A wireless power transfer system and an optimization method for determining its coil parameters

Abstract

The application provides a wireless power transmission system and an optimization method for determining coil parameters thereof, and relates to the technical field of wireless power transmission, wherein the system comprises a transmitting coil and a receiving coil structure, the receiving coil structure comprises a main receiving coil defining a central area and an auxiliary receiving coil arranged in the central area of the main receiving coil, and the magnetic field direction of the auxiliary receiving coil is the same as that of the main receiving coil; the coil parameters of the auxiliary receiving coil are determined by a set chaotic quasi-inverse artificial honeypot algorithm. Through the application, the magnetic field superposition potential of the large DD main coil and the nested small DD auxiliary coil is fully tapped, the central magnetic field blind area is accurately compensated, the hardware cost is not greatly increased, and the receiving side is uniformly subjected to the magnetic field.

Description

Technical Field

[0001] This invention relates to the field of wireless power transmission technology, and more particularly to a wireless power transmission system and an optimization method for determining its coil parameters. Background Technology

[0002] With the widespread adoption of wireless charging technology in various scenarios, the problem of transmission performance degradation caused by receiver coil misalignment is becoming increasingly prominent. In practical use, due to the difficulty in achieving precise coil alignment, lateral or longitudinal misalignment often leads to a sharp drop in the coupling coefficient, resulting in phenomena such as drastic fluctuations in output voltage, significant reduction in transmission efficiency, and even charging interruption, affecting the stability of power supply.

[0003] Traditional magnetically coupled wireless power transfer systems, such as those using DD coils, while widely used, suffer from inherent structural defects. Specifically, traditional DD coils exhibit a significant magnetic field cancellation dead zone in their central region. When the receiving coil shifts relative to the transmitting coil, particularly towards the center, its power pickup capability drops sharply, leading to a significant output voltage slump. While existing technologies attempt to address this issue by improving coil structure or compensating topologies, they often suffer from limitations such as unidirectional anti-shifting, inability to fundamentally improve magnetic field distribution, or increased system complexity and cost. Therefore, there is an urgent need for a solution that can fundamentally resolve the performance degradation problem caused by coil shifting. Summary of the Invention

[0004] This invention provides a wireless power transmission system and an optimization method for determining its coil parameters, in order to solve the defect in existing wireless power transmission systems where the output voltage and transmission efficiency drop sharply when the coil is deflected due to the presence of a magnetic field dead zone at the center of the traditional coil structure (such as a DD coil).

[0005] In a first aspect, the present invention provides a wireless power transmission system, comprising: Transmitting coil; and The receiving coil structure includes: A main receiving coil, the main receiving coil defining a central region; and An auxiliary receiving coil is disposed in the central region of the main receiving coil, and the magnetic field direction of the auxiliary receiving coil is the same as that of the main receiving coil. The coil parameters of the auxiliary receiving coil are optimized and determined using a set chaotic quasi-inverse artificial honey badger algorithm.

[0006] According to the wireless power transmission system provided by the present invention, the transmitting coil, the main receiving coil and the auxiliary receiving coil are all DD coils; The main receiving coil is connected in series with the auxiliary receiving coil.

[0007] According to a wireless power transmission system provided by the present invention, the coil parameters of the auxiliary receiving coil are optimized and determined by a set chaotic quasi-inverse artificial honey badger algorithm, including: Set optimization goals; Based on the optimization objective, the chaotic quasi-inverse artificial honey badger algorithm is invoked to iteratively optimize the coil parameters of the auxiliary receiving coil until the termination condition is met, thus determining the final coil parameters.

[0008] According to a wireless power transmission system provided by the present invention, the optimization objective is to maximize a comprehensive evaluation function, which is a weighted combination of output voltage fluctuation rate and system transmission efficiency.

[0009] According to the wireless power transmission system provided by the present invention, the honey badger algorithm of chaotic quasi-inverse artificial intelligence is invoked based on the optimization objective to iteratively optimize the coil parameters of the auxiliary receiving coil until a termination condition is met, including: The population position is initialized using a Tent chaotic mapping; the population position corresponds to the candidate values ​​of the coil parameters of the auxiliary receiving coil. Calculate the fitness function value of the honey badger algorithm for the current position in the chaotic quasi-inverse artificial model; The coil parameters of the auxiliary receiving coil are iteratively optimized to determine the current optimal position; A new solution is generated at the current optimal position using a quasi-inverse mirror learning strategy, and the new solution is compared with the original optimal solution for selection. An adaptive chaotic perturbation is applied to the population to force population diffusion, and the optimal position of the population is updated. The optimal coil parameters of the auxiliary receiving coil are then output.

[0010] According to a wireless power transmission system provided by the present invention, the fitness function includes an evaluation term based on the optimization objective and a penalty term for system operation constraints.

[0011] According to a wireless power transmission system provided by the present invention, a linear attenuation factor is introduced, and the iterative optimization process is divided into an initial iteration and a later iteration based on the linear attenuation factor. In the early stages of the iteration, a global search is performed; In the later stages of iteration, a transition is made from global search to local fine-grained search.

[0012] According to a wireless power transmission system provided by the present invention, the coil parameters of the auxiliary receiving coil are iteratively optimized to determine the current optimal position, including: In the early stages of the iteration, a global large-scale search is performed to traverse the potential optimal regions of the parameter space of the wireless power transmission system. In the later stages of the iteration, the potential optimal region is fine-tuned to determine the current optimal position.

[0013] In a second aspect, the present invention also provides an optimization method for determining coil parameters of a wireless power transmission system, characterized in that determining the coil parameters of an auxiliary receiving coil in the wireless power transmission system described in the first aspect includes: Set optimization goals; Based on the optimization objective, the chaotic quasi-inverse artificial honey badger algorithm is invoked to iteratively optimize the coil parameters of the auxiliary receiving coil until the termination condition is met, thus determining the final coil parameters.

[0014] According to the present invention, an optimization method for determining coil parameters of a wireless power transmission system is provided, which iteratively optimizes the coil parameters of the auxiliary receiving coil by invoking the chaotic quasi-inverse artificial honey badger algorithm based on the optimization objective until a termination condition is met, including: The population position is initialized using a Tent chaotic mapping; the population position corresponds to the candidate values ​​of the coil parameters of the auxiliary receiving coil. Calculate the fitness function value of the honey badger algorithm for the current position in the chaotic quasi-inverse artificial model; The coil parameters of the auxiliary receiving coil are iteratively optimized to determine the current optimal position; A new solution is generated at the current optimal position using a quasi-inverse mirror learning strategy, and the new solution is compared with the original optimal solution for selection. An adaptive chaotic perturbation is applied to the population to force population diffusion, and the optimal position of the population is updated. The optimal coil parameters of the auxiliary receiving coil are then output.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention designs a full-domain magnetic field collaborative compensation structure for main and auxiliary coils, which fully explores the magnetic field superposition potential of the large DD main coil and the nested small DD auxiliary coil, accurately compensates for the central magnetic field cancellation blind zone, and achieves uniform magnetization of the entire receiving area without significantly increasing hardware costs.

[0016] 2. Construct an integrated anti-offset system that combines DBF coil structure and LCC-S compensation topology, taking into account the triple functions of power transmission, magnetic field compensation and resonance stability, and can maintain stable output under conditions such as coil offset and misalignment.

[0017] 3. The honey badger optimization algorithm for chaotic quasi-inverse artificial intelligence integrates four strategies: Tent chaotic initialization, linear decay factor, quasi-inverse mirror learning, and adaptive lemming jump, which greatly improves the efficiency and accuracy of parameter optimization and effectively avoids the pain points of traditional algorithms that are prone to getting trapped in local optima, slow convergence, and unstable solutions.

[0018] 4. The coil parameters and algorithm strategy can adapt to different power, different offset ranges and different application scenarios, reducing the dependence on accurate modeling and positioning control. It is robust, has a simple structure and is easy to manufacture, and has outstanding engineering practicality.

[0019] 5. Dynamically balances transmission efficiency, output voltage fluctuation rate, and anti-offset capability. Through global magnetic field homogenization and parameter co-optimization, it completely solves the problem of voltage drop during offset, significantly improves the stability and compatibility of wireless charging, and is suitable for industrial applications in multiple scenarios. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0021] Figure 1 This is a diagram of the DBF coil structure provided by the present invention; Figure 2 This is a schematic diagram of the LCC-S wireless charging system circuit provided by the present invention; Figure 3 This is a diagram showing the magnetic flux density effect of the DBF coil at an X-axis offset of 0 in an embodiment of the present invention. Figure 4 This is a diagram showing the magnetic flux density effect of the DBF coil at a 100mm offset along the X-axis in an embodiment of the present invention. Figure 5 This is a diagram showing the magnetic flux density effect of the DBF coil at a 300mm offset along the X-axis in an embodiment of the present invention. Figure 6 This is a diagram showing the change in mutual inductance between the DD coil and the DBF coil in an embodiment of the present invention; Figure 7 This is a flowchart of the iterative optimization process of the honey badger algorithm for chaotic quasi-inverse artificial intelligence in this embodiment of the invention; Figure 8 This is a comparison diagram of the coil X-axis output fluctuation before and after optimization using this optimization method in an embodiment of the present invention; Figure 9 This is a comparison diagram of the coil Y-axis output fluctuation before and after optimization using this optimization method in an embodiment of the present invention; Figure 10A comparison diagram of coil Z-axis output fluctuation before and after optimization using this optimization method in this embodiment of the invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0023] Please see Figure 1 and Figure 2 This application provides a wireless power transfer system and related parameter optimization method, aiming to solve the core technical problems of degraded transmission performance, particularly sudden drop in output voltage and unstable transmission efficiency, caused by positional misalignment between the receiving and transmitting coils in existing wireless power transfer (WPT) technologies. This technical solution combines a novel coil structure with an efficient parameter optimization algorithm to achieve a comprehensive performance improvement in terms of high offset tolerance, high transmission efficiency, and high output voltage stability.

[0024] The LCC-S type wireless power transfer system provided in this application embodiment adopts a DBF coil structure, which includes a transmitting coil. It also features a special receiving coil structure. This DBF coil structure employs an LCC-S type wireless power transfer system topology. (See also...) Figure 2 The system circuit consists of a DC voltage source U, a full-bridge inverter circuit composed of switching transistors S1-S4, a transmitter-side LCC compensation network (including primary-side compensation inductor Lf, primary-side compensation capacitors CP1 and CP2), a transmitter coil L1, a receiver coil structure (including a main receiver coil L2 and an auxiliary receiver coil L3), a receiver-side series compensation network (secondary-side series compensation capacitor CS), and a load resistor RL. The transmitter is a DD-type transmitter coil L1, the large DD coil at the receiver serves as the main receiver coil L2, and two sets of small DD coils nested inside the main coil form the auxiliary receiver coil L3. By optimizing the coil parameters such as the wire diameter and number of turns of the auxiliary receiver coil L3, the overall system performance can be optimized.

[0025] The transmitting coil The primary excitation coil of the wireless power transmission system, powered by an inverter, generates an alternating magnetic field. The receiving coil is responsible for sensing the magnetic field and receiving energy. The design aims to fundamentally improve the system's robustness to positional offsets by optimizing the receiver structure, overcoming the performance degradation caused by a blind zone in the central magnetic field of traditional coils in background technologies. This collaborative design of structure and algorithm resolves the contradiction between offset resistance and transmission efficiency in traditional solutions, resulting in a significant improvement in overall system performance.

[0026] Specifically, the receiving coil structure includes a main receiving coil. and auxiliary receiving coil In terms of physical layout, the main receiving coil defines a central region, within which the auxiliary receiving coil is located. This nested design of a "large coil within a small coil" is one of the core structural innovations of this solution. The main receiving coil is responsible for receiving most of the magnetic field energy from the transmitting DD coil, serving as the primary source of the system's output power and undertaking over 90% of the power transmission. The design principle lies in the fact that while traditional large-size coils (such as DD coils) can effectively couple magnetic fields in most areas, a weak magnetic field region or "dead zone" often exists near their geometric center, where magnetic fields cancel each other out. When the receiving device happens to be offset into this region, the coupling coefficient drops sharply, leading to a precipitous drop in output voltage and power. This solution, by adding an auxiliary receiving coil in the central region, aims to precisely compensate for the magnetic field in this dead zone. After superimposing the magnetic field of the main receiving coil, it achieves a uniform magnetic field distribution across the entire receiving area, completely solving the problem of voltage drop when the center is offset.

[0027] To ensure effective compensation rather than further magnetic field cancellation, the magnetic field direction of the auxiliary receiving coil is set to be the same as that of the main receiving coil. This means that when the magnetic field generated by the transmitting coil passes through the receiving structure, the magnetic fields generated by the currents induced in the main and auxiliary receiving coils are superimposed in the same direction. Through this coordinated compensation, the originally concave central magnetic field is "filled in," making the magnetic field distribution on the effective receiving plane of the entire receiving coil structure more uniform. In this way, regardless of any lateral or longitudinal offset of the receiving coil structure, a sufficiently strong magnetic field is always coupled, thereby maintaining the stability of the total mutual inductance of the system, avoiding drastic fluctuations in the output voltage, and achieving good anti-offset performance.

[0028] The key innovation of this embodiment lies in nesting a set of small DD coils as auxiliary receiving coils within the central region of the main receiving coil on the receiving side. Traditional large DD coils, due to their structural characteristics, have a blind zone in the central region where magnetic fields cancel each other out. The small DD auxiliary receiving coils in this embodiment are located precisely within this blind zone. The supplementary magnetic field they generate is superimposed in the same direction as the magnetic field of the large DD coil, achieving a uniform distribution of the magnetic field throughout the entire effective area of ​​the receiving side. Figures 3 to 5 The simulation results of the magnetic flux density shown indicate that, regardless of whether it is directly opposite ( Figure 3 ), medium offset ( Figure 4 ) or a large-scale offset ( Figure 5 In this case, the magnetic field distribution in the receiving area maintains good uniformity and intensity, thus effectively solving the problem of voltage drop caused by the offset of the central region. For example... Figure 6 As shown, Figure 6 This is a diagram showing the change in mutual inductance between the DD coil and the DBF coil in an embodiment of the present invention. Compared with the traditional DD coil, the DBF coil structure of the present invention exhibits a very smooth change in total mutual inductance over a large offset range, without any drastic drop, which fundamentally ensures the stability of the output voltage.

[0029] To optimize the compensation effect of the auxiliary receiving coil, rather than simply adding another coil, its specific coil parameters, such as the number of turns, size, and wire diameter, need to be precisely designed. Arbitrarily set parameters may lead to undercompensation or overcompensation, or even introduce new losses and reduce overall efficiency. Therefore, this scheme introduces an optimization algorithm: a chaotic quasi-inverse artificial honey badger algorithm, which optimizes and determines the coil parameters of the auxiliary receiving coil. This algorithm can perform global optimization in a multi-dimensional parameter space to find a set of optimal parameter combinations that maximize the overall system performance (such as efficiency and voltage stability). In this way, structural design and intelligent algorithms are closely integrated, ensuring that the auxiliary coil can minimize its own losses while maximizing the overall system's anti-migration capability and stability.

[0030] In a preferred embodiment, to obtain good magnetic field distribution characteristics and anti-migration capability, the transmitting coil, main receiving coil, and auxiliary receiving coil all adopt a DD coil configuration. DD coils inherently possess the advantage of relatively uniform magnetic field distribution in a certain direction; applying them to both the transmitting and receiving sides lays a solid foundation for the system's anti-migration capability. Furthermore, the main receiving coil and auxiliary receiving coil are connected in series. This design simplifies the circuit topology on the receiving side, ensuring that the induced current flowing through the main and auxiliary coils is the same, facilitating unified resonance compensation and energy management. It also simplifies the mathematical model of the system's total mutual inductance and output voltage, providing convenience for subsequent parameter optimization calculations.

[0031] Furthermore, in a preferred embodiment, the process of optimizing the coil parameters of the auxiliary receiving coil using a set chaotic quasi-inverse artificial honey badger algorithm first includes setting an optimization objective. This is a crucial initial step, providing clear guidance for the algorithm's search direction. The optimization objective defines the performance characteristics that "good" coil parameters should possess. Subsequently, based on the set optimization objective, the chaotic quasi-inverse artificial honey badger algorithm is invoked to iteratively optimize the coil parameters of the auxiliary receiving coil until a preset termination condition is met (e.g., reaching the maximum number of iterations or convergence of solution accuracy), ultimately determining and outputting a set of optimal coil parameters. This iterative optimization process based on a clear objective ensures the scientific and optimal nature of parameter selection, avoiding the drawbacks of relying on experience and trial and error in traditional design methods.

[0032] In another preferred embodiment, the optimization objective is specifically defined as maximizing a comprehensive evaluation function. This comprehensive evaluation function is a weighted combination of output voltage fluctuation rate and system transmission efficiency. In wireless power transfer applications, the stability of the output voltage is crucial for the safe operation of downstream electrical equipment, while transmission efficiency directly relates to energy utilization and system heat generation. These two indicators are often mutually restrictive; excessive pursuit of one may compromise the other. By weighting and combining them, this scheme aims to find an optimal balance between the two, ensuring that the final determined coil parameters allow the system to maintain high transmission efficiency while also exhibiting extremely low output voltage fluctuation, thereby achieving optimal overall performance.

[0033] For example, the formula for system transmission efficiency is as follows:

[0034] in, For system transmission efficiency, The internal resistance of the primary coil is... The internal resistance of the main receiving coil, To assist the internal resistance of the receiving coil, For load resistance, The resonant angular frequency, This is the total mutual inductance of the system.

[0035] Specifically, the main receiving coil With auxiliary receiving coil For series structures located at the same level, the current relationship is as follows:

[0036] in, The excitation current of the main receiving coil, This is to provide the excitation current for the auxiliary receiving coil.

[0037] The self-inductance of the transmitting coil was calculated using COMSOL finite element simulation. Self-inductance of the main receiving coil Auxiliary coil self-inductance for:

[0038]

[0039]

[0040] in, For the self-inductance of the transmitting coil, The total magnetic flux through the transmitting coil, The number of turns of the transmitting coil. For the cross-sectional area region of the transmitting coil, The magnetic flux density of the transmitting coil, It is an area infinitesimal vector. This is the excitation current for the transmitting coil; The self-inductance of the main receiving coil, The total magnetic flux through the main receiving coil, The number of turns of the main receiving coil. The cross-sectional area of ​​the main receiving coil. The magnetic flux density of the main receiving coil, The excitation current for the main receiving coil; For the self-inductance of the auxiliary receiving coil on the receiving side, To the total magnetic flux through the auxiliary receiving coil, To increase the number of turns of the auxiliary receiving coil, To assist in the cross-sectional area domain of the receiving coil, To assist the magnetic flux density of the receiving coil, This is to provide the excitation current for the auxiliary receiving coil.

[0041] The formula for calculating the total mutual inductance of a wireless power transfer system is as follows:

[0042]

[0043]

[0044]

[0045] Main receiving coil With auxiliary receiving coil Mutual decoupling, The total inductance of the system is simplified to

[0046] in, For the total mutual inductance of the system, The mutual inductance between the transmitting coil and the main receiving coil, The mutual inductance between the transmitting coil and the auxiliary receiving coil, The mutual inductance between the main receiving coil and the auxiliary receiving coil The mutual magnetic flux generated by the transmitting coil on the main receiving coil, Excited by the current of the transmitting coil. The number of turns of the main receiving coil. The area of ​​the main receiving coil. This represents the magnetic flux density when only the transmitting coil is energized. It is an area infinitesimal vector. The mutual magnetic flux generated by the transmitting coil on the auxiliary receiving coil, To increase the number of turns of the auxiliary receiving coil, To assist in the cross-sectional area domain of the receiving coil, The mutual magnetic flux generated by the main receiving coil on the auxiliary receiving coil This represents the magnetic flux density when only the main receiving coil is energized. The main receiving coil is excited by current.

[0047] The resonance condition of a wireless power transfer system is as follows: The transmitter-side LCC resonance is:

[0048]

[0049] The series resonance of the receiver side S is:

[0050] in, The resonant angular frequency ( , (system operating frequency) The self-inductance of the main receiving coil, To assist the self-inductance of the receiving coil, For primary-side compensation inductance, , All are primary-side compensation capacitors. It is a secondary-side series compensation capacitor.

[0051] The receiver-side KVL equation is:

[0052] After resonance, the reactance is 0, and the equation simplifies to:

[0053] in, For the total mutual inductance of the system, Excited by the current of the transmitting coil. The resonant angular frequency, The self-inductance of the main receiving coil, To assist the self-inductance of the receiving coil, It is a secondary-side series compensation capacitor. For the receiving end load resistor, The main receiving coil is excited by current.

[0054] The output voltage is:

[0055] in, To output DC voltage for the system, The resonant angular frequency, For the total mutual inductance of the system, The current is used to excite the transmitting coil.

[0056] Output power is:

[0057] in, P 0 indicates output power. To output DC voltage for the system, The resonant angular frequency, For the total mutual inductance of the system, Excited by the current of the transmitting coil. This is the load resistor at the receiving end.

[0058] The optimization objective formula for output voltage fluctuation rate is as follows:

[0059] in, This indicates the output voltage fluctuation rate. This is the reference voltage when the coil is facing upwards. , These represent the maximum and minimum output voltages within the offset range.

[0060] In one alternative implementation, the specific process of iteratively optimizing by invoking the honey badger algorithm of chaotic quasi-inverse artificial algorithm based on the optimization objective includes several steps. Figure 7 This is a flowchart of the iterative optimization process of the honey badger algorithm for chaotic quasi-inverse artificial algorithms in this invention embodiment, as follows: Figure 7As shown, firstly, the Tent chaotic map is used to initialize the population positions, where each "position" in the population corresponds to a set of candidate values ​​for the parameters of the auxiliary receiving coils. Compared to traditional random initialization, the Tent chaotic map has better ergodicity and uniformity, enabling the initial population to cover the entire search space more broadly, laying a good foundation for subsequent global optimization and preventing the algorithm from getting trapped in local optima too early. After initialization, the fitness function values ​​of the chaotic quasi-inverse artificial honey badger algorithm corresponding to each current population position need to be calculated to evaluate the quality of each candidate solution.

[0061] Next, the algorithm iteratively optimizes the coil parameters of the auxiliary receiving coil to determine the current optimal position. This process is the core of the algorithm, simulating the foraging behavior of a honey badger. Then, to enhance the algorithm's ability to escape local optima, a quasi-inverse mirror learning strategy is used to generate a new solution in the "reverse" region of the currently found optimal position. This new solution is then compared with the original optimal solution, and the better one is retained. This strategy greatly broadens the search range and effectively prevents the algorithm from stagnating near a local optimum. Finally, to maintain population diversity and prevent all individuals from gathering too quickly at the same point, the algorithm applies adaptive chaotic perturbation to the population, forcing a certain degree of diffusion, and updates the optimal position of the population accordingly. When the termination condition is met, the algorithm outputs the currently found optimal coil parameters.

[0062] Furthermore, in a preferred embodiment, the fitness function is designed to reflect a deep understanding of practical engineering requirements. It includes not only an evaluation term based on the optimization objectives (i.e., voltage fluctuation rate and transmission efficiency) but also a penalty term for system operating constraints. For example, the actual output power cannot be lower than the rated value, and the coil current cannot exceed its safe upper limit. When a candidate solution violates these constraints, the penalty term significantly reduces its fitness value, thus naturally eliminating it during the optimization process. In this way, it is ensured that the optimal solution found by the algorithm is not only superior in theoretical performance but also feasible and safe in practical engineering applications.

[0063] In another preferred embodiment, to better balance the algorithm's global search and local exploration capabilities, a linear decay factor is introduced, and the iterative optimization process is dynamically divided into an early iteration and a late iteration based on this factor. In the early iteration, the algorithm needs to extensively explore the entire parameter space to find potential optimal regions, thus performing a global search. In the late iteration, when the algorithm has likely converged to the vicinity of the optimal region, a smooth transition from global search to refined local search is required to improve the accuracy of the solution. This dynamically adjusted search strategy allows the algorithm to have different focuses at different stages, significantly improving the efficiency and accuracy of optimization.

[0064] In one alternative implementation, the specific strategy for iteratively optimizing the coil parameters of the auxiliary receiving coil to determine the current optimal position is as follows: In the initial stage of iteration, a global large-scale search is performed, traversing all potential optimal regions in the parameter space of the wireless power transfer system with a large step size, avoiding overlooking any "corners" where a better solution might exist. In the later stage of iteration, within the already locked potential optimal regions, a small-scale fine-tuning and refined search is performed with a smaller step size, thereby accurately determining the current optimal position. This coarse-to-fine search strategy balances the breadth and depth of the search.

[0065] For example, the chaotic quasi-inverse artificial honey badger algorithm initializes the honey badger position, which is the optimization variable. The initialization formula for the position is:

[0066] in, For the first The position vector of each individual For the first dimension of optimization variables, For the second dimension of optimization variables, For the first The number of turns of the auxiliary receiving coil corresponding to each individual For the first The side length of the auxiliary receiving coil corresponding to each individual.

[0067] The chaotic quasi-inverse artificial honey badger algorithm uses chaotic sequences instead of pure random numbers to generate the initial population position of the algorithm (corresponding to the auxiliary receiving coil parameters). , (Initial candidate values), the position formula is:

[0068] By combining the Tent chaotic map, a chaotic sequence in the 0-1 interval is generated, providing a chaotic source for initialization, perturbation, and jumps. The formula is as follows:

[0069] in, For the first Individual, the first The initial position of the dimension. For the first Lower bound of dimensional variable For the first Upper limit of dimensional variables, It is a chaotic number (0-1). The individual number is (1 - population size N). For dimension number, The current chaos value, This is the next chaotic value.

[0070] The honey badger algorithm for chaotic quasi-inverse artificial algorithms introduces a linear decay factor in the initial iteration. As the step size increases, the algorithm performs a full global search; in the later stages of iteration... As the step size approaches 0, the transition from global search to local fine-grained search is achieved. The formula is:

[0071] in, As the attenuation factor, This represents the current iteration number. This represents the maximum number of iterations (typically 100-200).

[0072] The honey badger algorithm for chaotic quasi-inverse artificial algorithms introduces a density factor, which decays exponentially, allowing the search step size to decrease rapidly with the number of iterations. This enables large-step global exploration in the early stages and small-step fine-tuning in the later stages. The formula is as follows:

[0073] in, Density factor For natural index, This represents the current iteration number. This represents the maximum number of iterations.

[0074] The chaotic quasi-inverse artificial honey badger algorithm simulates the digging and foraging behavior of honey badgers during the digging phase, performing a global large-scale search and traversing the potential optimal regions in the WPT parameter space. The formula is as follows:

[0075] In the honeybee stage, the honey badger is simulated to precisely collect nectar near the optimal solution, achieving localized development through small-scale fine-tuning. The formula is as follows:

[0076] in, Current position For the new location, This is the current globally optimal position (prey). As the attenuation factor, It is a direction factor (1 or -1). The foraging ability coefficient (fixed at 0.5) For the honey badger's perception strength, , , , All are random numbers between 0 and 1.

[0077] The Chaotic Quasi-Inverse Artificial Intelligence Honey Badger Algorithm generates a new solution in the symmetrical region of the current optimal solution through "quasi-inverse mirroring." If the new solution is better, it replaces the original optimal solution, fundamentally avoiding the algorithm from getting stuck in local optima. The CQALA quasi-inverse learning formula is:

[0078] in, The new optimal candidate solution is generated in the quasi-inverse direction. The quasi-inverse coefficient is 0.8-1.2, with 1 recommended. The upper limit of the variable, The lower bound of the variable. This is the current optimal solution.

[0079] The chaotic quasi-inverse artificial honey badger algorithm introduces the CQALA adaptive lemming jumping mechanism and chaotic adaptive perturbation to simulate the group jumping and migration behavior of lemmings. It applies adaptive chaotic perturbation to the entire population, forcing population diffusion. The formula is as follows:

[0080] The above formula introduces a chaotic adaptive perturbation, and the formula is adjusted to:

[0081]

[0082] in, Current position For chaotic disturbance, For the range of variables, The jump attenuation coefficient is... This represents the disturbance coefficient.

[0083] The chaotic quasi-inverse artificial honey badger algorithm measures population diversity by the deviation of an individual's location from the median: The larger the species, the more dispersed the population. The smaller the population, the more concentrated it becomes, requiring the triggering of diversity protection mechanisms. This necessitates the use of a population diversity formula, which is:

[0084] in, As a diversity index, N For population size, Current position The middle number is the position.

[0085] The fitness function of the chaotic quasi-inverse artificial honey badger algorithm is:

[0086]

[0087] in, For fitness value, Current position For voltage fluctuation rate, For transmission efficiency, As a penalty item, , , All are weighting coefficients. Rated output power , This represents the actual output power. This is the excitation current for the transmitting coil. This is the maximum allowable current for the transmitting coil. Maximum allowable voltage fluctuation , , All are penalty coefficients (100-1000).

[0088] The present invention also provides an optimization method for determining coil parameters of a wireless power transmission system, for the coil parameters of an auxiliary receiving coil in the aforementioned wireless power transmission system, comprising: setting an optimization target; and iteratively optimizing the coil parameters of the auxiliary receiving coil by calling a chaotic quasi-inverse artificial honey badger algorithm based on the optimization target until a termination condition is met, thereby determining the final coil parameters.

[0089] Specifically, based on the optimization objective, the honey badger algorithm of chaotic quasi-inverse artificial intelligence is invoked to iteratively optimize the coil parameters of the auxiliary receiving coil until the termination condition is met. This includes: initializing the population position using the Tent chaotic map; generating candidate values ​​for the coil parameters of the auxiliary receiving coil corresponding to the population position; calculating the fitness function value of the honey badger algorithm of chaotic quasi-inverse artificial intelligence at the current position; iteratively optimizing the coil parameters of the auxiliary receiving coil to determine the current optimal position; generating a new solution at the current optimal position through a quasi-inverse mirror learning strategy, and comparing the new solution with the original optimal solution for selection; applying adaptive chaotic perturbation to the population to force population diffusion, updating the optimal position of the population, and outputting the optimal coil parameters of the auxiliary receiving coil.

[0090] To systematically address the challenges of voltage drop, transmission efficiency fluctuations, and insufficient anti-misalignment capability in wireless charging systems when the coil is misaligned, this application provides a specific DBF-type wireless power transfer coil structure. Please refer to... Figure 1 This structure uses a DD coil on the transmitting side as the primary excitation coil of the system. This transmitting coil is powered by an inverter to generate an alternating magnetic field. On the receiving side, a large-size DD coil is built as the main receiving coil. Its main function is to receive most of the magnetic field energy from the transmitting DD coil, and it is the main source of the system's output power, typically undertaking more than 90% of the power transmission.

[0091] The key innovation of this embodiment lies in nesting a set of small DD coils as auxiliary coils within the central region of the main receiving coil on the receiving side. Traditional large DD coils, due to their structural characteristics, have a blind zone in the central region where magnetic fields cancel each other out. The small DD auxiliary coils in this embodiment are located precisely within this blind zone. Their supplementary magnetic field, when superimposed in the same direction as the magnetic field of the large DD coil, achieves a uniform distribution of the magnetic field throughout the entire effective area of ​​the receiving side.

[0092] In this embodiment, the DBF coil structure adopts an LCC-S type wireless power transfer system topology. Please refer to [link / reference]. Figure 2 The system circuit consists of a DC voltage source U, a full-bridge inverter circuit composed of switching transistors S1-S4, a transmitter-side LCC compensation network (including primary-side compensation inductor Lf, primary-side compensation capacitors CP1 and CP2), a transmitter coil L1, a receiver coil structure (including a main receiver coil L2 and an auxiliary receiver coil L3), a receiver-side series compensation network (secondary-side series compensation capacitor CS), and a load resistor RL. The transmitter is a DD-type transmitter coil L1, the large DD coil at the receiver serves as the main receiver coil L2, and two sets of small DD coils nested inside the main coil form the auxiliary receiver coil L3. By optimizing the coil parameters such as the wire diameter and number of turns of the auxiliary receiver coil L3, the overall system performance can be optimized.

[0093] like Figure 8-10 As shown, Figure 8 This is a comparison chart of the coil X-axis output fluctuation before and after optimization using this optimization method in an embodiment of the present invention. Figure 9 This is a comparison chart of the coil Y-axis output fluctuation before and after optimization using this optimization method in an embodiment of the present invention. Figure 10 The figure shows a comparison of the coil Z-axis output fluctuation before and after optimization using the proposed optimization method in this embodiment of the invention. As can be seen from the figure, after adopting the coil parameter optimization method proposed in this invention, the output voltage fluctuation of the DBF coil on the X, Y, and Z axes is significantly reduced, and the output signal is more stable.

[0094] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A wireless power transmission system, characterized in that, include: Transmitting coil; as well as The receiving coil structure includes: A main receiving coil, the main receiving coil defining a central region; and An auxiliary receiving coil is disposed in the central region of the main receiving coil, and the magnetic field direction of the auxiliary receiving coil is the same as that of the main receiving coil. The coil parameters of the auxiliary receiving coil are optimized and determined by a set chaotic quasi-inverse artificial honey badger algorithm; The coil parameters of the auxiliary receiving coil are optimized and determined using a set chaotic quasi-inverse artificial honey badger algorithm, including: Set optimization goals; Based on the optimization objective, the chaotic quasi-inverse artificial honey badger algorithm is invoked to iteratively optimize the coil parameters of the auxiliary receiving coil until the termination condition is met, and the final coil parameters are determined. Based on the optimization objective, the chaotic quasi-inverse artificial honey badger algorithm is invoked to iteratively optimize the coil parameters of the auxiliary receiving coil until the termination condition is met, including: The population position is initialized using a Tent chaotic mapping; the population position corresponds to the candidate values ​​of the coil parameters of the auxiliary receiving coil. Calculate the fitness function value of the honey badger algorithm for the current position in the chaotic quasi-inverse artificial model; The coil parameters of the auxiliary receiving coil are iteratively optimized to determine the current optimal position; A new solution is generated at the current optimal position using a quasi-inverse mirror learning strategy, and the new solution is compared with the original optimal solution for selection. An adaptive chaotic perturbation is applied to the population to force population diffusion, and the optimal position of the population is updated. The optimal coil parameters of the auxiliary receiving coil are then output.

2. The wireless power transmission system according to claim 1, characterized in that, The transmitting coil, the main receiving coil, and the auxiliary receiving coil are all DD coils; The main receiving coil is connected in series with the auxiliary receiving coil.

3. The wireless power transmission system according to claim 1, characterized in that, The optimization objective is to maximize the comprehensive evaluation function, which is a weighted combination of output voltage fluctuation rate and system transmission efficiency.

4. The wireless power transmission system according to claim 1, characterized in that, The fitness function includes an evaluation term based on the optimization objective and a penalty term for system operation constraints.

5. The wireless power transmission system according to claim 1, characterized in that, A linear decay factor is introduced, and the iterative optimization process is divided into an initial iteration and a later iteration based on the linear decay factor. In the early stages of the iteration, a global search is performed; In the later stages of iteration, a transition is made from global search to local fine-grained search.

6. The wireless power transmission system according to claim 5, characterized in that, The coil parameters of the auxiliary receiving coil are iteratively optimized to determine the current optimal position, including: In the early stages of the iteration, a global large-scale search is performed to traverse the potential optimal regions of the parameter space of the wireless power transmission system. In the later stages of the iteration, the potential optimal region is fine-tuned to determine the current optimal position.

7. An optimization method for determining coil parameters of a wireless power transmission system, characterized in that, Determining the coil parameters of the auxiliary receiving coil in the wireless power transmission system according to any one of claims 1-6 includes: Set optimization goals; Based on the optimization objective, the chaotic quasi-inverse artificial honey badger algorithm is invoked to iteratively optimize the coil parameters of the auxiliary receiving coil until the termination condition is met, thus determining the final coil parameters.

8. The optimization method for determining coil parameters of a wireless power transmission system according to claim 7, characterized in that, Based on the optimization objective, the chaotic quasi-inverse artificial honey badger algorithm is invoked to iteratively optimize the coil parameters of the auxiliary receiving coil until the termination condition is met, including: The population position is initialized using a Tent chaotic mapping; the population position corresponds to the candidate values ​​of the coil parameters of the auxiliary receiving coil. Calculate the fitness function value of the honey badger algorithm for the current position in the chaotic quasi-inverse artificial model; The coil parameters of the auxiliary receiving coil are iteratively optimized to determine the current optimal position; A new solution is generated at the current optimal position using a quasi-inverse mirror learning strategy, and the new solution is compared with the original optimal solution for selection. An adaptive chaotic perturbation is applied to the population to force population diffusion, and the optimal position of the population is updated. The optimal coil parameters of the auxiliary receiving coil are then output.

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