and 215 respectively, while under the EEMS strategy, the stress is 156.8 and 226.6 respectively. The proposed strategy reduces the stress on the auxiliary power source compared to the ECMS and EEMS strategies, decreasing excessive consumption and resulting in a more reasonable power distribution.
Comprehensive simulation analysis reveals the core advantages of the proposed strategy: (1) Establishing an MCMC prediction model for condition prediction improves the adaptability of the energy management strategy to operating conditions, achieving more reasonable, precise, and efficient energy control and reducing damage to the hybrid power system. (2) Overcoming the poor fuel economy of traditional ECMS and the high inconsistency in power source degradation of EEMS. (3) Achieving superior fuel economy and system durability, thereby extending the lifecycle of fuel cell hybrid systems.

Battery energy storage (BES) is widely used in various applications in the power system as a flexible regulation resource. One of the key applications is peak shaving. BES can charge and discharge to capture the price difference between peak and valley periods in the electricity market. However, due to the inherent operational characteristics of BES, peak shaving behavior results in irreversible aging loss, leading to a reduction in lifespan and available capacity. Establishing an accurate aging model for BES is fundamental for simulating its peak shaving behavior and evaluating investment benefits. The aging characteristics of BES are state-dependent at different stages of its lifespan. However, aging models with fixed parameters fail to consider the variation in aging parameters over time during long-term peak shaving simulations.
A state-dependent aging model for BES is proposed, based on the Arrhenius empirical model, which accounts for the impact of lifespan on aging characteristics. This model can represent the full aging behavior of BES throughout its lifespan. A discrete form of the model is developed to meet the application requirements of power system optimization. The discrete variables are adapted to those of the power system, enabling their integration. Furthermore, the discrete aging model is embedded in a peak shaving optimization model. To address the challenge of solving the highly nonlinear discrete aging model, linearization techniques are employed to facilitate model computation. A dynamic parameter updating mechanism for BES is also designed and incorporated into the discrete aging model, enabling dynamic updates of the aging loss rate and available capacity during long-term peak shaving simulations, thus accurately capturing the full aging process throughout the lifespan.
To validate the effectiveness of the proposed method in investment benefits evaluation, historical locational marginal prices from the Southern Power Pool (SPP) electricity market are used as inputs to simulate the daily peak shaving operations of BES. The results of the daily simulations are used to update BES parameters via the dynamic updating mechanism, and the peak shaving benefits are then calculated. By updating boundary conditions, long-term peak shaving simulations of BES throughout its lifespan are performed, and the cumulative net present value (NPV) of investing in BES for peak shaving is computed. The results show that the proposed method allows for considering aging loss variation over time, reflecting the impact of state-dependent aging characteristics on the peak shaving performance of BES. This method enables accurate evaluation of BES investment benefits.
From the analysis, the following conclusions can be drawn: (1) The state-dependent aging model of BES, based on the Arrhenius empirical model and after discretization and linearization, can accurately capture the aging characteristics throughout the entire lifespan of BES. It also facilitates effective application in power system peak shaving simulations. (2) The state-dependent aging characteristics influence the charge and discharge behavior decisions of BES under electricity price signals. Using the state-dependent aging model allows for precise quantification of the aging costs arising from BES’s peak shaving behavior, enabling accurate evaluation of the investment benefits of BES in peak shaving applications.

Rotor-unmanned aerial vehicles (R-UAV) have structural irregularities and problems with docking offset. The traditional unipolar CP magnetic structure increases the wind resistance of the R-UAV due to its mechanical structure characteristics, which, in turn, affects the flight stability of the R-UAV. At the same time, the asymmetric characteristics of the original secondary side can generate high stray magnetic fields, affecting the stable operation of the R-UAV. Therefore, applying the wireless charging system to R-UAVs is challenging. In order to build a stable wireless charging system for R-UAVs, it is necessary to conduct a targeted optimization design based on the mechanical structure of the R-UAV. This paper proposes an “I工I” magnetic structure with high anti-offset and low stray magnetic field constraints for the wireless charging system of R-UAVs and designs a multi-stage constant current-constant voltage (MCC-CV) control strategy with soft switching capability based on the characteristics of series-series (S-S) networks.

Firstly, this paper comprehensively analyzes the characteristics of series-series (S-S) networks, develops an MCC-CV control strategy for wireless charging of lithium batteries, and designs a full-bridge phase-shift soft-switching half-bridge workflow to solve the output current oscillation problem caused by the energy storage characteristics of the resonant network during the multi-stage constant current switching process. By incorporating soft switching capabilities, the system can transition smoothly between different stages of the charging process, minimizing current oscillations and ensuring a stable charging experience. Then, a three-dimensional model of the “I工I” magnetic structure is proposed and established. The transmission structure of this coupling structure is a “I工I” core, while the receiving side structure is an “I” core integrated within the landing gear of the R-UAV. Integrating the magnetic structure within the landing gear allows for zero wind resistance characteristics in wireless charging of R-UAV. Based on the low magnetic resistance characteristics of ferrite cores, the design of the “I” core is completed. Through magnetic circuit analysis and finite element simulations, the magnetic circuit shaping and low stray magnetic field characteristics of the “I” core are determined, and a passive constraint magnetic circuit shaping method for stray magnetic fields is derived. The “I” core distribution is adjusted to increase the anti-offset capability of the “I工I” magnetic structure, thereby achieving a high anti-offset wireless charging system design.

Finally, a prototype of the “I工I” magnetic structure is built. It is shown that the system can maintain a constant output current under radial offset of 100 mm and rotational offset of 360°, with only a 2% fluctuation in transmission efficiency. At the same time, the distribution characteristics of the stray magnetic field outside the launch platform are measured, and a distribution diagram of the stray magnetic field is drawn. The proposed “I工I” is lightweight and efficient.

Wireless power transfer (WPT) technology provides an effective way to solve the problem of stable power supply for rotating equipment. However, in practical applications, the relative misalignment between the rotating side and the stationary side is inevitable. In the practical application of WPT system, due to the presence of ferrite cores, the misalignment of the coupling mechanism will significantly affect the self-inductance and mutual inductance parameters of the coils, resulting in output power fluctuations and efficiency reduction. In order to enhance the anti-misalignment capability of WPT systems under changes in coil parameters, this paper proposes a detuned WPT system anti-misalignment method that considers changes in coil parameters. The detuned WPT system is constructed using changes in coil self-inductance to counteract the output power fluctuations caused by changes in mutual inductance.

Firstly, using the finite element simulation software, the parameter variation laws of the rotary coupling mechanism under axial and radial offsets were summarized. The study found that the self-inductance and mutual inductance of the coupling mechanism have the same trend of change, and the degree of change is similar within a certain offset range. And based on this, the idea of using self-inductance changes to dynamically adjust the degree of system detuning to offset output fluctuations caused by mutual inductance changes was proposed.

Secondly, the influence of parameter changes on system operation was obtained through circuit analysis, and the constant voltage output conditions for the degree of receiver detuning and mutual inductance changes were derived, providing a theoretical basis for the coupling mechanism design and compensation parameters optimization. The coupling mechanism design revolves around the number of turns on the secondary side, and the compensation parameters optimization is based on the particle swarm optimization (PSO) algorithm. With the goal of constant output and efficiency improvement, the compensation topology parameters of the inductor-capacitor-capacitor-series (LCC-S) are comprehensively optimized to achieve good axial and radial anti-misalignment capabilities of the rotary WPT system.

Finally, a 170 W experimental setup was constructed to validate the effectiveness of the proposed method. The experimental results show that within the range of axial offset ±30 mm and radial offset ±5 mm, the maximum mutual inductance change of the rotary coupling mechanism is 74%, the self-inductance change is 48%, and the coupling coefficient is 0.39 to 0.89. The maximum output voltage fluctuation is only 9.5% (axial) and 2.8% (radial), and the maximum efficiency of the system is 93%. This method utilizes the equilibrium characteristic of the parameter changes for the coupling mechanism itself. Its significant advantages lie in simple and effective structure, no DC-DC converter, no communication and closed-loop control, and a more stable and reliable system. It is particularly suitable for WPT system in harsh environments such as high temperature, high voltage, and high-frequency vibration underground, reducing the failure rate of the system and improving power supply reliability.

Wireless power transfer (WPT) technology has garnered widespread attention in recent years due to its advantages in safety, reliability, and flexibility. However, these benefits are often dependent on the precise alignment of the coupling mechanism. In practical applications, as perfect alignment cannot always be ensured, misalignment leads to a reduction in the coupling coefficient, significantly degrading transmission efficiency and system performance. Traditional flat solenoid coils perform well in resisting longitudinal misalignment, but when lateral misalignment occurs, especially near the coil's edge, the coupling coefficient and efficiency drop rapidly. To address this issue, this paper proposes an improved flat solenoid coil WPT system.

First, an equivalent model of the LCC/S compensation circuit is established to analyze the effects of circuit parameters on output characteristics, and a method for configuring the parameters of resonant elements is derived, revealing key circuit parameters affecting voltage gain. Then, an equivalent magnetic circuit model is built to analyze the magnetic field distribution characteristics of the coil, demonstrating that core shape and winding configuration significantly influence the coupling coefficient. Consequently, an optimized winding distribution is proposed using an arithmetic progression for the inter-turn spacing, and the specific optimization process is provided. Additionally, the core shape of the transmitter coil in the traditional flat solenoid design is improved to better concentrate the magnetic field lines, enhancing magnetic field uniformity and increasing the misalignment tolerance of the coupling mechanism.

To verify the optimization effects, multiple simulation models were created with core shape and winding configuration as variables for comparison. Finite element simulation results show that the improved transmitter core achieves more uniform magnetic flux density distribution, significantly reducing the rate of change in the coupling coefficient. Magnetic field uniformity and misalignment tolerance are markedly improved. Finally, a 100 W WPT system prototype was built, and thermal imaging was used to analyze the system’s loss distribution.

Experimental results show that when the receiver is laterally misaligned within ±50% in both the X and Y directions, the output voltage fluctuation is controlled within 5%, and transmission efficiency reaches 89%. These results validate the effectiveness and feasibility of the proposed system.

Wireless charging technology is safer and more convenient than traditional wired charging, and has been widely used in the field of electric vehicles. However, wireless charging systems have the characteristic of separating the ground end and the vehicle end, and there are issues with the selection and measurement of power metering points. The mainstream approach is to set the measurement position of the wireless charging system on the transmitting coil. However, the current and voltage of the transmitting coil are relatively high, so that directly using sensors for measurement can lead to high sensor costs. To address the aforementioned issues, this paper proposed a non-contact measurement method for output power of transmitting coil of wireless charging systems using multi coil collaboration. This method did not require specialized high-power high-frequency current and voltage sensors, and adapted well to practical scenarios such as different power levels, horizontal and vertical ground displacement of cars, and shielding materials, and had high measurement accuracy.

The paper utilized the law of electromagnetic induction to measure the output power of transmitting coil by setting sensing coils. Firstly, it was inferred that there was a relationship between the output power of transmitting coil and the voltage product of the sensing coil. Secondly, the fitting coefficient was used to fit this relationship, and it was derived that when the positions of detection coils and transmission coil were fixed, the fitting coefficient did not shift with the receiving coil. After obtaining the fitting coefficient, only the terminal voltage of the sensing coil needed to be measured. Finally, by constructing the voltage matrix of sensing coils and the standard output power of transmitting coil matrix, the fitting coefficients were obtained, and a coupling matrix model was established between the voltage phasor of each sensing coil and the output power of transmitting coil considering horizontal offset, vertical offset, and power variation.

The experimental results show that under different power levels, the maximum measurement error of the proposed method is within 1.5% when the wireless charging system undergoes offset in both horizontal and vertical ground directions. Under the condition of 2 square sensing coils, the maximum error in power measurement was 47%. When the number of sensing coils increased to 6, the maximum error decreased to within 1.5%. This result indicates that as the number of sensing coils increases, the accuracy will further improve. Meanwhile, research has found that when the transmitting and receiving coil are square, using a square sensing coil results in more ideal accuracy. In addition, with the use of 6 detection coils, 3, 5, and 9 power sampling points were used within the measured power range. The maximum errors in power measurement were 7.8%, 3.6%, and 1.5%, respectively, indicating that the more sampling points are, the more accurate the model is. Finally, further exploration is conducted on the practical scenario of placing multiple sensing coils horizontally, which can avoid the problem of vertical stacking height affecting the short distance power measurement.

This method has been proven to be effective through simulation and experimental analysis. Through comparative analysis of the results, the following conclusions can be drawn: (1) The size and quantity of detection coils are key to ensuring the accuracy of the model. A sufficient number of sensing coils will bring higher model accuracy, but at the same time, it will also increase the number of samples in the model solving process. Therefore, in practice, a reasonable selection can be made based on the measured power error requirements. (2) For coupling devices where both the transmitting and receiving coils are square coils, the measurement accuracy using square sensing coils is more ideal compared to circular sensing coils. (3) A sufficient number of sampling points in model solving can also affect the accuracy of the entire system model. In practice, a reasonable selection can be made based on the measured power error requirements. (4) Horizontal placement of multiple sensing coils can achieve high-precision power measurement, just like vertical stacking. In practical scenarios, placing multiple sensing coils horizontally can avoid the problem of vertical stacking height affecting the short distance measurement of the transmitting and receiving coils.

The magnetic field's compactness can better increase energy's transmission distance in a near-filed wireless power transfer (WPT) system. This paper proposes a non-centrosymmetric excitation unit (NEU) design method for the WPT system with a matrix coupling mechanism to improve the magnetic flux density in the central region. The proposed method enhances the magnetic focusing performance with a flat two-dimensional structure while maintaining its misalignment tolerance without any other auxiliary coil or circuit. The self-inductance value of the transmitting coil in this design method is even smaller than that of the conventional design method under the same size and number of turns. In addition, the proposed method is applicable to all regular matrix coils. Then, a detailed design method of the coupler is given based on the circuit analysis. Finally, taking the LCL-LCC compensation WPT system as an example, the feasibility of the proposed design method is verified. Compared to traditional design methods, the proposed design method can increase the induced voltage by 24.7% at the optimal position.

The current mobile robot UAV (unmanned aerial vehicle)/AGV (automated guided vehicle) plays an important role in industrial inspection, office logistics, agricultural and forestry plant protection, and military reconnaissance and surveillance fields. A multi-level, multi-modal power supply demonstration application has been designed in intelligent parks and industrial inspections to achieve charging docking between unmanned devices. However, most power supply methods are fixed-point charging, which is complicated in the energy transmission process and faces energy loss problems. In special charging environments such as wilderness and underwater, if there is no suitable parking charging platform condition, UAV hovering charging technology has become the most necessary and perfect charging solution, and it is also the key to building wireless power transmission network environments. Based on the well-established technology of fixed-point hovering and hovering following for unmanned inspection equipment in the air-to-air, ground-to-ground, and air-to-ground scenarios, hovering and alignment energy replenishment is undoubtedly a highly efficient and cutting-edge design concept applied to unmanned inspection equipment. Nowadays, to further enhance the flexibility and reliability of wireless charging systems, more and more wireless charging systems are designed based on matrix coils. The matrix-based multi-excitation wireless charging system has gained more favor in charging applications due to its reconstruction mechanism of the magnetic field at the transmitting end and strong tolerance for voltage and current stress.

In summary, a matrix coil design method based on non-centrosymmetric excitation units is proposed, combined with an evaluation of the system's spatial field transmission capability to improve the optimization design steps and the focusing ability of the magnetic field. This design method, which has a two-dimensional planar structure, enhances the magnetic flux density at the center position without any auxiliary coils or circuits, avoiding the offset tolerance weakening of the matrix coil itself as much as possible. It reduces the self-inductance of the coil unit to improve mutual inductance utilization.

In underwater applications, such as underwater robots, autonomous underwater vehicles (AUVs), and remotely operated vehicles (ROVs), magnetic-field coupled wireless power transfer (MC-WPT) enables the transmission of electrical energy without electric contact, improving the flexibility and security of power transfer. Underwater electrical devices and base stations must achieve long-distance and high-power wireless power transfer while realizing high-speed bidirectional wireless information exchange to enable command transmission, data feedback, and closed-loop control. Many scholars have researched shared-channel magnetic-field coupled underwater simultaneous wireless power and information transfer (MC-USWPIT) technology. However, there is still a gap between the transmission distance, power transfer capacity, information transfer speed, and the requirements of engineering applications. Therefore, this paper proposes an underwater simultaneous wireless power and information transfer system with a coplanar double-coil coupler. The research focuses on rapid wireless power replenishment and high-speed bidirectional information transmission for AUVs in seawater. The goal is to achieve high-power energy transfer and high-speed bidirectional information transmission over long transmission distances.

The coupler with a coplanar double-coil and the MC-USWPIT system topology are proposed. Using the relay coil for information transmission reduces the voltage stress on the information transmission circuit and helps mitigate the crosstalk between the power and information transfer channels. By employing an injecting information method with a series of LC circuits in the information transmission channel, the LC circuit is fully compensated at the power transmission frequency. Furthermore, smaller capacitance-blocking capacitors further reduce the crosstalk between the power transmission channel and the information transmission channel, as well as the voltage stress on the information transmission channel, thereby reducing the difficulty of system design.

Subsequently, the system is analyzed and modeled, and equivalent circuit models for the power and information transfer channels are provided. A parameter design method for the MC-USWPIT system is proposed. The method reduces the eddy current losses induced by the seawater and minimizes the impact of high-power energy transmission on the information transfer speed. It enables the simultaneous improvement of transmission distance, power transfer capacity, and information transfer speed in a frequency-division multiplexed MC- USWPIT system.

Finally, a 5 kW experimental setup in simulated seawater is constructed. In an environment with a seawater conductivity of 4.15 S/m, the system achieved a transmission distance of 50 cm, an output power of 5.33 kW, and an information transfer speed of 5.68 Mbit/s. Furthermore, under varying seawater conductivities (4, 5, and 6 S/m) and transmission distances (30, 40, and 50 cm), the system still demonstrates good power transfer performance and high information transfer speed. The experimental results confirm that the proposed MC-USWPIT system and method can effectively improve the transmission distance, power transfer capability, and bidirectional information transfer speed in simulated seawater.