With the continuous development of mobile robots, battery detection and power management have become the current main research directions for robots. In order to address the insufficient adaptability of traditional technologies in dealing with dynamic environments and achieve the collaborative management of battery health status and multi-battery systems, a real-time charging state estimation algorithm based on extended Kalman filtering is proposed. This method simulates the signal transmission process of a robot battery pack, and uses the capacitance index to denote the amount of stored charge of the battery in the charging state. By using the least squares method to fit the voltage and current change curve in the circuit, a forgetting factor is applied to weaken the filtering saturation in the calculation process. The dynamic change curve in the circuit is processed by the extended Kalman filter to achieve battery charging state prediction. The proposed method has a prediction accuracy of 97.34%-98.75% for battery charging status. Meanwhile, the proposed model is used to simulate the battery's recharge test, and the battery's charge retention rate is maintained well, which is 12.68%-30.04% higher than other algorithms. In the application process, the proposed method has high battery durability under the recharge strategy, with an improvement of 14.62% -28.98% compared to other algorithms. Therefore, the proposed model can effectively identify the charging status of the robot, plan the recharge path reasonably, and improve the service life of the battery.

Abstract Under traffic congestion scenarios, the charging demand for electric vehicles (EVs) undergoes quantifiable variations. To better achieve refined control strategies for optimized dispatching of photovoltaic-storage-charging microgrids, this study proposes a hybrid prediction framework integrating Variational Mode Decomposition (VMD) and Long Short-Term Memory (LSTM) networks to enhance photovoltaic (PV) power generation forecasting accuracy. The revenue model of the charging station is established by dynamic programming, and the dispatching optimization model of electric vehicles is realized. Experimental analyses demonstrate the superior performance of the proposed VMD-LSTM model across diverse weather conditions. The mean absolute percentage error (MAPE) achieves 2.72%, exhibiting improvements over conventional methods: 2.84% for standalone VMD-LSTM, 6.22% for Support Vector Machine (SVM), and 4.76% for basic LSTM. Regarding charging scheduling optimization, by using price adjustment, the charging capacity of users is guided without affecting their satisfaction, and the revenue of charging stations is improved. Comparative results reveal 15.8% and 18.7% revenue enhancements in congestion scenarios 2 and 3 respectively, despite a marginal 1% reduction in user satisfaction indices.

For wireless charging systems operating at high powers of up to tens of kW, high-intensity electromagnetic fields can have a significant impact on surrounding electronic devices and the human body. In this study, Electromagnetic Field (EMF) simulations were performed for stationary charging modules targeting electric vehicles (EVs) and dynamic charging modules targeting EV buses, and the derived magnetic field data was used to perform induction analysis inside a human body model. Five scenarios assuming the worst exposure situation are provided, and the intensity of the magnetic field and the induction inside the human body were compared and evaluated with international guidelines (ICNIRP). The results of this study can be used to secure the safety of wireless charging modules and design criteria for shielding structures.

Design and implementation of a high-frequency inverter with wide soft-switching range for dynamic wireless charging of electric vehicles

A Compact Dynamic Wireless Charging System for Autonomous Consumer Platforms

Dynamic Electric Vehicle Charging Pricing for Load Balancing in Power Distribution Networks based on Collaborative DDPG Agents

A graph neural network enhanced decision transformer for efficient optimization in dynamic smart charging environments

Electric vehicles (EVs), as carriers of moveable loads, can not only store energy during off-peak electricity consumption but also feed energy back to the grid during peak electricity consumption. However, along with the scale surge of the EV industry, massive EV charging–discharging behaviors present randomness and disorder characteristics, affecting the stable operation as well as the balance of supply and demand for the grid. Thus, this paper proposes a spatiotemporal bilevel charging–discharging scheduling strategy for urban EVs based on functional zoning. First, the EV charging demand, geographic characteristics, and node coupling degree within each functional region are analyzed. A functional zoning method based on reactive voltage sensitivity and modularity coefficients is proposed. Second, a spatiotemporal bilevel charging–discharging optimization scheduling model is established. The upper-level performs rolling prediction of EV charging–discharging time window length, whereas the lower-level provides spatial guidance of EV charging–discharging locations. Specifically, the upper-level utilizes long short term memory to predict EV charging–discharging time, minimizing the voltage deviation. The obtained temporal features are imported into the rolling optimization model, adjusting the EV charging–discharging time window length. Then, the lower-level inputs the optimal results of the upper-level. Additionally, differentiated EV user behavior guidance strategies are established based on the regret theory and the dynamic charging–discharging cost model, achieving spatial optimal scheduling. Finally, a modified binary gravity search algorithm is developed, integrating the binary coding mechanism, adaptive gravitational constant, and dynamic particle update scheme. Case studies are conducted within an IEEE-123 bus system. Numerous experimental results show that the proposed methodology optimizes the spatiotemporal distribution of urban EV charging loads and improves the operation efficiency of the grid. It provides a novel idea for the friendly interaction of vehicle-to-grid as well.

The fast adoption of electric motors (EVs) needs charging answers that are green, consumer-pleasant, and seamlessly integrated into each day mobility. conventional plug-in charging faces demanding situations associated with person inconvenience, connector put on, protection, and infrastructure scalability. wi-fi strength switch (WPT) for EVs—primarily based on electromagnetic coupling—emerges as a promising opportunity, permitting contactless strength switch for stationary and dynamic charging packages. This paper affords a comprehensive look at of wireless power transfer technologies for electric motors, covering working concepts, device architectures, strength electronics, control strategies, and efficiency optimization. We overview inductive and resonant WPT methods, examine requirements and protection constraints, and talk alignment tolerance, energy degrees, and interoperability. A simulation-primarily based assessment framework is outlined for stationary and in-motion charging eventualities, assessing efficiency, energy transfer functionality, electromagnetic compatibility, and grid impact. practical deployment demanding situations, which includes infrastructure value, grid integration, and regulatory issues, are discussed together with destiny studies guidelines. The take a look at demonstrates that optimized WPT systems can extensively beautify EV charging comfort, enhance protection, and guide scalable electrified transportation.

Electric vehicles (EVs) are one of the best replacements of conventional vehicles due to environmental friendly nature. The poor State of Charge (SOC in %) control and large settling time & peak overshoot of speed are the few research gap which need to be address due to continuous degradation of battery. The paper demonstrates the conceptual design and implementation of a solar-powered electric vehicle that uses soft computing methods for smart control. The implementation of a dynamic solar-powered electric vehicle charging stations combining smart control & soft computation methods requires careful, optimal design, charging the economy, & regular upkeep. Nonetheless, it can offer an environmentally friendly, long-term EV charging option and perhaps reduce continuing operational costs. Because electric vehicles emit no pollutants, this work also assists global efforts to reduce emissions of greenhouse gases and combat climate change. In this paper, electric vehicle charging has been assessed at various voltage levels. Subsequently, an electric vehicle is controlled by DC motor. In addition to state of charging of electric vehicle which is measured in terms of state of charging, the speed of electric vehicle is analyzed. In order to attain the smooth operation of speed and SOC, an objective function has been developed. Further, the objective function has been controlled by using artificial neural network and genetic algorithm. It is observed that SOC (%) shows better and smoother performance with GA in comparison to ANN and existing methods. In addition to this, settling time and peak overshoot of the speed has been improved a lot with GA (2.5 sec, 2%) and ANN (3.1 sec, 2.7%).

<p class="abstract">Electric vehicles (EVs) have transformed the transportation sector, offering a sustainable alternative to fossil-fuel-powered vehicles. However, their widespread adoption faces challenges such as inadequate charging infrastructure, range anxiety, and concerns about user convenience. Wireless power transfer (WPT) technology provides an efficient, reliable, and user-friendly charging solution that eliminates physical connections, enabling both static and dynamic charging applications. This review explores key components of WPT systems, including wireless charging schemes, compensation circuits, coupling pad structures, and misalignment tolerance, emphasizing their impact on system efficiency and reliability. Findings highlight that WPT can enhance charging convenience, reduce dependence on large battery capacities, and support seamless EV integration into daily life. Additionally, WPT systems improve safety, lower maintenance needs, and create opportunities for autonomous charging. Key advancements in compensation topologies, coupling pad geometries, and misalignment-tolerant capabilities are discussed alongside their role in enhancing power transfer efficiency. By offering insights into the current state-of-the-art and future directions, this paper aims to support the development and deployment of WPT systems, contributing to the global transition toward sustainable transportation.</p>

Lithium-ion batteries (LIBs) are widely used in electric vehicles (EVs) due to their excellent characteristics, including high energy density, energy efficiency, long cycle life, and power stability. However, compared to internal combustion engine vehicles(ICE Vehicles), the long charging time remains a major limitation of EVs. To resolve this, high-current charging has been explored. Nevertheless, applying high current can lead to reduced energy efficiency, capacity fade, and power degradation, thereby revealing the limitations of simply increasing the charging rate. To overcome these issues, extensive research is being conducted not only on battery materials but also on charging strategies.This study explores the potential of suppressing battery degradation and extending battery life by employing a dynamic charging technique that introduces brief discharge patterns during the charging process. This novel charging method, which involves applying discharge pulses during charging, is experimentally compared with conventional constant current (CC) and multistage constant current charging methods to evaluate its effectiveness in reducing degradation.Previous studies have reported that under discharge conditions, dynamic cycling patterns that mimic real driving conditions—such as regenerative braking and rapid acceleration/deceleration—can more effectively suppress battery degradation than standard CC-based discharge cycles. This study extends that concept to the charging domain, hypothesizing that applying similar dynamic patterns during charging may also contribute to degradation mitigation.Commercial 18650 cylindrical lithium-ion cells were used for the experiments. Dynamic charging patterns with short discharge pulses were compared with CC charging across different state-of-charge (SOC) ranges (low, medium, and high). Throughout the charge-discharge cycles, degradation indicators such as capacity fade and increases in direct current internal resistance (DCIR) were periodically monitored using Reference Performance Tests (RPT) and Hybrid Pulse Power Characterization (HPPC) protocols. The results demonstrated that dynamic charging patterns had a more significant degradation suppression effect in certain SOC ranges compared to others.Additionally, an artificial intelligence (AI) model was applied to estimate state of health (SOH) and DCIR based on the voltage and current response data collected during the pulse tests. This approach presents a novel method for diagnosing battery condition solely using pulse responses generated during the charging process, without the need for additional testing equipment or disassembling the cells. By utilizing the brief discharge pulses that occur repeatedly in every charging cycle, this method enables continuous monitoring of battery degradation status, offering improved efficiency and practicality over traditional diagnostic techniques.In conclusion, this study proposes a novel charging method capable of mitigating battery degradation. Beyond degradation reduction through dynamic charging patterns, the study also presents an innovative approach wherein the charging process itself functions as a diagnostic tool. This contributes meaningfully to the fields of battery health diagnosis and life prediction. Furthermore, by optimizing SOC ranges and implementing data-driven charge control, the proposed method is expected to improve battery life and contribute to the development of lightweight diagnostic algorithms suitable for integration into future battery management systems (BMS).

The development of robust and efficient wireless charging systems is essential for the widespread adoption of electrification in the transport sector, e.g., Electric Vehicles (EVs). Capacitive Wireless Power Transfer (CWPT) has emerged as a promising alternative to inductive methods, offering advantages such as lower cost, lighter structure, and reduced electromagnetic interference. However, the performance of practical CWPT systems, particularly systems employing simple L-type compensation networks, is severely affected by coupling plate misalignment, which causes variations in coupling capacitance. These variations give rise to a pseudo-resonance phenomenon, wherein conventional controllers, such as traditional Sliding Mode Control, mistakenly regulate reactive power to zero at an off-resonant frequency, leading to a drastic collapse in active power transfer. To overcome this limitation, this paper introduces a novel Adaptive Sliding Mode Control (ASMC) framework augmented with an online Recursive Least Squares (RLS) observer for real-time estimation of the time-varying coupling capacitance. The proposed dual-loop control structure integrates an inner adaptive loop that accurately tracks capacitance changes and an outer sliding mode loop that dynamically adjusts the inverter switching frequency to sustain true resonant operation. A rigorous Lyapunov-based stability analysis confirms global convergence and robustness of the closed-loop system. Comprehensive MATLAB/Simulink R2025a simulations validate the proposed approach, demonstrating its capability to maintain zero reactive power and stable 35 kW power transfer with over 95% efficiency under dynamic misalignment conditions of up to 30%. In contrast, a conventional SMC approach experiences severe pseudo-resonant collapse, with output power degrading below 1 kW. These results conclusively highlight the effectiveness and necessity of the proposed ASMC-RLS strategy for achieving robust, misalignment-tolerant CWPT in high-power EV charging applications.

ABSTRACT Charging strategy optimization for lithium‐ion batteries is crucial to improve the efficiency of new energy devices. In this study, three conventional methods are compared: constant‐current constant‐voltage (CCCV) charging is the most efficient but slowest, pulse charging (PC) is the fastest but least efficient, and multistage constant‐current (MSCC) is a compromise between speed and efficiency. To this end, we propose a dynamic optimal charging strategy based on model predictive control (MPC) that balances rapid‐charging speed with battery safety. By integrating a low‐order electrochemical–thermal–aging coupled model with real‐time state estimation provided by an extended Kalman filter (EKF), a rolling‐horizon framework is established to track both state‐of‐charge (SOC) and temperature reference trajectories. Experiments show that EKF has stronger initial error robustness (maximum deviation < 2%) than unscented Kalman filter (UKF) and unscented Kalman Bucy filter (UKBF), which provides reliable feedback for MPC. The new strategy achieves an optimal balance between charging efficiency and safety by dynamically adjusting the charging profile and significantly improves the charging speed under closed‐loop control compared to the CCCV method, while controlling the temperature rise within 5°C.

This paper presents a Data-Augmented Model Predictive Control strategy that is optimized with Multi-Objective Particle Swarm Optimization for Active Power Filters operating in Electric Vehicle charging networks with random and unbalanced load profiles. The approach integrates physical modeling with data-driven forecasting based on historical charging logs, environmental parameters, and tariff signals to predict harmonic-rich current and a specific amount of demand based on previous events. These predictions form the basis of an adaptive MPC cost function that is optimized using MOPSO to minimize Total Harmonic Distortion, reactive power, and switching, while maintaining DC-link envelope and IEEE-519 compliance. Simulation results show that the proposed controller greatly improves the power quality and energy efficiency of the system. Under unbalanced nonlinear load profiles, THD is minimized to 2.7%, leading to an 88% reduction compared to the uncompensated case and 35% cost savings compared to conventional MPC. The power factor increased from 0.93 to 0.998, and DC-link ripple was confined to ±1.5%. Switching frequency decreased by 8%, lowering overall system efficiency by 2%. Resilience was verified in Monte Carlo simulations with N = 100 using Gaussian noise with σ = 3%, random grid impedance variation, and dynamic EV charging events under multiple random scenarios. The THD was 2.6% to 2.9%, far below IEEE limits, demonstrating robust disturbance rejection and dynamic adaptability. So, by combining optimal MOPSO with predictive intelligence from data, the MOPSO–MPC methodology guarantees near-unity power factor, low distortion, and enhanced stability under realistic grid variabilities. This makes it an appealing and scalable solution for improving power quality in smart-grid applications, as well as coordinating and deploying renewable energy facilities and Electric Vehicle infrastructure for electro-mobility.

Abstract - Wireless charging technology for electric vehicles (EVs) is emerging as a revolutionary solution to address the limitations of traditional plug-in charging systems. It offers enhanced convenience, safety, and automation by enabling energy transfer without physical connectors. This paper provides a comprehensive overview of wireless charging technologies, focusing on the principles, classifications, and advancements in static, dynamic, and quasi-dynamic systems. The study examines the electromagnetic coupling mechanisms such as inductive power transfer (IPT), capacitive power transfer (CPT), and resonant inductive coupling (RIC), highlighting their advantages, limitations, and design considerations. The research also discusses the role of power electronics, control algorithms, and magnetic field alignment techniques in optimizing energy efficiency. A comparative evaluation of existing technologies is presented to demonstrate their performance in terms of transfer efficiency, cost, and interoperability. Furthermore, the paper explores research gaps, challenges, and potential future directions, emphasizing the need for standardization, material innovation, and smart-grid integration. The results underscore that while wireless charging is technically feasible, significant efforts are still required to achieve large-scale commercial adoption for both static and dynamic charging infrastructures. Key Words: Wireless Power Transfer, Electric Vehicles, Inductive Charging, Dynamic Charging, Resonant Coupling.

HighlightsWhat are the main findings?Magnetite increased received power up to 13% and efficiency gains up to 2%.Thicker asphalt layers reduced transfer, but magnetite mitigated these losses.Magnetite mixtures maintain WPT performance under thicker layers.What is the implication of the main finding?Multivariate analysis guides optimal design of electrified pavements.Electrified roads with embedded wireless power transfer (WPT) systems provide a promising strategy for dynamic charging of electric vehicles, but pavement materials strongly influence transmission efficiency. This study examines the effect of replacing conventional filler with magnetite powder in AC-16 asphalt mixtures. Specimens were prepared with five magnetite substitution levels (0–100%) and three bitumen contents (4.1%, 4.6%, and 5.1%) and were tested under different temperatures (10, 20, and 40 °C), moisture conditions (dry and saturated), and specimen thicknesses. Power transmission was measured with a resonant inductive system at 85 kHz, and both received power variation (RPV) and relative efficiency (RE) were computed. Results showed that magnetite systematically improved electromagnetic performance: RPV increased by up to 13% under dry conditions at 20 °C with 100% magnetite, while RE exhibited smaller variations (−1% to +2%). Moisture reduced RPV, and high temperature (40 °C) caused additional losses, whereas RE remained largely stable. Bitumen contributed indirectly, adding modest RPV gains. Thickness was the dominant geometric factor, with magnetite content particularly effective in mitigating losses at greater depths. Random forest analysis confirmed thickness and magnetite as the most influential variables. These findings demonstrate the potential of magnetite-modified asphalt to enhance the design of WPT-enabled pavements, providing a robust experimental basis for future full-scale applications.

In this study, we investigate a shielded capacitive power transfer (S-CPT) system that employs cast iron road covers as transmission electrodes for both dynamic and static charging of electric vehicles. Coupling capacitance was evaluated from S-parameters using copper, aluminum, ductile cast iron, structural steel, and carbon steel electrodes, with additional comparisons of ductile iron surface conditions (casting, machining, electrocoating). In a four-plate S-CPT system operating at 13.56 MHz, capacitance decreased with electrode spacing, yet ductile cast iron reached ~70 pF at 2 mm, demonstrating a performance comparable to that of copper and aluminum despite having higher resistivity and permeability. Power transmission experiments using a Ø330 mm cast iron cover meeting road load standards achieved 58% efficiency at 100 W, maintained around 40% efficiency at power levels above 200 W, and retained 45% efficiency under 200 mm lateral displacement, confirming robust dynamic performance. Simulations showed that shield electrodes enhance grounding, stabilize potential, and reduce return-path impedance. Finite element analysis confirmed that the ductile cast iron electrodes can withstand a 25-ton design load. The proposed S-CPT concept integrates an existing cast iron infrastructure with thin aluminum receiving plates, enabling high efficiency, mechanical durability, EMI mitigation, and reduced installation costs, offering a cost-effective approach to urban wireless charging.

A review of static and dynamic charging in electric vehicle routing: Transition, algorithms and future directions

Exploring selective continuous learning for battery state of charge prediction in real-world dynamic charging scenarios