Charging method and device based on real-time feedback control and charger

Abstract

The invention provides a charging method and device based on real-time feedback control and a charger, and the method comprises the steps: building communication connection with power supply equipment and to-be-charged equipment based on a protocol, and collecting the target electric state quantity of the to-be-charged equipment; extracting state data of to-be-charged equipment and state data of power supply equipment, constructing a charging prediction control model, and constructing a feedback regulation model according to the charging prediction control model; performing iterative optimization on the charging prediction control model and the feedback adjustment model to generate a charging strategy model; and obtaining a charging strategy based on the charging strategy model, and charging the to-be-charged equipment to the target power state quantity based on the charging strategy, so that the effect of balance adjustment of the power supply equipment and the power output quantity of the power supply equipment is realized, the effect of smooth increase of the power change curve of the to-be-charged equipment is achieved, and the stable increase of the power change is realized. Therefore, over-high voltage fluctuation in the battery is avoided, and the minimum loss of the battery is realized on the premise of ensuring the charging efficiency.

Description

Technical Field

[0001] The present invention relates to the field of intelligent charging technology, and in particular to a charging method, device and charger based on real-time feedback control. Background Art

[0002] The battery charging process has the greatest impact on the battery life, while the discharge process has less impact. During the charging process, the chemical reaction inside the battery may cause voltage fluctuations, which means that most batteries are not damaged by use, but by "charged". This shows that a good charger has a very important impact on the battery life.

[0003] At present, there are many electric vehicle chargers on the market, and their performance varies with different manufacturers. Some use linear transformers to step down and then rectify, some use half-bridge power conversion circuits, and some use single-ended flyback power conversion circuits, etc. However, basically the circuits are relatively simple, the protection function is poor, and the charging is not performed according to the battery charging curve, which may affect the battery life. At the same time, when the battery power changes, it will affect the battery charging efficiency. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the deficiencies in the prior art, the present invention provides a charging method, device and charger based on real-time feedback control to solve the problems raised in the above background technology.

[0006] (II) Technical solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: A charging method based on real-time feedback control, comprising the following steps:

[0008] Establish a communication connection with the power supply device and the device to be charged based on the protocol, and collect the target power state quantity of the device to be charged;

[0009] Extract the status data of the device to be charged and the status data of the power supply device, build a charging prediction control model, and build a feedback regulation model based on the charging prediction control model;

[0010] Among them, the status data of the device to be charged includes the power data before charging, the charging time and the charging power change data, and the status data of the power supply device includes the power output of the power supply device;

[0011] Iteratively optimize the charging prediction control model and feedback regulation model to generate a charging strategy model;

[0012] Based on the charging strategy model, a charging strategy is obtained, and the device to be charged is charged to a target power state based on the charging strategy.

[0013] As a preferred embodiment of this embodiment, the building of the charging prediction control model includes:

[0014] The power data before charging, charging time and charging power change data are used to calculate the power state curve of the device to be charged and the state quantity S of the device to be charged at time t. t ;

[0015] The power output of the power supply equipment is used to calculate the power output curve of the power supply equipment and the supply amount L of the power supply equipment at time t t ;

[0016] The state of charge curve of the device to be charged and the state quantity S of the device to be charged at time t t The power output of the device to be powered and the power supply L at time t t The data training set is input into the decision tree model for training, and the output result of the decision tree model is output as a charging prediction control model.

[0017] As a preferred embodiment of this embodiment, the constructing of the feedback regulation model includes:

[0018] The supply quantity L of the power supply equipment at time t t Calculate the state quantity S of the device to be charged at time t+1 t+1 ;

[0019] According to the supply quantity L of the power supply equipment at time t t , the state quantity S of the device to be charged at time t+1 t+1 and the charge state change curve of the device to be charged from t to t+1, and calculate the correction function W (L t , S t );

[0020] According to the state quantity S of the device to be charged at time t+1 t+1 , power supply equipment t~t+1 time power output curve and correction function W (L t , S t ), calculate the feedback function P(S t+1 , L t ).

[0021] As a preferred embodiment of this embodiment, the correction function

[0022] W(L t , S t )=S t +µL t

[0023] µ represents the correction factor;

[0024] The feedback function P(S t+1 , L t ) = [(S t+1 -S t )-(S G t+1 -S t )]*γ

[0025] γ represents the feedback coefficient, S G t+1 Indicates the state quantity of the device to be charged at time t+1 after correction.

[0026] As a preferred embodiment of this embodiment, the iterative optimization of the charging prediction control model and the feedback regulation model includes:

[0027] The state quantity S of the device to be charged at time t t , the supply quantity L of the power supply equipment at time t t , correction function W(L t , S t ) and the feedback function P(S t+1 , L t ) are supplemented with the corresponding data sets;

[0028] Cluster these data sets respectively and extract the clustered sample clusters as training data;

[0029] The charging prediction control model and the feedback regulation model are trained respectively based on the training data until the iteration time and number reach the maximum value.

[0030] As a preferred embodiment of this embodiment, a charging device based on real-time feedback control is used to implement the above-mentioned charging method based on real-time feedback control, including:

[0031] The data interaction module establishes a communication connection with the power supply device and the device to be charged based on the protocol, and collects the target power state quantity of the device to be charged;

[0032] A model building module extracts the status data of the device to be charged and the status data of the power supply device, builds a charging prediction control model, and builds a feedback regulation model based on the charging prediction control model;

[0033] Among them, the status data of the device to be charged includes the power data before charging, the charging time and the charging power change data, and the status data of the power supply device includes the power output of the power supply device;

[0034] The strategy generation module iteratively optimizes the charging prediction control model and the feedback regulation model to generate a charging strategy model;

[0035] The charging module obtains the charging strategy based on the charging strategy model, and charges the device to be charged to a target power state based on the charging strategy.

[0036] As a preferred embodiment of this embodiment, the process of the model building module building a charging prediction control model includes:

[0037] The power data before charging, charging time and charging power change data are used to calculate the power state curve of the device to be charged and the state quantity S of the device to be charged at time t. t ;

[0038] The power output of the power supply equipment is used to calculate the power output curve of the power supply equipment and the supply amount L of the power supply equipment at time t t ;

[0039] The state of charge curve of the device to be charged and the state quantity S of the device to be charged at time t t The power output of the device to be powered and the power supply L at time t t The data training set is input into the decision tree model for training, and the output result of the decision tree model is output as a charging prediction control model.

[0040] As a preferred embodiment of this embodiment, the process of constructing the feedback adjustment model by the model construction module includes:

[0041] The supply quantity L of the power supply equipment at time t t Calculate the state quantity S of the device to be charged at time t+1 t+1 ;

[0042] According to the supply quantity L of the power supply equipment at time t t , the state quantity S of the device to be charged at time t+1 t+1 And the charge state change curve of the device to be charged from t to t+1, calculate the correction function

[0043] W(L t , S t )=S t +µL t

[0044] µ represents the correction factor;

[0045] According to the state quantity S of the device to be charged at time t+1 t+1 , power supply equipment t~t+1 time power output curve and correction function W (L t , S t ), calculate the feedback function

[0046] P(S t+1 , L t) = [(S t+1 -S t )-(S G t+1 -S t )]*γ

[0047] γ represents the feedback coefficient, S G t+1 Indicates the state quantity of the device to be charged at time t+1 after correction.

[0048] As a preferred embodiment of this embodiment, the strategy generation module iteratively optimizes the charging prediction control model and the feedback regulation model, including:

[0049] The state quantity S of the device to be charged at time t t , the supply quantity L of the power supply equipment at time t t , correction function W(L t , S t ) and the feedback function P(S t+1 , L t ) are supplemented with the corresponding data sets;

[0050] Cluster these data sets respectively and extract the clustered sample clusters as training data;

[0051] The charging prediction control model and the feedback regulation model are trained respectively based on the training data until the iteration time and number reach the maximum value.

[0052] As a preferred embodiment of this embodiment, a charger includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the above-mentioned charging method based on real-time feedback control when executing the program.

[0053] (III) Beneficial effects

[0054] The present invention provides a charging method, device and charger based on real-time feedback control, which have the following beneficial effects: constructing a charging prediction control model by extracting status data of a device to be charged and status data of a power supply device, and constructing a feedback regulation model according to the charging prediction control model; iteratively optimizing the charging prediction control model and the feedback regulation model to generate a charging strategy model; obtaining a charging strategy based on the charging strategy model, charging the device to be charged to a target power state quantity based on the charging strategy, determining the corresponding change in the power output of the power supply device when the power data of the device to be charged changes steadily through the relationship between the power output of the device to be charged and the power supply device, so as to achieve the effect of balancing and regulating the power output of the power supply device and the power supply device, and achieving the effect of smooth growth of the power change curve of the device to be charged, and the steadily growing power change prevents excessive voltage fluctuations inside the battery, so as to achieve minimum battery loss under the premise of ensuring charging efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 is a flow chart of a charging method based on real-time feedback control of the present invention;

[0056] Figure 2 It is a block diagram of a charging device based on real-time feedback control of the present invention. DETAILED DESCRIPTION

[0057] The embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0058] The disclosure below provides many different embodiments or examples to realize different structures of the present invention. In order to simplify the disclosure of the present invention, the parts and settings of specific examples are described below. Of course, they are only examples, and the purpose is not to limit the present invention. In addition, the present invention can repeat reference numbers and / or reference letters in different examples, and this repetition is for the purpose of simplicity and clarity, which itself does not indicate the relationship between the various embodiments and / or settings discussed. In addition, the present invention provides various specific examples of processes and materials, but those of ordinary skill in the art can appreciate the application of other processes and / or the use of other materials.

[0059] like Figure 1 As shown, an embodiment of the present invention provides a charging method based on real-time feedback control, comprising the following steps:

[0060] Establish a communication connection with the power supply device and the device to be charged based on the protocol, and collect the target power state quantity of the device to be charged;

[0061] Extract the status data of the device to be charged and the status data of the power supply device, build a charging prediction control model, and build a feedback regulation model based on the charging prediction control model;

[0062] Among them, the status data of the device to be charged includes the power data before charging, the charging time and the charging power change data, and the status data of the power supply device includes the power output of the power supply device;

[0063] Iteratively optimize the charging prediction control model and feedback regulation model to generate a charging strategy model;

[0064] Based on the charging strategy model, a charging strategy is obtained, and the device to be charged is charged to a target power state based on the charging strategy.

[0065] Specifically, by collecting data from the device to be charged and the power supply device, data support is provided for subsequent control, and a charging prediction control model is constructed based on the collected data, wherein the charging prediction control model is used to display the relationship between the power output of the device to be charged and the power supply device, and further determine the change in power data of the device to be charged over time during charging. At the same time, the feedback adjustment model is used to reversely infer the power output of the power supply device when the power data of the device to be charged changes uniformly over time during charging, thereby completing stable charging of the device to be powered.

[0066] Furthermore, the construction of the charging prediction control model includes:

[0067] The power data before charging, charging time and charging power change data are used to calculate the power state curve of the device to be charged and the state quantity S of the device to be charged at time t. t ;

[0068] The power output of the power supply equipment is used to calculate the power output curve of the power supply equipment and the supply amount L of the power supply equipment at time t t ;

[0069] The state of charge curve of the device to be charged and the state quantity S of the device to be charged at time t t The power output of the device to be powered and the power supply L at time t t The data training set is input into the decision tree model for training, and the output result of the decision tree model is output as a charging prediction control model.

[0070] The charging prediction control model is based on the analysis of the historical charging data of the device to be charged. It mainly determines the state curve of the device to be charged and the state quantity S of the device to be charged at time t based on the power data before charging, charging time and charging power change data. tAt the same time, the power output of the power supply equipment calculates the power output curve of the power supply equipment and the supply amount L of the power supply equipment at time t t After training these data through the decision tree model, the relationship between the power output of the power supply equipment and the charging data of the device to be charged can be simulated.

[0071] The charging data of the device to be charged may be charging time, change in the amount of power of the device to be charged over time, and other data related to charging.

[0072] Furthermore, the constructing of the feedback regulation model includes:

[0073] The supply quantity L of the power supply equipment at time t t Calculate the state quantity S of the device to be charged at time t+1 t+1 ;

[0074] According to the supply quantity L of the power supply equipment at time t t , the state quantity S of the device to be charged at time t+1 t+1 and the charge state change curve of the device to be charged from t to t+1, and calculate the correction function W (L t , S t );

[0075] According to the state quantity S of the device to be charged at time t+1 t+1 , power supply equipment t~t+1 time power output curve and correction function W (L t , S t ), calculate the feedback function P(S t+1 , L t ).

[0076] Furthermore, the correction function

[0077] W(L t , S t )=S t +µL t

[0078] µ represents the correction factor;

[0079] The feedback function P(S t+1 , L t ) = [(S t+1 -S t )-(S G t+1 -S t )]*γ

[0080] γ represents the feedback coefficient, S G t+1 Indicates the state quantity of the device to be charged at time t+1 after correction.

[0081] Specifically, through the relationship between the charging data of the device to be charged and the power output of the power supply device, the change in the power output of the power supply device when the charging data of the device to be charged smoothly transitions during charging can be determined.

[0082] Furthermore, the iterative optimization of the charging prediction control model and the feedback regulation model includes:

[0083] The state quantity S of the device to be charged at time t t , the supply quantity L of the power supply equipment at time t t , correction function W(L t , S t ) and the feedback function P(S t+1 , L t ) are supplemented with the corresponding data sets;

[0084] Cluster these data sets respectively and extract the clustered sample clusters as training data;

[0085] The charging prediction control model and the feedback regulation model are trained respectively based on the training data until the iteration time and number reach the maximum value.

[0086] Among them, the charging prediction control model and feedback regulation model can be further optimized through continuous iterative optimization.

[0087] The charging method based on real-time feedback control of the present invention constructs a charging prediction control model by extracting the status data of the device to be charged and the status data of the power supply device, and constructs a feedback regulation model according to the charging prediction control model; iteratively optimizes the charging prediction control model and the feedback regulation model to generate a charging strategy model; based on the charging strategy model, a charging strategy is obtained, and the device to be charged is charged to a target power state quantity based on the charging strategy, and the corresponding change of the power output of the power supply device when the power data of the device to be charged changes steadily is determined through the relationship between the power output of the device to be charged and the power supply device, so as to achieve the effect of balancing and regulating the power output of the power supply device and the power supply device, and achieve the effect of smooth growth of the power change curve of the device to be charged, and the steadily growing power change prevents excessive voltage fluctuations inside the battery, so as to achieve minimum loss of the battery under the premise of ensuring charging efficiency.

[0088] like Figure 2 As shown, the present invention also provides a charging device based on real-time feedback control, which is used to implement the above-mentioned charging method based on real-time feedback control, including:

[0089] The data interaction module establishes a communication connection with the power supply device and the device to be charged based on the protocol, and collects the target power state quantity of the device to be charged;

[0090] A model building module extracts the status data of the device to be charged and the status data of the power supply device, builds a charging prediction control model, and builds a feedback regulation model based on the charging prediction control model;

[0091] Among them, the status data of the device to be charged includes the power data before charging, the charging time and the charging power change data, and the status data of the power supply device includes the power output of the power supply device;

[0092] The strategy generation module iteratively optimizes the charging prediction control model and the feedback regulation model to generate a charging strategy model;

[0093] The charging module obtains the charging strategy based on the charging strategy model, and charges the device to be charged to a target power state based on the charging strategy.

[0094] Furthermore, the process of constructing the charging prediction control model by the model construction module includes:

[0095] The power data before charging, charging time and charging power change data are used to calculate the power state curve of the device to be charged and the state quantity S of the device to be charged at time t. t ;

[0096] The power output of the power supply equipment is used to calculate the power output curve of the power supply equipment and the supply amount L of the power supply equipment at time t t ;

[0097] The state of charge curve of the device to be charged and the state quantity S of the device to be charged at time t t The power output of the device to be powered and the power supply L at time t t The data training set is input into the decision tree model for training, and the output result of the decision tree model is output as a charging prediction control model.

[0098] Furthermore, the process of constructing the feedback adjustment model by the model construction module includes:

[0099] The supply quantity L of the power supply equipment at time t t Calculate the state quantity S of the device to be charged at time t+1 t+1 ;

[0100] According to the supply quantity L of the power supply equipment at time t t , the state quantity S of the device to be charged at time t+1 t+1 And the charge state change curve of the device to be charged from t to t+1, calculate the correction function

[0101] W(L t , S t )=St +µL t

[0102] µ represents the correction factor;

[0103] According to the state quantity S of the device to be charged at time t+1 t+1 , power supply equipment t~t+1 time power output curve and correction function W (L t , S t ), calculate the feedback function

[0104] P(S t+1 , L t ) = [(S t+1 -S t )-(S G t+1 -S t )]*γ

[0105] γ represents the feedback coefficient, S G t+1 Indicates the state quantity of the device to be charged at time t+1 after correction.

[0106] Furthermore, the strategy generation module iteratively optimizes the charging prediction control model and the feedback regulation model, including:

[0107] The state quantity S of the device to be charged at time t t , the supply quantity L of the power supply equipment at time t t , correction function W(L t , S t ) and the feedback function P(S t+1 , L t ) are supplemented with the corresponding data sets;

[0108] Cluster these data sets respectively and extract the clustered sample clusters as training data;

[0109] The charging prediction control model and the feedback regulation model are trained respectively based on the training data until the iteration time and number reach the maximum value.

[0110] The present invention also provides a charger, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the above-mentioned charging method based on real-time feedback control when executing the program.

[0111] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A charging method based on real-time feedback control, characterized in that: The following steps are involved: Establish a communication connection with the power supply device and the device to be charged based on the protocol, and collect the target power state quantity of the device to be charged; Extract the status data of the device to be charged and the status data of the power supply device, build a charging prediction control model, and build a feedback regulation model based on the charging prediction control model; Among them, the status data of the device to be charged includes the power data before charging, the charging time and the charging power change data, and the status data of the power supply device includes the power output of the power supply device; Iteratively optimize the charging prediction control model and feedback regulation model to generate a charging strategy model; Based on the charging strategy model, a charging strategy is obtained, and the device to be charged is charged to a target power state based on the charging strategy.

2. A charging method based on real-time feedback control according to claim 1, characterized in that: The construction of the charging prediction control model comprises: The power data before charging, charging time and charging power change data are used to calculate the power state curve of the device to be charged and the state quantity S of the device to be charged at time t. t ; The power output of the power supply equipment is used to calculate the power output curve of the power supply equipment and the supply amount L of the power supply equipment at time t t ; The state of charge curve of the device to be charged and the state quantity S of the device to be charged at time t t The power output of the device to be powered and the power supply L at time t t The data training set is input into the decision tree model for training, and the output result of the decision tree model is output as a charging prediction control model.

3. A charging method based on real-time feedback control according to claim 2, characterized in that: The constructing of the feedback regulation model comprises: The supply quantity L of the power supply equipment at time t t Calculate the state quantity S of the device to be charged at time t+1 t+1 ; According to the supply quantity L of the power supply equipment at time t t , the state quantity S of the device to be charged at time t+1 t+1 and the charge state change curve of the device to be charged from t to t+1, and calculate the correction function W (L t , S t ); According to the state quantity S of the device to be charged at time t+1 t+1 , power supply equipment t~t+1 time power output curve and correction function W (L t , S t ), calculate the feedback function P(S t+1 , L t ).

4. A charging method based on real-time feedback control according to claim 3, characterized in that: The correction function W(L t , S t )=S t +µL t µ represents the correction factor; The feedback function P(S t+1 , L t ) = [(S t+1 -S t )-(S G t+1 -S t )]*γ γ represents the feedback coefficient, S G t+1 Indicates the state quantity of the device to be charged at time t+1 after correction.

5. A charging method based on real-time feedback control according to claim 4, characterized in that: The iterative optimization of the charging prediction control model and the feedback regulation model includes: The state quantity S of the device to be charged at time t t , the supply quantity L of the power supply equipment at time t t , correction function W(L t , S t ) and the feedback function P(S t+1 , L t ) are supplemented with the corresponding data sets; Cluster these data sets respectively and extract the clustered sample clusters as training data; The charging prediction control model and the feedback regulation model are trained respectively based on the training data until the iteration time and number reach the maximum value.

6. A charging device based on real-time feedback control, used to implement the charging method based on real-time feedback control according to any one of claims 1 to 5, characterized in that: include: The data interaction module establishes a communication connection with the power supply device and the device to be charged based on the protocol, and collects the target power state quantity of the device to be charged; A model building module extracts the status data of the device to be charged and the status data of the power supply device, builds a charging prediction control model, and builds a feedback regulation model based on the charging prediction control model; Among them, the status data of the device to be charged includes the power data before charging, the charging time and the charging power change data, and the status data of the power supply device includes the power output of the power supply device; The strategy generation module iteratively optimizes the charging prediction control model and the feedback regulation model to generate a charging strategy model; The charging module obtains the charging strategy based on the charging strategy model, and charges the device to be charged to a target power state based on the charging strategy.

7. A charging device based on real-time feedback control according to claim 6, characterized in that: The process of constructing the charging prediction control model by the model construction module includes: The power data before charging, charging time and charging power change data are used to calculate the power state curve of the device to be charged and the state quantity S of the device to be charged at time t. t ; The power output of the power supply equipment is used to calculate the power output curve of the power supply equipment and the supply amount L of the power supply equipment at time t t ; The state of charge curve of the device to be charged and the state quantity S of the device to be charged at time t t The power output of the device to be powered and the power supply L at time t t The data training set is input into the decision tree model for training, and the output result of the decision tree model is output as a charging prediction control model.

8. A charging device based on real-time feedback control according to claim 7, characterized in that: The process of constructing the feedback adjustment model by the model construction module includes: The supply quantity L of the power supply equipment at time t t Calculate the state quantity S of the device to be charged at time t+1 t+1 ; According to the supply quantity L of the power supply equipment at time t t , the state quantity S of the device to be charged at time t+1 t+1 And the charge state change curve of the device to be charged from t to t+1, calculate the correction function W(L t ,S t )=S t +µL t µ represents the correction factor; According to the state quantity S of the device to be charged at time t+1 t+1 , power supply equipment t~t+1 time power output curve and correction function W (L t , S t ), calculate the feedback function P(S t+1 ,L t )=[(S t+1 -S t )-(S G t+1 -S t )]*γ γ represents the feedback coefficient, S G t+1 Indicates the state quantity of the device to be charged at time t+1 after correction.

9. A charging device based on real-time feedback control according to claim 8, characterized in that: The strategy generation module iteratively optimizes the charging prediction control model and the feedback regulation model, including: The state quantity S of the device to be charged at time t t , the supply quantity L of the power supply equipment at time t t , correction function W(L t , S t ) and the feedback function P(S t+1 , L t ) are supplemented with the corresponding data sets; Cluster these data sets respectively and extract the clustered sample clusters as training data; The charging prediction control model and the feedback regulation model are trained respectively based on the training data until the iteration time and number reach the maximum value.

10. A charger, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the charging method based on real-time feedback control according to any one of claims 1 to 5 is implemented when the processor executes the program.

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