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Review

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

by
Shuguang Wei
,
Feifan Xu
,
Dong Yuan
*,
Kewei Chen
,
Bin Liu
and
Jiaqi Li
Department of Control Engineering, Army Academy of Armored Forces, Beijing 100072, China
*
Author to whom correspondence should be addressed.
Energies 2025, 18(3), 592; https://doi.org/10.3390/en18030592
Submission received: 7 January 2025 / Revised: 22 January 2025 / Accepted: 25 January 2025 / Published: 27 January 2025
(This article belongs to the Section E: Electric Vehicles)
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Abstract

:
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.

1. Introduction

Electric vehicles (EVs) have experienced rapid growth in recent years due to the use of very efficient electric motors, high-voltage energy storage systems, and improved electrified power train with respect to gas-powered vehicles [1,2,3,4]. Governments worldwide have strongly supported the advance of EVs since the vigorous development of EVs plays a significant role in improving energy security, promoting energy conservation, and preventing air pollution [5,6]. However, this promotion continues to encounter various challenges, including limited driving miles and inconvenient charging processes. Due to the limitations of vehicle battery capacity, electric vehicles must be charged more often, even on a daily basis [7]. There are two main ways of charging: wired charging and wireless charging [8]. Wired charging has been used for a very long time but comes with several limitations, such as plug damage, security risks, and the need for manual operation. Compared to wired charging, wireless charging has advantages of convenience, high safety, and low maintenance cost, and so it has attracted increasing attention. Among the means of wireless charging, magnetic coupling resonant wireless charging is one of the most popular methods for EVs because it is generally insensitive to the coupling coefficient and spatial non-magnetic obstacles [5].
With the development of artificial intelligence, many scholars have focused on the optimization of or novel control strategies for wireless charging by means of intelligent algorithms (IA) [9]. The wireless charging system consists of the primary side and the secondary side [10,11], as shown in Figure 1. The energy source of the primary side is generally the direct current (DC) voltage obtained through the rectification of the city power grid. The high-frequency inverter converts the DC voltage into high-frequency alternating current (AC) voltage, which is controlled by the compensation circuit of the primary side to reduce or even eliminate reactive power loss. At the transmitter of the coupling coils, the high-frequency AC voltage is transformed into magnetic energy. The receiver in the coupling coils converts the magnetic field energy into high-frequency AC voltage, which is controlled by the compensation circuit of the secondary side. The primary and secondary side of the wireless charging system are controlled by the primary-side and the secondary-side controllers, respectively. To enhance the performance of wireless charging systems, it is necessary to optimize the design of each component in the system. For example, optimization could be conducted in which the compensation circuits, coupling coils, and control algorithms are parameterized, designed, and explored. Among them, the application of various IAs, including swarm optimization algorithms, bionic algorithms, and deep learning, is demanded by each optimizing process [12,13,14].
The IAs frequently employed in the design of the EV wireless charging systems are comprehensively examined in this paper. It elaborates on the optimization targets, processes, advantages, and limitations of these IAs, thereby offering valuable insights for the intelligent optimization design of the EV wireless charging systems. The rest of this article is organized as follows. Section 2 reviews the current situation of IAs in the design of compensation circuits in terms of the improvement mentality, parameter optimization, and structure design. In Section 3, the research status of IAs in the optimal design of coupling coils is thoroughly investigated. Section 4 discusses various controllers based on IAs, which are capable of implementing various control strategies for the EV wireless charging system. In Section 5, the future work of IA applications in wireless charging systems are analyzed and outlined.

2. IA Based Optimization of Compensation Topology in Wireless Charging System

Inside the wireless charging systems, owing to the weak coupling between the transmitting and receiving coils, a compensation circuit is typically required to improve system performance [15,16,17,18]. The principal aim of the compensation circuit is to ensure transmission efficiency, sustain stable output voltage and current, and, therefore, to advance and optimize system performance under different load and coupling conditions. Inside the compensation circuit, its resonant frequency and impedance matching are modified to accomplish soft switching, reduce switching loss, and increase the power transmission capacity [19]. Furthermore, the compensation circuits are critical to diminishing the voltage and current stress on components, improving system reliability and safety, thereby guaranteeing the stability of output voltage and current under load variations, which is an essential part of the wireless charging system [20,21,22].

2.1. Target of Compensation Topology Optimization

The topologies of the compensation circuits are classified into two types based on the quantity of resonant inductors and capacitors as well as the connection mode of series and parallel compensation: the basic compensation topology and the high-order compensation topology [4,23]. Basic compensation topologies encompass S/S, S/P, P/S, and P/P (S and P stand for series capacitance and parallel capacitance compensation, and the left and right sides of the forward slash represent the compensation forms of the primary and the secondary sides, respectively), while high-order compensation topologies include LCC/LCC and LCC/S [4,11,24] (L and C denote inductive and capacitive compensation, while the other symbols are the same as basic compensation topologies, respectively). The aforementioned topology structures are shown in Figure 2.
Compensation topology requires consideration of the following factors in parameter optimization and structure design, which is assisted by IA in numerous research achievements [25,26].
(1) Enhancement of System Efficiency:
The compensation topology enables the coupling coils to operate in resonance, thereby minimizing energy loss and enhancing transmission efficiency [27].
(2) Optimization of Output Characteristics:
The compensation topology is capable of optimizing the output voltage and current characteristics of the wireless charging system. A stable output voltage or current is realized under variable load and coupling coefficient conditions [28].
(3) Reduction of Voltage and Current Stress on Components:
Through the design of the compensation topology, the voltage and current stress imposed on components can be mitigated, which enhances the reliability and safety of the wireless charging system [29].
(4) Achievement of Soft Switching:
The design of the compensation topology reduces the switching loss of the inverter and facilitates soft switching to reduce the power consumption [30].
(5) Solution of Frequency Mismatch:
The issue of frequency mismatch can be resolved in some novel compensation topologies [6].

2.2. Parameter Optimization of Compensation Topology

The parameter optimization of basic compensation topologies primarily focuses on optimizing the system resonant frequency and capacitor parameters. Since there are fewer compensation components than those of the high-order compensation topologies, it is feasible and possible to execute optimization with a limited number of parameters [31].
Various parameter optimization methods are applied to solve the parameter optimization issues for basic compensation topologies. Li et al. [32] introduced a power tracking method based on an improved variable-step perturbation observation (VSPO) algorithm to handle the detuning shortcomings resulting from capacitor parameter drift in S/S compensation topology. Compared to traditional tuning approaches, the proposed method features a fast response, high accuracy, low complexity, and is less likely to over-track. Gao et al. [33] applied the ant colony optimization (ACO) algorithm to achieve an operator-based robust control, which is an efficient approach for S/S compensation topology. The proposed method enhances tracking performance by adopting the optimal parameter and ensures robust stability without complex mathematical verification. To enhance the performance of the wireless charging system, Zhang et al. [34] presents a dual-layer nested optimization (DLNO) approach by employing the differential evolution (DE) algorithm to determine the capacitance values. It is discovered that the proposed parameter optimization method performs at a much higher wireless charging efficiency compared to that of the traditional method, which is verified by the finite element method (FEM). Niu et al. [35] introduced a harmonic model (HM) to select the optimal frequency when seeking to improve the output power of system rather than limiting it to operating at the inherent resonant frequency.
High-order compensation topologies possess a greater number of compensation components and a more diverse topological structure [36,37,38]. Therefore, there is a higher degree of design freedom, which has attracted the attention of several research groups. Since the wireless charging systems operate under resonant conditions, even minor deviations of the components from their nominal values can result in a significant reduction of the power transferred to the load, along with an increase in the circulating currents, thereby reducing the system efficiency. Therefore, it is necessary to carry out an IA-based optimization design of the device parameters. Corti et al. [39] and Hassan et al. [40] used a genetic algorithm (GA) to improve an innovative design process. The proposed method can obtain several parameter combinations of the LCC/S compensation topology, and is capable of attaining the desired output power. Zhuo et al. [41] proposed a parameter design approach based on the chaotic sparrow search (CSS) algorithm to optimize the transmission efficiency of the wireless charging system. The sparrow search algorithm is enhanced by adopting the chaotic reverse learning strategy. The system is parameterized and optimized by the improved algorithm in terms of the frequency, coupling coefficient, and load resistance. Furthermore, the Bayesian method was used to realize capacitance optimization based on the comprehensive consideration of the capacitance, as mentioned by Xia et al. [42]. Research by Cai et al. [43] introduced a novel method combined with GA and nonlinear programming to optimize the compensating parameters of the system. Taking S/LCC compensation topology as an example, a nonlinear programing model with the objective function of minimum voltage gain difference is established, and minimum fluctuation of output voltage gain within any given range of mutual inductance parameters is realized with mutual inductance changing. For the LCC/LCC compensation topology, Zhou et al. [44] proposed a constrained adaptive particle swarm optimization (CAPSO) algorithm with high precision to optimize the output power, efficiency, and cost of the wireless charging system of EVs.

2.3. Structure Design of Compensation Topology

Designing reconfigurable compensation topologies and using IAs for optimization are the common approach in the research of high-order compensation topology [36,45,46]. Yang et al. [47] put forward a multi-objective particle swarm optimization approach for the reconfigurable topology to achieve constant current (CC) output and constant voltage (CV) output modes under varying resistance conditions and wide coupling ranges. The output features of an LCC/LCC compensation topology were investigated and eight optimized compensation parameters of the reconfigurable topology were acquired. By switching the compensated capacitors, the selected parameters can change the current and voltage fluctuations slightly during the coupling coefficient charging. Esfahani et al. [48] employed a linear programming algorithm to determine the appropriate values for reconfigurable compensation topology, which facilitated achieving high efficiency over a broad range of loads and zero voltage switching (ZVS) for the proposed system. As mentioned by Chen et al. [49], a hybrid and reconfigurable wireless charging system with 3-dimension misalignment tolerance for CC and CV outputs is proposed and a novel parametric design method is given, which suppresses the fluctuation of the output voltage/current within a certain range of misalignment. In order to withstand the coupling variation for the wireless charging system with stable transmission power, Chen et al. [45] introduced a reconfigurable compensation topology based on the parameter design method according to power-coupling coefficient (P-k) curves. The four reconfigurable topologies mentioned above are shown in Figure 3.
The performance of the above mentioned IAs applied to the compensation topology is shown in Table 1.

3. IA Based Design of Coupling Coils in Wireless Charging System

In addition to the optimization of the compensation topology, the optimization of coupling coils plays an equally critical role in the overall design of wireless charging systems. The coupling coils serve as a crucial component in the wireless charging system for the transmission of energy between the primary and secondary sides [50]. The magnetically coupled connection of the coupling coils can effectively transmit electrical energy, minimize energy loss, and enhance charging efficiency [51]. In addition, the alignment issue between the coupling coils directly influences the efficiency of energy transmission [52,53]. The structure design of coupling coils, as well as the control of the distribution and intensity of the magnetic field, are the key to achieving efficient energy transmission. Current research on coupling coils primarily focuses on enhancing the coupling coefficient, strengthening the ability to withstand misalignment, as well as reducing the size and cost of the coupling coils. A considerable number of studies concentrate on optimizing related parameters through the utilization of IAs.

3.1. Parameter Optimization of Coupling Coils

The parameters of the coupling coils encompass the size of the coils, the turn number of coils, and the spacing between turns, which exert an influence on the self-inductance and mutual inductance of the coupling coils [30,52,54]. Fundamentally, parameter optimization of coupling coils constitutes a multi-objective optimization approach. Conventionally, finite element simulation software is employed to iterate parameter combinations and find the optimal solution. Nonetheless, this approach is not only time-consuming but also laborious. Recently, the application of IAs has effectively addressed these issues, and, as a result, it has gained widespread usage. Zhang et al. [55] introduced a coil parameter optimization method based on crowding distance division and adaptive genetic operators. The proposed study improves the nondominated sorting genetic algorithm-II (NSGA-II) and the best transmission performance is achieved through optimizing the design of decision variables, such as the number of coil turns, the spacings between coupling coil turns, and the vertical distance of the coupling coils. A similar approach is taken by Wu et al. [56], who introduced a PSO model with transmission efficiency as the optimization objective. The distribution spacing between coils, the working frequency, and the number of coils are optimized and an optimization scheme of non-equidistant arrangement is proposed. To steady the mutual inductance of the coupling coils, Wang et al. [9] presented a tactic for adapting the mutual inductance with the aid of an IA optimization. Rong et al. [57] applied GA for parameter optimization of a coupling coil, which is attached to UAV landing gear. Hu et al. [58] employed the NSGA-II algorithm for the multi-objective optimization of the autonomous underwater vehicles (AUVs) coupling coils. Similarly, to optimize the coupling coils of AUVs, the parameters of the coupling coils that impact the magnetic flux density were analyzed and optimized by non-linear programming by the quadratic Lagrangian (NLPQL) algorithm to achieve a field with desirable intensity and uniformity, as mentioned by Lyu et al. [59]. Jang et al. [60] and Jeong et al. [61] used a deep Q-learning network (DQN) based on the reinforcement learning (RL) algorithm to find the optimal number of turns and the core structure of coupling coils, respectively. Compared with traditional methods, the proposed algorithm has the potential to reduce the design time and cost and improve the overall performance of wireless charging systems.

3.2. Structure Design of Coupling Coils

The structure design of the coupling coils is as essential as parameter optimization in the design of wireless charging systems, and IAs play an important role in this process [12,30]. Zhang et al. [62] applied the three-coil structure for a wireless charging system, whose parameters are optimized by an evolutionary algorithm. Compared with the two-coil structure, the distance of power transmission, efficiency, and electromagnetic field (EMF) leakage are effectively improved by incorporating relay coils. Optimized by an evolutionary algorithm of coupling coil parameters, the coil current is reduced, transmission efficiency is improved, and electromagnetic field leakage is suppressed. An optimization method for arranging a four-coil wireless charging system is proposed to improve medium or long-distance power transfer efficiency, as mentioned by Wu et al. [63]. For the six-coil configuration wireless charging system, Zhang et al. [64] introduced a method for optimizing coupling coil parameters based on machine learning. The convolutional neural network is adapted for training and predicting the performance of a pair of coupling coils under a set of input parameters, which are critical in determining the efficiency and selecting optimal coil parameters such as the number of turns and wire diameter. The figures of the coupling coil structure with relay coils are shown in Figure 4.
Beyond optimizing the relay coils, structure design of coupling coils also achieves the desired outcome by designing special structures and optimizing them using IAs [65,66,67]. Zeng et al. [68] introduced a multi-objective genetic algorithm to optimize multidirectional coupling coil parameters, aiming at high coupling strength and low core volume. Tan et al. [69] designed hexagonal coupling coils and proposed an optimization strategy based on GA, which considers the actual requirements of the efficiency and power of the wireless charging system. Li et al. [70] proposed a novel coil structure with eight solenoid coils and one regular octagon coil, which is optimized by finite element analysis (FEA). The proposed structure can tolerate all 360° rotational misalignments and large-distance horizontal offset for UAV wireless charging. The special structures of the coupling coils are shown in Figure 5.

4. IA Based Research of Control Strategies in Wireless Charging System

After optimizing the topology and coil structure using IAs, it is also vital to design the wireless charging controller using IAs. The control circuit in the wireless charging system is accountable for precisely controlling the power output, optimizing the system efficiency, and tracking the system parameters to guarantee the stability and safety of the wireless charging process [71,72,73]. The related controllers are mainly designed with the use of IAs.

4.1. Control Strategies of Power Controlling

Li et al. [71] proposed an innovative adaptive strategy based on PSO to address the output power issues resulting from the misalignment of coupling structure and the variability of load, which significantly enhances the output power of the wireless charging system. To realize the maximum power point tracking (MPPT) of the wireless charging system, Chen et al. [74] introduced an invasive weed optimization (IWO) algorithm to change the current injected into the orthogonal coupling coils to indirectly achieve the maximum power of the load. The control circuit of the overall system is shown in Figure 6.

4.2. Control Strategies of Efficiency Optimizing

To realize the maximum efficiency transfer of the wireless charging system, Li et al. [75] applied a P&O algorithm control method to regulate the current of the primary and secondary sides in order to achieve stable constant power charging. Zhao et al. [76] introduced a multi-level coordinated control efficiency optimization method for wireless charging systems utilizing the PSO algorithm. The proposed method considers the transmission losses across all power conversion units within the system as well as establishing a mathematical model for the joint optimization of overall system transmission efficiency and power. As mentioned by Cao et al. [77], a new model predictive control (MPC) method based on a computationally efficient hybrid optimization scheme is proposed to improve the efficiency of the EV wireless charging system. The circuit diagrams of the efficiency optimization control strategy are shown in Figure 7.

4.3. Control Strategies of Parameter Tracking

Yang et al. [78] introduced an improved whale optimization (WO) algorithm to complete the PI parameter tuning of the frequency tracking controller. The proposed algorithm has better performance for the PI parameter tuning of frequency tracking control in the wireless charging system, which can effectively help reduce the investment of human resources. As mentioned by Gao et al. [79], a modified genetic algorithm (MGA) is proposed to realize the CV output of wireless charging in the presence of perturbation. Figure 8 shows the circuit diagrams of the parameter tracking strategy.
The performance of the above mentioned IAs applied to the control strategy is shown in Table 2.

5. Conclusions

This paper reviews the current state of EV wireless charging systems and the application of IAs. A brief discussion on the application of IAs in compensation topologies, coupling coils, and control strategies of wireless charging systems for EVs are discussed. Beyond the IAs referred to herein and their applications in wireless charging systems that are cited in this paper, more IAs can be used to improve the design process of various parts of EV wireless charging systems. Since the EVs, wireless charging, and the IAs belong to the emerging industry, which have proposed, applied, and studied in recent decades, there are many challenges of EV wireless charging system that are still waiting to be addressed. These include the misalignment of the coupling coils, the detection of foreign objects between coupling coils, electromagnetic compatibility design, and collaborative optimization with the battery management system of electric vehicles.
As a result, IAs demonstrate significant R&D value and research prospects, not only in the field of EVs, but also many other industrial applications. This is especially true in industrial technology development, from the preliminary designing process to improving overall performance.

Author Contributions

Conceptualization, F.X. and D.Y.; methodology, S.W.; software, J.L.; validation, F.X., S.W., and K.C.; formal analysis, D.Y.; investigation, S.W.; resources, D.Y.; data curation, K.C.; writing—original draft preparation, F.X.; writing—review and editing, B.L.; visualization, F.X.; supervision, J.L.; project administration, D.Y.; funding acquisition, S.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Industrial Bureau of China (grant number 2022XX04). The APC was funded by Dong Yuan.

Data Availability Statement

This study did not include any data.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Structure of wireless charging system for EVs.
Figure 1. Structure of wireless charging system for EVs.
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Figure 2. Structure of compensation topologies: (a) S/S; (b) S/P; (c) P/S; (d) P/P; (e) LCC/LCC; (f) LCC/S.
Figure 2. Structure of compensation topologies: (a) S/S; (b) S/P; (c) P/S; (d) P/P; (e) LCC/LCC; (f) LCC/S.
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Figure 3. Structures of reconfigurable compensation topologies: (a) Regulated LCC/LCC compensation Topology; (b) Double-sided reconfigurable compensation topology; (c) Combination of hybrid topology and reconfigurable topology; (d) Reconfigurable compensation topology with delta network and wye network.
Figure 3. Structures of reconfigurable compensation topologies: (a) Regulated LCC/LCC compensation Topology; (b) Double-sided reconfigurable compensation topology; (c) Combination of hybrid topology and reconfigurable topology; (d) Reconfigurable compensation topology with delta network and wye network.
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Figure 4. Structures of coupling coils with relay coils: (a) Three-coil structure; (b) Four-coil structure; (c) Six-coil structure.
Figure 4. Structures of coupling coils with relay coils: (a) Three-coil structure; (b) Four-coil structure; (c) Six-coil structure.
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Figure 5. Special structures of coupling coils: (a) Multidirectional coupling coils; (b) Hexagonal coupling coils; (c) coil structure with eight solenoid coils and one regular octagon coil.
Figure 5. Special structures of coupling coils: (a) Multidirectional coupling coils; (b) Hexagonal coupling coils; (c) coil structure with eight solenoid coils and one regular octagon coil.
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Figure 6. IWO based control circuit diagram of the wireless charging system for MPPT.
Figure 6. IWO based control circuit diagram of the wireless charging system for MPPT.
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Figure 7. Circuit diagrams of efficiency optimization control strategy: (a) P&O; (b) PSO; (c) MPC.
Figure 7. Circuit diagrams of efficiency optimization control strategy: (a) P&O; (b) PSO; (c) MPC.
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Figure 8. Circuit diagrams of the parameter tracking strategy: (a) PI; (b) MGA.
Figure 8. Circuit diagrams of the parameter tracking strategy: (a) PI; (b) MGA.
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Table 1. The performance of IAs applied to the compensation topology.
Table 1. The performance of IAs applied to the compensation topology.
Topology
Type		ReferenceAlgorithmOptimization
Target		Optimization
Effect		Real-Time
Performance
Basic compensation topology[32]VSPODetuning caused by capacitor parameter driftHigh accuracy, low complexityFast response
[33]ACOEnhance the tracking parameter performance Ensure the robust stabilityFast response
[34]DLNODetermine the capacitance valuesImprove the system efficiencySingle optimization
[35]HMSelect optimal frequencyImprove the output powerFast response
High-order compensation topology[39,40]GAFind the optimal parameter combinationAttain the desired output powerSingle optimization
[41]CSSOptimize the system transmission efficiencyImprove system efficiencySingle optimization
[42]Bayesian methodCapacitance parameter optimizationImprove system efficiencySingle optimization
[43]GA and nonlinear programmingReduce voltage gain difference under mutual inductance changeReduce system output fluctuationSingle optimization
[44]CAPSOOptimize the wireless charging system of EVsOptimize the output power, efficiency, and system costSingle optimization
Table 2. The performance of IAs applied to the control strategy.
Table 2. The performance of IAs applied to the control strategy.
ObjectiveReferenceAlgorithmEffectReal-Time
Performance
Power Controlling[71]PSOEnhances the output power under the coil misalignment and the load variabilityFast response
[74]IWOMPPTSecond-level response
Efficiency Optimizing[75]P&ORealize maximum efficiency transferSecond-level response
[76]PSOMulti-level coordinated control efficiency optimizationFast response
[77]MPCImprove the system efficiencySecond-level response
Parameter Tracking[78]WOComplete the PI parameter tuning of the frequency tracking controllerMicrosecond-level response
[79]MGARealize CV output in the presence of perturbationFast response
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Wei, S.; Xu, F.; Yuan, D.; Chen, K.; Liu, B.; Li, J. Review on the Applications of Intelligent Algorithm in Wireless Charging System for Electric Vehicles. Energies 2025, 18, 592. https://doi.org/10.3390/en18030592

AMA Style

Wei S, Xu F, Yuan D, Chen K, Liu B, Li J. Review on the Applications of Intelligent Algorithm in Wireless Charging System for Electric Vehicles. Energies. 2025; 18(3):592. https://doi.org/10.3390/en18030592

Chicago/Turabian Style

Wei, Shuguang, Feifan Xu, Dong Yuan, Kewei Chen, Bin Liu, and Jiaqi Li. 2025. "Review on the Applications of Intelligent Algorithm in Wireless Charging System for Electric Vehicles" Energies 18, no. 3: 592. https://doi.org/10.3390/en18030592

APA Style

Wei, S., Xu, F., Yuan, D., Chen, K., Liu, B., & Li, J. (2025). Review on the Applications of Intelligent Algorithm in Wireless Charging System for Electric Vehicles. Energies, 18(3), 592. https://doi.org/10.3390/en18030592

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