Magnetic resonance wireless charging strategy selection method and device and wireless charging equipment

Abstract

The invention relates to a magnetic resonance wireless charging strategy selection method and device and wireless charging equipment, in particular to the technical field of magnetic resonance wireless charging. Comprising the steps of obtaining battery initial parameters and a charging strategy set; based on the battery initial parameter and the charging strategy set, simulating a battery charging process to obtain charging parameters corresponding to different charging strategies; and according to the charging parameters corresponding to different charging strategies, selecting a target charging strategy from the charging strategy set, and carrying out magnetic resonance wireless charging based on the target charging strategy. According to the embodiment of the invention, the problem of serious hysteresis when the charging power is adjusted by the battery management chip is solved, and the magnetic resonance wireless charging process of the battery is adjusted in real time.

Description

Technical Field

[0001] The present application relates to the field of magnetic resonance wireless charging technology, and in particular to a method and apparatus for selecting a magnetic resonance wireless charging strategy, and a magnetic resonance wireless charging device, as well as a computer-readable storage medium for implementing the method for selecting a magnetic resonance wireless charging strategy, and a computer program product for implementing the method for selecting a magnetic resonance wireless charging strategy. Background Art

[0002] Magnetic resonance wireless charging technology can achieve wireless energy transmission over a distance of several centimeters or even longer, providing greater flexibility and freedom for electronic devices. Its principle is based on magnetic resonance and requires two resonant systems for transmission and reception, usually made of induction coils. The transmitting end vibrates at a certain frequency to generate a non-radiative magnetic field. The receiving end has the same natural frequency as the transmitting end, and resonance occurs. With each resonance, more voltage is generated in the receiving end sensor. After multiple resonances, enough energy is gathered on the sensor surface to complete the conversion of magnetic energy to electrical energy, thus realizing wireless transmission of electrical energy.

[0003] Existing device charging technology relies on the battery management chip in the device to adjust the charging power, and cannot adjust it in real time according to the actual situation of the device. For example, when the device heats up and the signal deteriorates during charging, but the user still needs good network quality, the battery management chip will have serious lag in adjusting the charging power. Summary of the invention

[0004] In order to solve the above technical problems or at least partially solve the above technical problems, the present application provides a method, an apparatus and a magnetic resonance wireless charging device for selecting a magnetic resonance wireless charging strategy, as well as a computer-readable storage medium for implementing the method for selecting a magnetic resonance wireless charging strategy, and a computer program product for implementing the method for selecting a magnetic resonance wireless charging strategy, which can realize real-time adjustment of battery charging.

[0005] In order to achieve the above purpose, the technical solutions provided by the embodiments of the present application are as follows:

[0006] In a first aspect, the present application provides a method for selecting a magnetic resonance wireless charging strategy, comprising: obtaining initial battery parameters and a charging strategy set; based on the initial battery parameters and the charging strategy set, simulating the battery charging process to obtain charging parameters corresponding to different charging strategies; according to the charging parameters corresponding to different charging strategies, selecting a target charging strategy from the charging strategy set, so as to perform magnetic resonance wireless charging based on the target charging strategy.

[0007] As an optional implementation in the embodiment of the present application, a target charging strategy is selected from a charging strategy set according to the charging parameters corresponding to different charging strategies, so as to perform charging based on the target charging strategy, including: evaluating the fitness of different charging strategies according to the charging parameters corresponding to different charging strategies; and selecting the target charging strategy from the charging strategy set according to the fitness of different charging strategies.

[0008] As an optional implementation in the embodiment of the present application, the charging parameters corresponding to different charging strategies include: remaining battery power, current battery temperature, battery aging speed, charging current, charging voltage, charging efficiency, battery health status and objective function; wherein, the battery aging speed is calculated according to the following formula: age(S(t),T(t))=k1·(1-S(t)α)+k2·T(t)β, S(t) is the battery power, T(t) is the battery temperature, k1 and k2 are adjustment parameters; α and β are power terms used to simulate the influence of battery power and battery temperature on battery aging speed; the charging efficiency is calculated according to the following formula: η(t)=e (-k3·I(t)γ·V(t)δ) , I(t) is the charging current, V(t) is the charging voltage, k3 is the adjustment parameter; γ and δ are power terms used to simulate the influence of battery current and battery voltage on charging efficiency; the objective function is calculated according to the following formula: f(S(t), T(t), I(t), V(t)) = min[age(S(t), T(t))·(1-η(t))].

[0009] As an optional implementation in the embodiment of the present application, the fitness of different charging strategies is evaluated according to the charging parameters corresponding to different charging strategies, including: according to the remaining battery power and charging efficiency corresponding to different charging strategies, the fitness is calculated according to the following formula

[0010] As an optional implementation in the embodiment of the present application, the charging strategy includes charging according to a preset current or a preset voltage, and the method also includes: according to the fitness of different charging strategies, selecting N charging strategies with higher fitness, N is a positive integer; performing an exchange process on each of the N charging strategies, and the exchange process is charging with a charging current value of a preset voltage and charging with a charging voltage value of a preset current; for the charging strategy after the exchange process, randomly adjusting the preset voltage and / or preset current, generating a new charging strategy and adding it to the charging strategy set to update the charging strategy set.

[0011] In a second aspect, the present application provides a device for selecting a magnetic resonance wireless charging strategy, the device comprising:

[0012] An acquisition module is used to obtain the initial battery parameters and charging strategy set;

[0013] A simulation module, used for simulating the battery charging process to obtain charging parameters corresponding to different charging strategies based on the battery initial parameters and the charging strategy set;

[0014] The selection module is used to select a target charging strategy from the charging strategy set according to the charging parameters corresponding to the different charging strategies, so as to perform magnetic resonance wireless charging based on the target charging strategy.

[0015] As an optional implementation in the embodiment of the present application, the selection module is specifically used to: evaluate the fitness of different charging strategies according to the charging parameters corresponding to different charging strategies; and select a target charging strategy from a charging strategy set according to the fitness of different charging strategies.

[0016] As an optional implementation in the embodiment of the present application, the charging parameters corresponding to different charging strategies include: remaining battery power, current battery temperature, battery aging speed, charging current, charging voltage, charging efficiency, battery health status and objective function; wherein, the battery aging speed is calculated according to the following formula: age(S(t),T(t))=k1·(1-S(t)α)+k2·T(t)β, S(t) is the battery power, T(t) is the battery temperature, k1 and k2 are adjustment parameters; α and β are power terms used to simulate the influence of battery power and battery temperature on battery aging speed; the charging efficiency is calculated according to the following formula: η(t)=e (-k3·I(t)γ·V(t)δ) , I(t) is the charging current, V(t) is the charging voltage, k3 is the adjustment parameter; γ and δ are power terms used to simulate the influence of battery current and battery voltage on charging efficiency; the objective function is calculated according to the following formula: f(S(t), T(t), I(t), V(t)) = min[age(S(t), T(t))·(1-η(t))].

[0017] As an optional implementation in the embodiment of the present application, the selection module is specifically used to: calculate the fitness according to the following formula based on the remaining battery power and charging efficiency corresponding to different charging strategies:

[0018] As an optional implementation in the embodiment of the present application, the charging strategy includes charging according to a preset current or a preset voltage, and the selection module is also used to: select N charging strategies with higher fitness according to the fitness of different charging strategies, where N is a positive integer; perform exchange processing on each of the N charging strategies, and the exchange processing is charging with a charging current value of a preset voltage and charging with a charging voltage value of a preset current; for the charging strategy after the exchange processing, randomly adjust the preset voltage and / or preset current, generate a new charging strategy and add it to the charging strategy set to update the charging strategy set.

[0019] In a third aspect, the present application provides a magnetic resonance wireless charging device, comprising:

[0020] Magnetic resonance transmitter module, magnetic resonance receiver module, battery management chip, battery and user interface;

[0021] Wherein, the magnetic resonance transmitter module includes an algorithm chip, and when the algorithm chip is executed, the method for selecting a magnetic resonance wireless charging strategy as described in the first aspect or any optional implementation manner thereof is implemented.

[0022] In a fourth aspect, the present application provides a computer-readable storage medium, comprising: a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the method for selecting a magnetic resonance wireless charging strategy as described in the first aspect or any one of its optional embodiments is implemented.

[0023] In a fifth aspect, the present application provides a computer program product, including: the computer program product includes a computer program, and when the computer program is run on a computer, the computer implements the method for selecting a magnetic resonance wireless charging strategy as described in the first aspect or any one of its optional embodiments.

[0024] Compared with the prior art, the technical solution provided by the embodiments of the present application has the following advantages:

[0025] The disclosed embodiments provide a method, apparatus and magnetic resonance wireless charging device for selecting a magnetic resonance wireless charging strategy, as well as a computer-readable storage medium for implementing the method for selecting a magnetic resonance wireless charging strategy, and a computer program product for implementing the method for selecting a magnetic resonance wireless charging strategy. The method first obtains the initial parameters of the battery and a charging strategy set, and based on the initial parameters of the battery and the charging strategy set, simulates the battery charging process to obtain the charging parameters corresponding to different charging strategies, and selects a target charging strategy from the charging strategy set according to the charging parameters corresponding to the different charging strategies, so as to perform magnetic resonance wireless charging based on the target charging strategy. In this way, the embodiment of the present application simulates the charging process of the battery under different charging strategies, determines the charging parameters corresponding to each charging strategy, and optimizes the target charging strategy, thereby adjusting the magnetic resonance wireless charging in real time. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0027] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0028] Figure 1 A schematic diagram of a flow chart of a method for selecting a magnetic resonance wireless charging strategy provided in an embodiment of the present application;

[0029] Figure 2 A schematic diagram of the structure of a device for selecting a magnetic resonance wireless charging strategy provided in an embodiment of the present application;

[0030] Figure 3 This is a schematic diagram of the structure of a magnetic resonance wireless charging device described in an embodiment of the present application. DETAILED DESCRIPTION

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the technical terms required to be used in the embodiments or the description of the prior art are briefly introduced below.

[0032] The state of health (SOH) of a battery refers to the maximum capacity retention rate of a battery relative to a new battery during long-term use. SOH is a percentage value, usually expressed as a percentage of the initial capacity. The battery health state is an important indicator for measuring the degree of battery degradation and remaining capacity. It is mainly achieved by real-time monitoring of battery charge and discharge status, temperature, voltage and other parameters, and estimating the battery SOH through an algorithm. When the SOH is below 80%, the battery performance will be significantly reduced and may need to be replaced or repaired.

[0033] In order to more clearly understand the above-mentioned purposes, features and advantages of the present application, the scheme of the present application will be further described below. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0034] In the following description, many specific details are set forth to facilitate a full understanding of the present application, but the present application may also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only part of the embodiments of the present application, rather than all of the embodiments.

[0035] In order to solve some or all of the technical problems existing in the related art, the embodiments of the present application provide a method, device and magnetic resonance wireless charging device for selecting a magnetic resonance wireless charging strategy, as well as a computer-readable storage medium for implementing the method for selecting a magnetic resonance wireless charging strategy, and a computer program product for implementing the method for selecting a magnetic resonance wireless charging strategy. The method first obtains the initial parameters of the battery and the charging strategy set, and based on the initial parameters of the battery and the charging strategy set, simulates the battery charging process to obtain the charging parameters corresponding to different charging strategies, and selects the target charging strategy from the charging strategy set according to the charging parameters corresponding to the different charging strategies, so as to perform magnetic resonance wireless charging based on the target charging strategy. In this way, the embodiments of the present application simulate the charging process of the battery under different charging strategies, determine the charging parameters corresponding to each charging strategy, and select the target charging strategy, so as to adjust the magnetic resonance wireless charging in real time.

[0036] A method for selecting a magnetic resonance wireless charging strategy provided in an embodiment of the present application can be implemented by a magnetic resonance wireless charging strategy selection device or a magnetic resonance wireless charging device, and the magnetic resonance wireless charging device includes but is not limited to a personal computer, a laptop computer, a tablet computer, a smart phone, etc. The operating system of the magnetic resonance wireless charging device may include Android, a mobile operating system (iOS) developed by Apple, an operating system (Windows) developed by Microsoft Corporation of the United States, etc., and the embodiments of the present application are not limited to this. The magnetic resonance wireless charging device can be operated alone to implement the present application, or it can be connected to the network and implemented by interacting with other computer devices in the network. Among them, the network where the magnetic resonance wireless charging device is located includes but is not limited to the Internet, a wide area network, a metropolitan area network, a local area network, a virtual private network (VPN) network, etc.

[0037] It should be noted that the protection scope of the method for selecting a magnetic resonance wireless charging strategy described in the embodiment of the present application is not limited to the execution order of the steps listed in this embodiment. All solutions implemented by adding, reducing or replacing steps in the prior art based on the principles of the present application are included in the protection scope of the present application.

[0038] like Figure 2 As shown, Figure 2 A flowchart of a method for selecting a magnetic resonance wireless charging strategy provided in an embodiment of the present application is provided. The method can be executed by a magnetic resonance wireless charging strategy selection device or a magnetic resonance wireless charging device, wherein the device can be implemented using software and / or hardware and can generally be integrated into a magnetic resonance wireless charging device. The method mainly includes the following steps S101 to S103:

[0039] S101. Obtaining initial battery parameters and a charging strategy set.

[0040] The initial battery parameters include but are not limited to the initial battery charge, initial temperature, and initial health state of the battery. For example, the initial battery charge S(t0)=20%, the initial temperature T(t0)=25°C, and the initial health state H(t0)=90%.

[0041] The charging strategy set includes multiple charging strategies, including charging according to the preset current I i (t) Charging is performed according to the preset voltage V i (t) for charging. The preset current and the preset voltage can be adjusted. The preset current I i (t) = I min +(I max -I min )*rand(1,D), preset voltage V i (t) = V min +(V max -V min )*rand(1,D), where rand(1,D) is a random array of shape 1*D uniformly distributed in the interval [0,1). min =0.5A, I max =2.0A, V min =3.6V, V max =4.2V, D=3, rand(1,3) is a row vector containing 3 elements, for example [0.123, 0.456, 0.789].

[0042] In some embodiments, before executing step S101, the charging environment is initialized and external charging condition parameters are determined, including but not limited to the power of the charger and the maximum charging current of the battery. When executing step S101, the battery parameters are initialized to obtain the set initial power, initial temperature, initial health status, etc.; the charging strategy set is initialized to obtain multiple pre-created charging strategies to obtain the charging strategy set.

[0043] S102: Based on the initial battery parameters and the charging strategy set, simulate the battery charging process to obtain charging parameters corresponding to different charging strategies.

[0044] According to different charging strategies in the charging strategy set, the battery is simulated for charging, the initial parameters of the battery change, and the charging parameters corresponding to each charging strategy can be obtained. The charging parameters corresponding to each charging strategy include: the remaining battery power S(t), the current battery temperature T(t), the battery aging rate age(S(t), T(t)), the charging current I(t), the charging voltage V(t), the charging efficiency η(t), the battery health status SOH and the objective function.

[0045] The battery aging rate age(S(t),T(t)) is calculated according to the following formula (1):

[0046] age(S(t),T(t))=k1·(1-S(t)α)+k2·T(t)β(1)

[0047] In formula (1), k1 and k2 are adjustment parameters; α and β are power terms used to simulate the effects of the remaining battery power and the current battery temperature on the battery aging rate. The battery aging rate increases as the remaining battery power decreases and the temperature increases.

[0048] The charging efficiency η(t) is calculated according to the following formula (2):

[0049] η(t)=e (-k3·I(t)γ·V(t)δ) (2)

[0050] In formula (2), k3 is an adjustment parameter; γ and δ are power terms used to simulate the effects of battery current and battery voltage on charging efficiency. Charging efficiency increases with the increase of current and voltage.

[0051] The battery health status is related to the charging speed and charging efficiency. For specific calculations, please refer to the existing technology and this application will not go into details here.

[0052] The objective function is calculated according to the following formula (3):

[0053] f(S(t),T(t),I(t),V(t))=min[age(S(t),T(t))·(1-η(t))](3)

[0054] The objective function aims to minimize the battery aging rate while considering the charging efficiency to maximize the battery life and performance.

[0055] S103. Select a target charging strategy from a charging strategy set according to charging parameters corresponding to different charging strategies, so as to perform magnetic resonance wireless charging based on the target charging strategy.

[0056] In some embodiments, when executing step S103, the constraint conditions for selecting the target charging strategy include: the battery health state is greater than or equal to the health state threshold; the battery current temperature is greater than or equal to the first temperature threshold and less than or equal to the second temperature threshold; the charging efficiency is greater than or equal to the efficiency threshold. min It can be the minimum threshold of the battery health status, the first temperature threshold T min is the minimum battery temperature, the second temperature threshold T max is the maximum temperature of the battery, the efficiency threshold η min Is the minimum efficiency of battery charging.

[0057] In some embodiments, step S103 (selecting a target charging strategy from a charging strategy set based on the charging parameters corresponding to different charging strategies) may include the following process: evaluating the fitness of different charging strategies based on the charging parameters corresponding to different charging strategies; and selecting a target charging strategy from the charging strategy set based on the fitness.

[0058] Optionally, the fitness is calculated according to the following formula (4) based on the remaining battery capacity S(t) and the charging efficiency η(t) corresponding to different charging strategies:

[0059]

[0060] Optionally, the fitness of different charging strategies is evaluated according to the battery health status SOH corresponding to different charging strategies. Fitness is positively correlated with the battery health status SOH. The larger the battery health status SOH, the higher the fitness. In addition, the present application can be combined with two optional implementation methods, for example, different weight coefficients are set for different fitness calculation methods to comprehensively consider the remaining battery power, charging efficiency and battery health status, calculate the fitness, and thus select a better charging strategy.

[0061] In some embodiments, after calculating the fitness of different charging strategies, N charging strategies with higher fitness may be selected as target charging strategies.

[0062] In some embodiments, after evaluating the fitness of different charging strategies, cross processing is first performed on N charging strategies with higher fitness, and the cross processing is to charge with a preset voltage as the charging current value and to charge with a preset current as the charging voltage value. The current value and voltage value of the charging are exchanged to realize the crossover of the charging strategy and optimize the charging strategy. N is a positive integer, and N=5 is an exemplary example. For the charging strategy after the exchange processing, the preset voltage and / or the preset current are randomly adjusted to generate a new charging set to update the charging strategy set. Thus, the next round of selection iteration process is carried out until the constraint condition is met, or the preset number of iterations is reached. Randomly adjusting the charging strategy can improve the diversity of the charging strategy set. Since the relationship between the preset current and the preset voltage in the charging strategy includes the random array rand(1, D), the range of random adjustment is [0, 1). The charging current or charging voltage of the charging strategy obtained by random adjustment will become smaller, which can prevent the selection of the charging strategy from falling into a local optimal solution. Optionally, after the new charging strategy is generated in the above embodiment and added to the charging strategy set, all charging strategies in the charging strategy set are randomly adjusted to achieve the variation of the charging strategy to update the charging strategy set.

[0063] On the basis of the above-mentioned embodiment, the charging strategies in the charging strategy set are crossover and mutation, and the target charging strategy is iteratively selected from such a charging strategy set to ensure that it is not a local optimal solution. Charging according to such a target strategy can achieve real-time optimization of the charging process.

[0064] In summary, the embodiment of the present application provides a method for selecting a magnetic resonance wireless charging strategy. The method first obtains the initial battery parameters and the charging strategy set. Based on the initial battery parameters and the charging strategy set, the battery charging process is simulated to obtain the charging parameters corresponding to different charging strategies. According to the charging parameters corresponding to the different charging strategies, a target charging strategy is selected from the charging strategy set to perform magnetic resonance wireless charging based on the target charging strategy. In this way, the embodiment of the present application simulates the charging process of the battery under different charging strategies, determines the charging parameters corresponding to each charging strategy, and selects the target charging strategy, thereby adjusting the magnetic resonance wireless charging in real time.

[0065] In addition, the target charging strategy selected in the above embodiment can provide an optimal charging speed and ensure the health of the battery.

[0066] like Figure 2 As shown, Figure 2 A schematic diagram of a magnetic resonance wireless charging strategy selection device provided in an embodiment of the present application, the device comprising:

[0067] An acquisition module 201 is used to acquire initial battery parameters and a charging strategy set;

[0068] A simulation module 202, configured to simulate a battery charging process based on the battery initial parameters and the charging strategy set to obtain charging parameters corresponding to different charging strategies;

[0069] The selection module 203 is used to select a target charging strategy from the charging strategy set according to the charging parameters corresponding to the different charging strategies, so as to perform magnetic resonance wireless charging based on the target charging strategy.

[0070] As an optional implementation in the embodiment of the present application, the selection module 203 is specifically used to: evaluate the fitness of different charging strategies according to the charging parameters corresponding to different charging strategies; and select a target charging strategy from the charging strategy set according to the fitness of different charging strategies.

[0071] As an optional implementation in the embodiment of the present application, the charging parameters corresponding to different charging strategies include: remaining battery power, current battery temperature, battery aging speed, charging current, charging voltage, charging efficiency, battery health status and objective function; wherein, the battery aging speed is calculated according to the following formula: age(S(t),T(t))=k1·(1-S(t)α)+k2·T(t)β, S(t) is the battery power, T(t) is the battery temperature, k1 and k2 are adjustment parameters; α and β are power terms used to simulate the influence of battery power and battery temperature on battery aging speed; the charging efficiency is calculated according to the following formula: η(t)=e (-k3·I(t)γ·V(t)δ) , I(t) is the charging current, V(t) is the charging voltage, k3 is the adjustment parameter; γ and δ are power terms used to simulate the influence of battery current and battery voltage on charging efficiency; the objective function is calculated according to the following formula: f(S(t), T(t), I(t), V(t)) = min[age(S(t), T(t))·(1-η(t))].

[0072] As an optional implementation in the embodiment of the present application, the selection module 203 is specifically used to: calculate the fitness according to the following formula based on the remaining battery power and charging efficiency corresponding to different charging strategies:

[0073] As an optional implementation in the embodiment of the present application, the charging strategy includes charging according to a preset current or a preset voltage, and the selection module 203 is also used to: select N charging strategies with higher fitness according to the fitness of different charging strategies, where N is a positive integer; perform exchange processing on each of the N charging strategies, and the exchange processing is charging with a charging current value of a preset voltage and charging with a charging voltage value of a preset current; for the charging strategy after the exchange processing, randomly adjust the preset voltage and / or preset current, generate a new charging strategy and add it to the charging strategy set to update the charging strategy set.

[0074] In summary, the present application provides a magnetic resonance wireless charging device, which first obtains the initial parameters of the battery and the charging strategy set, simulates the battery charging process based on the initial parameters of the battery and the charging strategy set to obtain the charging parameters corresponding to different charging strategies, and selects the target charging strategy from the charging strategy set according to the charging parameters corresponding to the different charging strategies, so as to perform magnetic resonance wireless charging based on the target charging strategy. In this way, the embodiment of the present application simulates the charging process of the battery under different charging strategies, determines the charging parameters corresponding to each charging strategy, and optimizes the target charging strategy, thereby adjusting the magnetic resonance wireless charging in real time.

[0075] For the specific definition of the device for selecting the magnetic resonance wireless charging strategy, please refer to the definition of the method for selecting the magnetic resonance wireless charging strategy above, which will not be repeated here. Each module in the above-mentioned device for selecting the magnetic resonance wireless charging strategy can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0076] like Figure 3 As shown, Figure 3 A magnetic resonance-based wireless charging device provided in an embodiment of the present application includes: a magnetic resonance transmitter module, a magnetic resonance receiver module, a battery management chip, a battery, and a user interface;

[0077] Wherein, the magnetic resonance transmitter module includes an algorithm chip, and when the algorithm chip is executed, the method for selecting a magnetic resonance wireless charging strategy as described in any one of the embodiments above is implemented.

[0078] In some embodiments, the magnetic resonance transmitter module includes an algorithm chip, a circuit module, a protocol module, a transmitting antenna, a protection module and a communication module; the algorithm chip implements a wireless charging strategy selection method based on magnetic resonance as provided above; the transmitting antenna includes a transmitting coil, a matching circuit and a driving circuit for generating a magnetic field and transmitting energy; the protection module includes a cooling fan, a heat sink, an overheating protection circuit, an overcharging protection circuit and a short circuit protection circuit;

[0079] In some embodiments, the magnetic resonance receiver module includes a receiving antenna, a protocol module, a circuit module and a communication module; the receiving antenna includes a receiving coil, a matching circuit, a rectifier circuit and a power extraction circuit, which are used to capture the magnetic field and convert it into electrical energy; the communication module is used to identify the device, negotiate parameters, exchange data, feedback the charging status and provide safety protection;

[0080] In some embodiments, a battery management chip is used to distribute power, convert power, regulate power, and manage batteries;

[0081] In some embodiments, the user interface is used to display the charging status and receive charging control input from the user.

[0082] In one embodiment, the present application provides a magnetic resonance wireless charging device, which may be a terminal, and its internal structure diagram may be as follows: Figure 3As shown. The magnetic resonance wireless charging device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the magnetic resonance wireless charging device is used to provide computing and control capabilities. The memory of the magnetic resonance wireless charging device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the magnetic resonance wireless charging device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, near field communication (NFC) or other technologies. When the computer program is executed by the processor, a method for selecting a magnetic resonance wireless charging strategy is implemented. The display screen of the magnetic resonance wireless charging device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the magnetic resonance wireless charging device can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the shell of the magnetic resonance wireless charging device, or an external keyboard, touchpad or mouse, etc.

[0083] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the magnetic resonance wireless charging device to which the solution of the present application is applied. The specific magnetic resonance wireless charging device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0084] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by the computer program, the following steps are implemented:

[0085] Obtaining battery initial parameters and a charging strategy set; simulating the battery charging process to obtain charging parameters corresponding to different charging strategies based on the battery initial parameters and the charging strategy set; selecting a target charging strategy from the charging strategy set according to the charging parameters corresponding to the different charging strategies, so as to perform magnetic resonance wireless charging based on the target charging strategy.

[0086] In one embodiment, when the computer program executes the computer program, the following steps are also implemented: according to the charging parameters corresponding to different charging strategies, a target charging strategy is selected from a charging strategy set to charge based on the target charging strategy, including: according to the charging parameters corresponding to different charging strategies, the fitness of different charging strategies is evaluated; according to the fitness of different charging strategies, the target charging strategy is selected from the charging strategy set.

[0087] In one embodiment, the computer program further implements the following steps when executing the computer program: the charging parameters corresponding to different charging strategies include: the remaining battery power, the current battery temperature, the battery aging speed, the charging current, the charging voltage, the charging efficiency, the battery health status and the objective function; wherein the battery aging speed is calculated according to the following formula: age(S(t),T(t))=k1·(1-S(t)α)+k2·T(t)β, S(t) is the battery power, T(t) is the battery temperature, k1 and k2 are adjustment parameters; α and β are power terms used to simulate the influence of the battery power and battery temperature on the battery aging speed; the charging efficiency is calculated according to the following formula: η(t)=e (-k3·I(t)γ·V(t)δ) , I(t) is the charging current, V(t) is the charging voltage, k3 is the adjustment parameter; γ and δ are power terms used to simulate the influence of battery current and battery voltage on charging efficiency; the objective function is calculated according to the following formula: f(S(t), T(t), I(t), V(t)) = min[age(S(t), T(t))·(1-η(t))].

[0088] In one embodiment, when the computer program is executed, the following steps are further implemented: according to the charging parameters corresponding to the different charging strategies, the fitness of the different charging strategies is evaluated, including: according to the remaining battery power and charging efficiency corresponding to the different charging strategies, the fitness is calculated according to the following formula

[0089] In one embodiment, when the computer program executes the computer program, the following steps are also implemented: the charging strategy includes charging according to a preset current or a preset voltage, and the method also includes: according to the fitness of different charging strategies, selecting N charging strategies with higher fitness, N is a positive integer; performing an exchange process on each of the N charging strategies, and the exchange process is charging with a charging current value of a preset voltage and charging with a charging voltage value of a preset current; for the charging strategy after the exchange process, randomly adjusting the preset voltage and / or preset current, generating a new charging strategy and adding it to the charging strategy set to update the charging strategy set.

[0090] When the computer program in the computer-readable storage medium provided by the present application executes the computer program, the initial battery parameters and the charging strategy set are first obtained. Based on the initial battery parameters and the charging strategy set, the battery charging process is simulated to obtain the charging parameters corresponding to different charging strategies. According to the charging parameters corresponding to the different charging strategies, a target charging strategy is selected from the charging strategy set to perform magnetic resonance wireless charging based on the target charging strategy. In this way, the embodiment of the present application simulates the charging process of the battery under different charging strategies, determines the charging parameters corresponding to each charging strategy, and optimizes the target charging strategy, thereby adjusting the magnetic resonance wireless charging in real time.

[0091] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media that include computer-usable program code.

[0092] In several embodiments provided by the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0093] In the present application, the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0094] In this application, memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0095] In this application, computer-readable media includes permanent and non-permanent, removable and non-removable storage media. Storage media can be implemented by any method or technology to store information, and the information can be computer-readable instructions, data structures, modules of programs or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. According to the definition in this article, computer-readable media does not include temporary computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0096] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0097] The above is only a specific implementation of the present application, so that those skilled in the art can understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for selecting a magnetic resonance wireless charging strategy, characterized in that: include: Get the battery initial parameters and charging strategy set; Based on the battery initial parameters and the charging strategy set, simulating the battery charging process to obtain charging parameters corresponding to different charging strategies; According to the charging parameters corresponding to the different charging strategies, a target charging strategy is selected from the charging strategy set, so as to perform magnetic resonance wireless charging based on the target charging strategy.

2. The method according to claim 1, characterized in that The selecting a target charging strategy from the charging strategy set according to the charging parameters corresponding to the different charging strategies, so as to charge based on the target charging strategy, includes: Evaluating the adaptability of the different charging strategies according to the charging parameters corresponding to the different charging strategies; A target charging strategy is selected from the charging strategy set according to the fitness of the different charging strategies.

3. The method according to claim 2, characterized in that The charging parameters corresponding to the different charging strategies include: remaining battery power, current battery temperature, battery aging rate, charging current, charging voltage, charging efficiency, battery health status and objective function; The battery aging rate is calculated according to the following formula: age(S(t),T(t))=k1·(1-S(t)α)+k2·T(t)β, S(t) is the battery power, T(t) is the battery temperature, k1 and k2 are adjustment parameters; α and β are power terms, which are used to simulate the influence of battery power and battery temperature on battery aging rate; The charging efficiency is calculated according to the following formula: η(t)=e (-k3·I(t)γ·V(t)δ) , I(t) is the charging current, V(t) is the charging voltage, k3 is the adjustment parameter; γ and δ are power terms used to simulate the effects of battery current and battery voltage on charging efficiency; The objective function is calculated according to the following formula: f(S(t), T(t), I(t), V(t))=min[age(S(t), T(t))·(1-η(t))].

4. The method according to claim 3, characterized in that The step of evaluating the adaptability of different charging strategies according to the charging parameters corresponding to the different charging strategies includes: According to the remaining battery power and charging efficiency corresponding to the different charging strategies, the fitness is calculated according to the following formula:

5. The method according to claim 4, characterized in that The charging strategy includes charging according to a preset current or a preset voltage. The method further includes: According to the fitness of different charging strategies, select N charging strategies with higher fitness, where N is a positive integer; Performing a switching process on each of the N charging strategies, wherein the switching process is to charge with a charging current value of a preset voltage and to charge with a charging voltage value of a preset current; For the charging strategy after the exchange process, the preset voltage and / or the preset current are randomly adjusted to generate a new charging strategy and add it to the charging strategy set to update the charging strategy set.

6. A device for selecting a magnetic resonance wireless charging strategy, characterized in that: include: An acquisition module is used to obtain the initial battery parameters and charging strategy set; A simulation module, used for simulating the battery charging process to obtain charging parameters corresponding to different charging strategies based on the battery initial parameters and the charging strategy set; The selection module is used to select a target charging strategy from the charging strategy set according to the charging parameters corresponding to the different charging strategies, so as to perform magnetic resonance wireless charging based on the target charging strategy.

7. The device according to claim 6, characterized in that The selection module is specifically used for: Evaluating the adaptability of the different charging strategies according to the charging parameters corresponding to the different charging strategies; A target charging strategy is selected from the charging strategy set according to the fitness of the different charging strategies.

8. A magnetic resonance wireless charging device, characterized in that: include: Magnetic resonance transmitter module, magnetic resonance receiver module, battery management chip, battery and user interface; Wherein, the magnetic resonance transmitter module includes an algorithm chip, and when the algorithm chip is executed, the method for selecting a magnetic resonance wireless charging strategy according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that: include: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for selecting a magnetic resonance wireless charging strategy according to any one of claims 1 to 6 is implemented.

10. A computer program product, characterized in that include: The computer program product comprises a computer program, and when the computer program is executed on a computer, the computer is enabled to implement the method for selecting a magnetic resonance wireless charging strategy according to any one of claims 1 to 6.

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