A wireless charger scheduling method for optimizing charging efficiency

Abstract

The application discloses a wireless charger scheduling method for optimizing charging efficiency, which comprises the following steps: collecting relevant information of wireless chargers and target chargeable devices; determining problems to be optimized according to actual demands; executing corresponding optimization algorithms according to the problems to be optimized; and starting corresponding wireless chargers according to results obtained by the corresponding optimization algorithms. The application provides a wireless charger scheduling method for optimizing charging efficiency. Different charging schemes are executed respectively when charging demands are different. Three optimization algorithms for different application scenarios are proposed, which can save a large amount of calculation time compared with common optimal algorithms, and the results are also accurate, so that the charging efficiency can be effectively increased and the charging cost can be reduced.

Description

Technical Field

[0001] This invention relates to the field of wireless rechargeable sensor network technology, specifically to a wireless charger scheduling method for optimizing charging efficiency. Background Technology

[0002] Wireless charging technology enables the transfer of electromagnetic energy to rechargeable devices wirelessly. Due to its convenience, it is widely used in various high-power products, including induction generators, robotics, electric vehicles, and kitchen appliances, in addition to low-power applications. However, the number of rechargeable devices requiring energy is sometimes large and widely distributed, while the coverage of wireless chargers is limited. Even within the coverage area of ​​a wireless charger, charging efficiency varies in different areas. Therefore, without an efficient charging efficiency optimization solution, a massive number of wireless chargers would be needed to operate all rechargeable devices, resulting in significant costs.

[0003] Currently, optimization schemes for wireless charger power efficiency are receiving increasing attention. For example, research focuses on how to improve charging throughput and efficiency by employing multiple wireless charging vehicles for cooperative charging; how to leverage the one-to-many charging capability of mobile wireless chargers to charge one charging cluster at a time; and how to schedule charging vehicles to minimize node failures in the charging network. However, current research does not fully consider diverse charging needs. Summary of the Invention

[0004] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by this invention is that the number of rechargeable devices that need to receive energy is enormous and widely distributed, while the coverage of wireless chargers is limited; even within the coverage area of ​​a wireless charger, charging efficiency varies in different areas. Consequently, there is a lack of efficient charging solutions, requiring a massive number of wireless chargers to enable all rechargeable devices to operate, resulting in significant costs.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a wireless charger scheduling method for optimizing charging efficiency, comprising:

[0008] Collect relevant information about wireless chargers and target rechargeable devices;

[0009] Identify the issues that need optimization based on actual requirements;

[0010] Execute the corresponding optimization algorithm based on the problem that needs to be optimized;

[0011] The corresponding wireless charger is activated based on the results obtained from the optimization algorithm.

[0012] As a preferred embodiment of the wireless charger scheduling method for optimizing charging efficiency described in this invention, the relevant information includes:

[0013] The set of wireless chargers in a fixed, off state, G = {g1, g2, ..., g...} l};

[0014] Wireless charger collection in power-on state

[0015] A collection of rechargeable devices

[0016] Among them, wireless charger If g i If ∈G, then g i Initially in the off state; if Then g i Initially, it is in the "on" state and can be used as a rechargeable device.

[0017] As a preferred embodiment of the wireless charger scheduling method for optimizing charging efficiency described in this invention, the actual demand includes a first demand, a second demand, and a third demand.

[0018] The first requirement is that, under the constraint of a certain number of wireless chargers that can be turned on, the user wants to maximize the cumulative utility of each rechargeable device;

[0019] The second requirement is that, under a certain constraint on the number of wireless chargers that can be turned on, users want the charging device with the lowest utility to have the highest utility.

[0020] The third requirement is that, under a certain threshold of rechargeable device utility, the user wants to minimize the number of wireless chargers that need to be turned on.

[0021] As a preferred embodiment of the wireless charger scheduling method for optimizing charging efficiency described in this invention, the problems to be optimized include:

[0022] The cumulative optimization problem corresponding to the first requirement;

[0023] The cumulative optimization problem is:

[0024]

[0025] Where S is the independent variable, representing the set of wireless chargers that are turned on after scheduling; U(S) is the optimization objective, representing the charging utility when the set of wireless chargers turned on is S. In the cumulative optimization problem, the value of U(S) is the sum of the charging power of all rechargeable devices; k is the upper limit of the number of chargers turned on, a given positive integer, indicating that the size of the set of wireless chargers turned on |S| cannot be greater than k. For rechargeable devices j When the charger assembly is opened The charging power obtained under the condition of.

[0026] As a preferred embodiment of the wireless charger scheduling method for optimizing charging efficiency described in this invention, the problem to be optimized further includes:

[0027] The second requirement corresponds to the uniform optimization problem;

[0028] The uniform optimization problem is:

[0029]

[0030] Where S is the independent variable, representing the set of wireless chargers that are turned on after scheduling; U(S) is the optimization objective, representing the charging utility when the set of wireless chargers turned on is S. In the uniform optimization problem, the value of U(S) is the minimum charging power among all rechargeable devices; k is the upper limit of the number of chargers turned on, which is a given positive integer, indicating that the size of the set of wireless chargers turned on, |S|, cannot be greater than k. For rechargeable devices j When the charger assembly is opened The charging power obtained under the condition of.

[0031] As a preferred embodiment of the wireless charger scheduling method for optimizing charging efficiency described in this invention, the problem to be optimized further includes:

[0032] The threshold optimization problem corresponding to the third requirement;

[0033] The threshold optimization problem is:

[0034]

[0035] Where S is the independent variable, representing the set of wireless chargers that are turned on after scheduling; θ j For rechargeable devices j The threshold is a given positive real number representing the threshold value for rechargeable devices. j The charging power obtained must be no less than θ j , For rechargeable devicesj When the charger assembly is opened The charging power obtained under the condition of.

[0036] As a preferred embodiment of the wireless charger scheduling method for optimizing charging efficiency described in this invention, wherein: the rechargeable device o j From wireless charger g i The method for calculating the obtained charging power is as follows:

[0037]

[0038] Among them, d(g i ,o j (This is a wireless charger g) i With rechargeable devices j The Euclidean distance between them; α i,j and β i,j It is made by charger g i and rechargeable devices j Two constants determined by the magnetic field environment and hardware parameters; D i It is any rechargeable device that can be charged from the charger g i The maximum charging distance to achieve positive power;

[0039] Rechargeable devices j The charging power obtained from all wireless chargers is:

[0040]

[0041] in, S represents the set of wireless chargers that are turned on in the schedule.

[0042] As a preferred embodiment of the wireless charger scheduling method for optimizing charging efficiency according to the present invention, the optimization algorithm includes an accumulation-plus-optimization algorithm;

[0043] The cumulative optimization algorithm includes:

[0044] R1: Enter the maximum number of chargers that can be turned on, k;

[0045] R2: Initialization

[0046] R3: Calculation

[0047] R4: Update S = S∪{g i};

[0048] R5: If |S|≤k, then jump to step R3; otherwise, proceed to step R6.

[0049] R6: Return S.

[0050] As a preferred embodiment of the wireless charger scheduling method for optimizing charging efficiency according to the present invention, the optimization algorithm further includes a uniform optimization algorithm;

[0051] The uniform optimization algorithm includes:

[0052] A1: Input relaxation coefficient The upper limit k of the number of chargers to be opened and the search precision ε, where ε is a given positive real number;

[0053] A2: Initialize p min =0,

[0054] A3: Calculation initialization

[0055] A4: Calculation Update S′=S′∪{g i},in,

[0056] A5: If If S′≠G, then proceed to step A4; otherwise, proceed to step A6.

[0057] A6: If |S'|>δk, then update p. max =p; otherwise update p min =p, S = S′;

[0058] A7: If (p max -p min If ε ≥ ε, then proceed to step A3; otherwise, proceed to step A8.

[0059] A8: Return to S.

[0060] As a preferred embodiment of the wireless charger scheduling method for optimizing charging efficiency according to the present invention, the optimization algorithm further includes a threshold optimization algorithm;

[0061] The threshold optimization algorithm includes:

[0062] B1: For all o j ∈O Input each rechargeable device o j threshold θ j ;

[0063] B2: Initialization For all o j ∈O, initialization

[0064] B3: Calculation

[0065] B4: Update S = S∪{g i}, for all o j ∈O, update

[0066] B5: If Then proceed to step B3; otherwise, proceed to step B6.

[0067] B6: Return to S.

[0068] The beneficial effects of this invention are as follows: Considering the diverse charging needs in wireless charging scenarios, this invention proposes a wireless charger scheduling method to optimize charging efficiency. Different charging schemes are executed depending on the charging needs. When it is necessary to maximize the sum of charging efficiencies of all rechargeable devices, an additive optimization algorithm is executed. When it is necessary to maximize the minimum rechargeable device efficiency, a uniform optimization algorithm is executed. When it is necessary to minimize the number of active wireless chargers while ensuring that the charging efficiency of all rechargeable devices exceeds a certain threshold, a threshold optimization algorithm is executed. These three optimization algorithms for different application scenarios save a significant amount of computation time compared to ordinary optimization algorithms, while maintaining the same accuracy. This effectively increases charging efficiency and reduces charging costs. Attached Figure Description

[0069] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0070] Figure 1 A flowchart illustrating a wireless charger scheduling method for optimizing charging efficiency, provided as an embodiment of the present invention;

[0071] Figure 2 This is a schematic diagram illustrating a method for optimizing charging efficiency in a wireless charger scheduling method according to an embodiment of the present invention.

[0072] Figure 3 A flowchart of the cumulative optimization algorithm for a wireless charger scheduling method to optimize charging efficiency provided in an embodiment of the present invention;

[0073] Figure 4 This is a flowchart of a uniform optimization algorithm in a wireless charger scheduling method for optimizing charging efficiency, provided in one embodiment of the present invention.

[0074] Figure 5This is a flowchart of a threshold optimization algorithm in a wireless charger scheduling method for optimizing charging efficiency, provided in one embodiment of the present invention.

[0075] Figure 6 This is a performance comparison chart of the cumulative optimization algorithm of a wireless charger scheduling method for optimizing charging efficiency provided in an embodiment of the present invention;

[0076] Figure 7 A performance comparison chart of a uniform optimization algorithm for a wireless charger scheduling method that optimizes charging efficiency according to an embodiment of the present invention;

[0077] Figure 8 This is a performance comparison chart of a threshold optimization algorithm for a wireless charger scheduling method that optimizes charging efficiency according to an embodiment of the present invention.

[0078] Figure 9 A comparison diagram of the time overhead of various algorithms in a wireless charger scheduling method for optimizing charging efficiency, provided as an embodiment of the present invention. Detailed Implementation

[0079] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0080] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0081] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0082] This invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of this invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not adhering to the usual scale. Furthermore, the schematic diagrams are merely examples and should not be construed as limiting the scope of protection of this invention. In actual fabrication, the three-dimensional spatial dimensions of length, width, and depth should be included.

[0083] Furthermore, in the description of this invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used solely for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In addition, the terms "first," "second," or "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0084] Unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" in this invention should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; similarly, they can refer to mechanical connections, electrical connections, or direct connections, or indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0085] Example 1

[0086] Reference Figures 3-5 As an embodiment of the present invention, a wireless charger scheduling method for optimizing charging efficiency is provided, comprising:

[0087] S1: Collect relevant information about the wireless charger and the target rechargeable device;

[0088] Furthermore, the relevant information includes:

[0089] The set of wireless chargers in a fixed, off state, G = {g1, g2, ..., g...} l};

[0090] Wireless charger collection in power-on state

[0091] A collection of rechargeable devices

[0092] Among them, wireless charger If g i If ∈G, then g i Initially in the off state; if Then g i Initially, it is in the "on" state and can be used as a rechargeable device.

[0093] S2: Determine the issues that need optimization based on actual requirements;

[0094] Furthermore, the actual needs include a first need, a second need, and a third need;

[0095] The first requirement is that, under the constraint of a certain number of wireless chargers that can be turned on, the user wants to maximize the cumulative utility of each rechargeable device;

[0096] The second requirement is that, under a certain constraint on the number of wireless chargers that can be turned on, users want the charging device with the lowest utility to have the highest utility.

[0097] The third requirement is that, under a certain threshold of rechargeable device utility, the user wants to minimize the number of wireless chargers that need to be turned on.

[0098] Furthermore, the issues requiring optimization include:

[0099] The cumulative optimization problem corresponding to the first requirement;

[0100] The cumulative optimization problem is:

[0101]

[0102] Where S is the independent variable, representing the set of wireless chargers that are turned on after scheduling; U(S) is the optimization objective, representing the charging utility when the set of wireless chargers turned on is S. In the cumulative optimization problem, the value of U(S) is the sum of the charging power of all rechargeable devices; k is the upper limit of the number of chargers turned on, a given positive integer, indicating that the size of the set of wireless chargers turned on |S| cannot be greater than k. For rechargeable devices j When the charger assembly is opened The charging power obtained under the condition of.

[0103] Furthermore, the issues requiring optimization also include:

[0104] The second requirement corresponds to the uniform optimization problem;

[0105] The uniform optimization problem is:

[0106]

[0107] Where S is the independent variable, representing the set of wireless chargers that are turned on after scheduling; U(S) is the optimization objective, representing the charging utility when the set of wireless chargers turned on is S. In the uniform optimization problem, the value of U(S) is the minimum charging power among all rechargeable devices; k is the upper limit of the number of chargers turned on, which is a given positive integer, indicating that the size of the set of wireless chargers turned on, |S|, cannot be greater than k. For rechargeable devices j When the charger assembly is opened The charging power obtained under the condition of.

[0108] Furthermore, the issues requiring optimization also include:

[0109] The threshold optimization problem corresponding to the third requirement;

[0110] The threshold optimization problem is:

[0111]

[0112] Where S is the independent variable, representing the set of wireless chargers that are turned on after scheduling; θ j For rechargeable devices j The threshold is a given positive real number representing the threshold value for rechargeable devices. j The charging power obtained must be no less than θ j , For rechargeable devices j When the charger assembly is opened The charging power obtained under the condition of.

[0113] Specifically, the rechargeable device o j From wireless charger g i The method for calculating the obtained charging power is as follows:

[0114]

[0115] Among them, d(g i ,o j (This is a wireless charger g) i With rechargeable devices j The Euclidean distance between them; α i,j and β i,j It is made by charger g i and rechargeable devices j Two constants determined by the magnetic field environment and hardware parameters; D i It is any rechargeable device that can be charged from the charger g i The maximum charging distance to achieve positive power;

[0116] Rechargeable devices j The charging power obtained from all wireless chargers is:

[0117]

[0118] in, S represents the set of wireless chargers that are turned on in the schedule.

[0119] S3: Execute the corresponding optimization algorithm based on the problem that needs to be optimized;

[0120] The optimization algorithms include cumulative optimization algorithms, uniform optimization algorithms, and threshold optimization algorithms.

[0121] Specifically, the cumulative optimization algorithm includes:

[0122] R1: Enter the maximum number of chargers that can be turned on, k;

[0123] R2: Initialization

[0124] R3: Calculation

[0125] R4: Update S = S∪{g i};

[0126] R5: If |S|≤k, then jump to step R3; otherwise, proceed to step R6.

[0127] R6: Return S.

[0128] It should be noted that this cumulative optimization algorithm is a An approximation algorithm. Its applicability, usability, and beneficial effects will be demonstrated through the following arguments:

[0129] First, let's give Lemma 1 and Lemma 2:

[0130] Lemma 1: The objective function U(S) of the cumulative optimization problem is a nonnegative, monotonic submodule function. Proof: P j (S) is clearly nonnegative and monotonic. Next, we prove that for any o... j ∈O, P j (S) is a submodule function. For any And if x∈Ω\Y, then:

[0131]

[0132]

[0133]

[0134] Obviously there is P j (X∪{x})-f j (X)≥P j (Y∪{x})-f j (Y). Therefore, P j U(S) must be a submodule function. Since a non-negative linear combination of submodule functions is also a submodule function, U(S) is also a non-negative, monotonic submodule function.

[0135] Lemma 2: In cumulative optimization problems, there must be... Where S * Let S be the optimal solution to the cumulative optimization problem, where S is any solution.

[0136] Proof: Let S\S * ={g1,g2,…,g q}, where q≤k. Therefore, there must be... As can be seen from Lemma 1, U(S) has submodule property.

[0137] Therefore, there must be

[0138] Therefore, there must be

[0139] Organized

[0140] Let S r Let S be the solution set at the end of the r-th iteration of the loop shown in steps R3-R5 of the cumulative optimization algorithm. According to Lemma 2, we must have...

[0141] Therefore, there is

[0142] The following explains that the cumulative optimization algorithm is... Approximate algorithm.

[0143] Let S r The solution to the set S is obtained by accumulating the optimization algorithm steps R3-R5, which are shown in the loop.

[0144] According to Lemma 2, there must be...

[0145] Therefore, there is

[0146] In summary, the cumulative optimization algorithm is used to obtain an approximate solution to the cumulative optimization problem. This approximate solution ensures that the objective function of the cumulative optimization problem is no less than the optimal value of the objective function. times.

[0147] Meanwhile, the asymptotic upper bound time complexity of the cumulative optimization algorithm is O(ktl).

[0148] The cumulative optimization algorithm's steps, shown in steps R3-R5, are executed at most k times. In each iteration of this loop, step R3 searches all l off-state wireless chargers to find the one with the highest marginal utility. When calculating marginal utility, the charging utility of all t rechargeable devices is accumulated. Therefore, the asymptotic upper bound time complexity of the cumulative optimization algorithm is O(ktl).

[0149] In practice, the cumulative optimization algorithm can obtain a solution that is only slightly different from the optimal algorithm, thus saving a lot of time while ensuring accuracy.

[0150] Specifically, the optimization algorithm also includes a uniform optimization algorithm;

[0151] The uniform optimization algorithm includes:

[0152] A1: Input relaxation coefficient The upper limit k of the number of chargers to be opened and the search precision ε, where ε is a given positive real number;

[0153] A2: Initialize p min =0,

[0154] A3: Calculation initialization

[0155] A4: Calculation Update S′=S′∪{g i},in,

[0156] A5: If If S′≠G, then proceed to step A4; otherwise, proceed to step A6.

[0157] A6: If |S'|>δk, then update p. max =p; otherwise update p min =p, S = S′;

[0158] A7: If (p max -p min If ε ≥ ε, then proceed to step A3; otherwise, proceed to step A8.

[0159] A8: Return to S.

[0160] It should be noted that in the uniform optimization algorithm, when the relaxation coefficient...

[0161] When, the solution obtained by uniform optimization must satisfy And |S|≤δk, where After relaxing the constraints by a factor of δ, the uniform optimization algorithm can guarantee that the objective equation of the uniform optimization problem approximates the optimal solution of the original problem.

[0162] To illustrate its beneficial effects, Lemma 3 and its proof are given first.

[0163] Lemma 3: In uniform optimization algorithms It is a sub-modular function.

[0164] prove:

[0165] P j (S′) has been proven to be a submodule function in Lemma 1. Since the cutoff function of a submodule function is necessarily a submodule function, therefore... It must be a submodular function. Since a non-negative linear combination of submodular functions is also a submodular function, therefore... It is also a submodular function.

[0166] First, prove... It is easy to see that the loop shown in steps A4-A6 of the uniform optimization algorithm is essentially a greedy method to solve the problem given a specific p and with the domain |S|≤δk. In step A5, only when Only then is it possible to execute S = S′ in step A6. At this point, it is certain that for any o... j ∈O has P j (S)≥p. Therefore, after the algorithm iterates and searches for a suitable p, it leads to... It gets larger and larger. Furthermore, since the domain of the problem solved by the algorithm is |S|≤δk, the solution space of the problem solved by the algorithm is larger than the domain of the original uniform optimization problem. Therefore, when the search iteration reaches a certain point, it is certain that...

[0167] Then we prove that for any |S|≤δk, in step A6, S=S′ can only be executed if |S'|≤δk. Therefore, |S|≤δk must hold.

[0168] Meanwhile, the asymptotic upper bound time complexity of the uniform optimization algorithm is...

[0169] In the loop shown in steps A3-A7, a total of binary search is performed on ε. In each iteration of ε, finding the charger with the highest marginal utility in step (324) takes O(tl) time. Since there are l wireless chargers in the off state, the loop shown in steps A4-A5 is executed at most l times. Therefore, the asymptotic upper bound time complexity of the uniform optimization algorithm is O(tl).

[0170] Therefore, in practice, with appropriate relaxation of constraints, uniform optimization can yield solutions no worse than the optimal algorithm. Furthermore, uniform optimization can be completed in polynomial time. Compared to the optimal algorithm, uniform optimization can save a significant amount of time.

[0171] Specifically, the optimization algorithm also includes a threshold optimization algorithm;

[0172] The threshold optimization algorithm includes:

[0173] B1: For all o j ∈O Input each rechargeable device o j threshold θ j ;

[0174] B2: Initialization For all o j ∈O, initialization

[0175] B3: Calculation

[0176] B4: Update S = S∪{g i}, for all o j ∈O, update

[0177] B5: If Then proceed to step B3; otherwise, proceed to step B6.

[0178] B6: Return to S.

[0179] It should be noted that the threshold optimization algorithm is a... Approximate algorithm.

[0180] Since the threshold optimization problem is essentially a generalized covering problem, the above conclusion can be directly obtained through the approximation of the generalized covering problem. This approximate solution ensures that the objective function of the threshold optimization problem is no less than the optimal value of that objective function. times.

[0181] Meanwhile, the asymptotic upper bound time complexity of the threshold optimization algorithm is O(tl). 2 ).

[0182] In the loop shown in steps B3-B5, the iteration continues until all l off-state chargers are selected, therefore iterates at most l times. In step B3, each time the charger that reduces the threshold by the most after being turned on is found, it requires traversing at most all l off-state chargers and all t rechargeable devices. Therefore, it consumes O(tl) time. Thus, the asymptotic upper bound time complexity of the threshold optimization algorithm is O(tl). 2 ).

[0183] In practice, threshold optimization algorithms can yield solutions that are not significantly different from the optimal algorithm, demonstrating that they can be completed in polynomial time. Compared to the optimal algorithm, threshold optimization algorithms can save a tremendous amount of time.

[0184] S4: Turn on the corresponding wireless charger based on the result obtained from the corresponding optimization algorithm.

[0185] Example 2

[0186] Reference Figure 1-5 This is one embodiment of the present invention.

[0187] A schematic diagram of a wireless charger scheduling method for optimizing charging efficiency as described in this invention is shown below. Figure 1 As shown.

[0188] The purpose of this invention is to enable a series of wireless chargers that are normally in a closed state to increase the charging power at the charging device for specific charging optimization needs. Figure 1 The central area measures 5m x 6m, with each square measuring 1m x 1m. There are 5 wireless chargers in the off state and 5 wireless chargers in the off state, along with 5 charging devices. All wireless chargers and charging devices are located in the center of each square. Figure 2 This is a flowchart of a wireless charger scheduling method to optimize charging efficiency.

[0189] In this implementation case, the wireless charger and rechargeable device are deployed in a rectangular area of ​​18m × 18m. Other parameters are shown in Table 1.

[0190] Table 1. Parameter Settings

[0191]

[0192]

[0193] In step S1 of this implementation case, by Figure 1 It can be seen that the relevant information collected about the wireless charger and the target rechargeable device is as follows: the set of wireless chargers in the off state at fixed locations, G = {g1, g2, f3, g4, g5}, and the set of wireless chargers in the on state. and a collection of rechargeable devices

[0194] Furthermore, substituting the parameters from Table 1 into formula (4) yields:

[0195]

[0196] In step S2 of this implementation example, the optimization problem needs to be determined according to the actual needs. This example will now use all three types of optimization problems (cumulative optimization problem, uniform optimization problem, and threshold optimization problem) as examples to illustrate the usage method of the present invention.

[0197] In step S3 of this implementation case, a specified optimization algorithm needs to be executed based on the optimization objective determined in step S2. If the optimization problem determined in step S2 is an incremental optimization problem, then the incremental optimization algorithm is executed. If the optimization problem determined in step S2 is a uniform optimization problem, then the uniform optimization algorithm is executed. If the optimization problem determined in step S2 is a threshold optimization problem, then the threshold optimization algorithm is executed.

[0198] If the cumulative optimization algorithm is executed, steps R1-R6 must be performed. First, in step R1, input the upper limit k for turning on the charger, which is 4. Step R2 involves initialization. Next, calculate in step R3 Where, for any g i′ ∈G, U(S∪{g i′ The results of})-U(S) are shown in Table 2. Therefore, we have Next, in step R4, update S = S∪{g1} = {g1}. Then, in step R5, check if the condition |S|≤k is satisfied. Since |S| = 1 and k = 4, the condition |S|≤k is obviously satisfied, so jump to step R3. This process continues, looping from step R3 to step R5 until the condition |S|≤k is no longer satisfied in step R5, at which point step R6 is executed. Then, in step R6, return S = {g1,g3,g4,g5}. Therefore, S = {g1,g3,g4,g5} represents the specific set of wireless chargers activated in step S4.

[0199] Table 2 shows the steps (313) of the cumulative optimization algorithm, including each U(S∪{g i})-U(S) value

[0200]

[0201]

[0202] If the uniform optimization algorithm is to be executed, steps (321)-(328) should be performed. First, in step A1, input parameters δ=1, the upper limit of the charger k=4, and the search precision ε=10. -3 Next, in step A2, p is initialized. min =0, Where, for any o j ∈O, P j The results for (G) are shown in Table 3. It is easy to see from Table 3 that... Therefore, p is initialized. max =3.484. Then initialize. Next, calculate in step (323) initialization Next, calculate in step A4. Where, for any g j ∈G\S′, The results are shown in Table 4. It is easy to see from Table 4 that… Therefore, update S′=S′∪{g5}={g5}. Next, in step A5, determine the condition. And whether S′≠G holds true. If true, proceed to step A3; otherwise, execute step A8. Because now... And S′={g5}≠{g1,g2,g3,g4,g5}=G, therefore the condition is true. And S′≠G holds true. Therefore, we jump to step A3. This process continues, executing the loop shown in steps A4 to A5 until the condition in step A5 is met. Only after S′≠G is no longer true will the process proceed to step A6. Then, during step A6, if the condition |S'|>δk=4 is true, then p is updated. max =p; otherwise update p min =p, S = S′. Since S′ = {g1, g2, g4, g5} at this time, the condition |S′| > 4 is not true. Therefore, update p. min =p=1.742, S=S′={g1,g2,g4,g5}. Then execute step A7, if condition (p max -p min )≥ε=10 -3 If the condition is met, proceed to step A3; otherwise, execute step A8. Because at this point (p max -p min ) = 3.484 - 1.742 = 1.742, therefore the condition (p) max -p min )≥ε=10 -3 The condition is met. Therefore, proceed to step A3. This process continues, looping from step A3 to step A7 until the condition in step A7 is met. The process continues until S′≠G is found to be false, then proceeds to step A8. During step A8, the result is S = {g1, g2, g4, g5}. Therefore, S = {g1, g3, g4, g5} represents the specific set of wireless chargers activated in step S4.

[0203] Table 3. Steps A2 of the uniform optimization algorithm, each P j (G) value

[0204]

[0205] Table 4. Steps A4 of the uniform optimization algorithm numerical values

[0206]

[0207] If the threshold optimization algorithm is executed, steps B1-B6 must be performed. First, in step B1, for all o... j ∈O Input each rechargeable device o j threshold θ j =1. Then execute step B2 to initialize. For all o j ∈O, initialization Next, proceed to step B3 to calculate. Where, for any g i′ ∈G\S, The results are shown in Table 5. It is easy to see from Table 5 that… Next, in step (334), update S = S∪{g5} = {{g5}}. For all o j ∈O, update After the update, each θ′ j The values ​​are shown in Table 6. Next, the conditions are determined in step B5. Check if the condition is met. If it is, proceed to step B3; otherwise, execute step B6. Because at this point... Therefore, the conditions The condition is met. Therefore, proceed to step B3. This process continues, looping from step B3 to step B6 until the condition in step B6 is met. Only after this condition is not met will the process proceed to step B7. Then, during step B7, the result is S = {g2, g5}. Therefore, S = {g2, g5} represents the specific set of wireless chargers activated in step S4.

[0208] Table 5. Each step in step B3 of the threshold optimization algorithm. numerical values

[0209]

[0210] Table 5 shows the θ′ values ​​in step B4 of the threshold optimization algorithm. j numerical values

[0211]

[0212] Example 3

[0213] Reference Figures 6-9 This is another embodiment of the present invention. In order to verify and explain the technical effects used in this method, this embodiment uses a traditional technical solution to compare and test with the method of the present invention. The test results are compared using scientific demonstration methods to verify the real effect of the method.

[0214] This implementation case uses MATLAB on a PC with a CPU clock speed of 2.50GHz for simulation. Figures 6-9This figure shows the results of all experiments in this implementation case. Each data point in the figure is the average of the results obtained from 100 experiments. The locations of the rechargeable devices and wireless chargers in this implementation case are randomly generated by a random algorithm, and their default parameters are shown in Table 6.

[0215] Table 6. Parameter Settings

[0216]

[0217]

[0218] like Figures 6-9 As shown, this embodiment compares the cumulative optimization algorithm (COA), uniform optimization algorithm (OOA), and threshold optimization algorithm (TOA) with two traditional heuristic greedy algorithms. These two heuristic greedy algorithms are:

[0219] Minimum Coverage Algorithm (MinT): In each iteration, selects the wireless charger that covers the fewest number of rechargeable devices. Maximum Coverage Algorithm (MaxC): In each iteration, selects the wireless charger that covers the most rechargeable devices.

[0220] from Figure 6 The results show that the total utility calculated by COA is on average 4.7 times higher than that of MinT and 2.5 times higher than that of MaxC. From... Figure 7 The results show that the total utility calculated by OOA is on average 1.6 times higher than that of MinT and infinitely higher than that of MaxC. From... Figure 8 The results show that the number of chargers turned on, calculated by TOA, is only 23.66% and 24.66% of that of MinT and MaxC, respectively. From... Figure 9 The results show that the execution time for COA, OOA, and TOA is within an acceptable range (less than 500 milliseconds).

[0221] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A wireless charger scheduling method for optimizing charging efficiency, characterized in that, include: Collect relevant information about the wireless charger and the target rechargeable device, including: A collection of wireless chargers that achieve a fixed-position, off-state state. ; Wireless charger collection in power-on state ; A collection of rechargeable devices ; Among them, wireless charger ;like ,but Initially in the off state; if ,but Initially, it is in the "on" state and can be used as a rechargeable device. ; Identify the issues that need optimization based on actual requirements; Execute the corresponding optimization algorithm based on the problem that needs to be optimized; The rechargeable device From wireless charger The method for calculating the obtained charging power is as follows: in, It is a wireless charger With rechargeable devices The Euclidean distance between them; and It is a charger and rechargeable devices Two constants determined by the magnetic field environment and hardware parameters; It can be used by any rechargeable device from the charger. The maximum charging distance to achieve positive power; Rechargeable devices The charging power obtained from all wireless chargers is: in, , This represents the set of wireless chargers that are turned on during the scheduling process; The optimization algorithm includes an accumulation-plus optimization algorithm; The cumulative optimization algorithm includes: R1: Enter the maximum number of chargers that can be turned on. ; R2: Initialization ; R3: Calculation ; R4: Update ; R5: If If yes, proceed to step R3; otherwise, proceed to step R6. R6: Return ; The optimization algorithm also includes a uniform optimization algorithm; The uniform optimization algorithm includes: A1: Input relaxation coefficient Maximum number of chargers that can be opened and search accuracy ,in Let be a given positive real number; A2: Initialization , , ; A3: Calculation ,initialization ; A4: Calculation ,renew ,in, , ; A5: If and If yes, proceed to step A4; otherwise, proceed to step A6. A6: If Then update Otherwise update , ; A7: If If yes, proceed to step A3; otherwise, proceed to step A8. A8: Return ; The optimization algorithm also includes a threshold optimization algorithm; The threshold optimization algorithm includes: B1: For all Input each rechargeable device threshold ; B2: Initialization For all ,initialization ; B3: Calculation ; B4: Update For all ,renew ; B5: If If the condition is met, proceed to step B3; otherwise, proceed to step B6. B6: Return ; The corresponding wireless charger is activated based on the results obtained from the optimization algorithm.

2. The wireless charger scheduling method for optimizing charging efficiency as described in claim 1, characterized in that: The actual needs include the first need, the second need, and the third need; The first requirement is that, under the constraint of a certain number of wireless chargers that can be turned on, the user wants to maximize the cumulative utility of each rechargeable device; The second requirement is that, under a certain constraint on the number of wireless chargers that can be turned on, users want the charging device with the lowest utility to have the highest utility. The third requirement is that, under a certain threshold of rechargeable device utility, the user wants to minimize the number of wireless chargers that need to be turned on.

3. The wireless charger scheduling method for optimizing charging efficiency as described in claim 2, characterized in that: The issues requiring optimization include: The cumulative optimization problem corresponding to the first requirement; The cumulative optimization problem is: in, is the independent variable, representing the set of wireless chargers that are turned on after scheduling; To optimize the objective, the set of wireless chargers that are turned on is represented as follows: The charging efficiency during the summation optimization problem, The value is the sum of the charging power of all rechargeable devices; The maximum number of chargers that can be turned on is a given positive integer representing the size of the set of wireless chargers that can be turned on. Cannot be greater than , For rechargeable devices When the charger assembly is opened The charging power obtained under the condition of.

4. The wireless charger scheduling method for optimizing charging efficiency as described in claim 3, characterized in that: The issues requiring optimization also include: The second requirement corresponds to the uniform optimization problem; The uniform optimization problem is: in, is the independent variable, representing the set of wireless chargers that are turned on after scheduling; To optimize the objective, the set of wireless chargers that are turned on is represented as follows: The charging efficiency during the process, in the problem of uniform optimization. The value is the minimum charging power among all rechargeable devices; The maximum number of chargers that can be opened is a given positive integer representing the size of the infinite set of chargers that can be opened. Cannot be greater than ; For rechargeable devices When the charger assembly is opened The charging power obtained under the condition of.

5. The wireless charger scheduling method for optimizing charging efficiency as described in claim 4, characterized in that: The issues requiring optimization also include: The threshold optimization problem corresponding to the third requirement; The threshold optimization problem is: in, is the independent variable, representing the set of wireless chargers that are turned on after scheduling; For rechargeable devices The threshold is a given positive real number representing the threshold for rechargeable devices. The charging power obtained must be no less than , For rechargeable devices When the charger assembly is opened The charging power obtained under the condition of.

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