Storage battery electric quantity endurance time prediction method based on grey prediction model

Abstract

The invention relates to the technical field of storage battery electric quantity prediction, in particular to a storage battery electric quantity endurance time prediction method based on a grey prediction model. The method comprises the following steps; the method comprises the following steps: S1, acquiring storage battery electric quantity data and controller power consumption data; s2, constructing a residual electric quantity sequence X (t) of the electric quantity of the storage battery; constructing controller power consumption data use time x (i) based on the storage battery electric quantity residual electric quantity sequence X (t); s3, constructing an accumulation sequence model; s4, constructing a data matrix; s5, solving an inversion matrix; s6, solving an estimated value; step S7, calculating a fitting value; and step S8, performing inspection and prediction. By means of the method, the real-time use condition of the electric quantity of the storage battery can be dynamically checked by a user on the mobile phone, and the use time of the application (controller) capable of being supported by the electric quantity of the storage battery at a certain specific time point can be accurately calculated.

Description

Technical Field

[0001] The present invention relates to the technical field of battery power prediction, and in particular to a method for predicting battery power endurance time based on a grey prediction model. Background Art

[0002] The usage of car battery power and the power consumption of applications have always been issues of concern to users. For example, users can view the power consumption of related controllers through mobile phone applications, or roughly estimate through changes in battery life. Although the above method can roughly estimate the power usage of the controller, for users, whether the remaining battery power can support the user to operate a certain controller, or how long the remaining battery power can support the use of a certain controller; furthermore, when users use the car, most of the controllers in the car will be in a working state at the same time, so it is more important to know how long the car battery life can support the use of the application (controller). Summary of the invention

[0003] In view of the defect in the prior art that users cannot accurately know the battery life of a car that can support the use time of an application (controller), the present invention provides a method for predicting the battery life based on a grey prediction model.

[0004] In order to solve the above technical problems, the present invention is solved by the following technical solutions:

[0005] A method for predicting battery life based on a grey prediction model comprises the following steps:

[0006] Step S1, obtaining battery power data and controller power consumption data;

[0007] Step S2, constructing a battery power remaining power sequence X(t); constructing a controller power consumption data usage time x(i) based on the battery power remaining power sequence X(t);

[0008] Step S3, constructing a cumulative sequence model;

[0009] Step S4, constructing a data matrix;

[0010] Step S5, finding the inverse matrix;

[0011] Step S6, obtaining an estimated value;

[0012] Step S7, calculating the fitting value; and,

[0013] Step S8: Testing and prediction.

[0014] As a preferred solution of the present invention, in step S2,

[0015] X(t)=[X(1),X(2),X(3),…X(t)]; t=1, 2…;

[0016] Among them, X(t) represents the real-time battery power;

[0017] Set x (0) The controller power consumption data usage time x(i) under the real-time battery power X(t);

[0018] x (0) (n) = [x (0) (1),x (0) (2),x (0) (3),…x (0) (n)]; n = 1, 2, ...;

[0019] Among them, x (0) (n) represents the usage time of the power consumption data of the X(t) controller under the battery power.

[0020] As a preferred solution of the present invention, in step S3, x in step 2 (0) (n) performing accumulation;

[0021]

[0022] Among them, x (1) (k) is represented by x (0) (n) is generated by one accumulation.

[0023] As a preferred solution of the present invention, in step S4, a grey derivative model is constructed;

[0024] x (1) The grey derivative of (k) is dx (1) k = x (0) k = x (1) (k)―x (1) (k―1);

[0025]

[0026] Construct a data matrix model;

[0027] Set x (0) (k)+az (1) (k) = b;

[0028] Among them, x (0) It is expressed as grey derivative, a is expressed as development coefficient, and b is expressed as grey action;

[0029] set up is the parameter vector to be estimated, that is Then the least squares estimated parameter sequence of the grey differential equation satisfies:

[0030]

[0031] in,

[0032] Then it is called

[0033]

[0034] is the grey differential equation x (0) (k)+ax (1) = the whitening equation of b.

[0035] As a preferred solution of the present invention, the whitening equation x (0) (k)+ax (1) = b to solve; therefore, the time response function is,

[0036]

[0037] As a preferred solution of the present invention, (0) (k)+az (1) (k) = b; therefore, the time response function is,

[0038]

[0039] As a preferred solution of the present invention, take x (1) (0) = x (0) (0); then

[0040]

[0041] So the prediction equation is,

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] 1. When the battery is not charged, it can monitor the energy consumption of the battery in real time and know the working time of the battery, which can largely avoid the battery power shortage;

[0044] 2. Dynamically calculate the real-time battery usage based on the user's mobile phone, and more accurately calculate the battery power at a specific point in time that can support the use of the application (controller). BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 is a flow chart of the prediction method in this embodiment. DETAILED DESCRIPTION

[0046] In order to further understand the content of the present invention, the present invention is described in detail in conjunction with the accompanying drawings and embodiments. It should be understood that the embodiments are only for explaining the present invention and are not intended to limit it.

[0047] Example 1

[0048] like Figure 1 As shown, this embodiment provides a method for predicting battery life based on a grey prediction model, which includes the following steps:

[0049] Step S1, obtaining battery power data and controller power consumption data;

[0050] Step S2, constructing a battery power remaining power sequence X(t); constructing a controller power consumption data usage time x(i) based on the battery power remaining power sequence X(t);

[0051] Step S3, constructing a cumulative sequence model;

[0052] Step S4, constructing a data matrix;

[0053] Step S5, finding the inverse matrix;

[0054] Step S6, obtaining an estimated value;

[0055] Step S7, calculating the fitting value; and,

[0056] Step S8: Testing and prediction.

[0057] In this embodiment, in step S2,

[0058] X(t)=[X(1),X(2),X(3),…X(t)]; t=1, 2…;

[0059] Among them, X(t) represents the real-time battery power;

[0060] Set x (0) The controller power consumption data usage time x(i) under the real-time battery power X(t);

[0061] x (0) (n) = [x (0) (1),x (0) (2),x (0) (3),…x (0) (n)]; n = 1, 2, ...;

[0062] Among them, x (0) (n) represents the usage time of the power consumption data of the X(t) controller under the battery power.

[0063] As a preferred solution of the present invention, in step S3, x in step 2(0) (n) performing accumulation;

[0064]

[0065] Among them, x (1) (k) is represented by x (0) (n) is generated by one accumulation.

[0066] In this embodiment, in step S4, a grey derivative model is constructed;

[0067] x (1) The grey derivative of (k) is dx (1) k = x (0) k = x (1) (k)―x (1) (k―1);

[0068]

[0069] Construct a data matrix model;

[0070] Set x (0) (k)+az (1) (k) = b;

[0071] Among them, x (0) It is expressed as grey derivative, a is expressed as development coefficient, and b is expressed as grey action;

[0072] Let a be the parameter vector to be estimated, that is Then the least squares estimated parameter sequence of the grey differential equation satisfies:

[0073]

[0074]

[0075] Then it is called

[0076]

[0077] is the grey differential equation x (0) (k)+ax (1) = the whitening equation of b.

[0078] In this embodiment, the whitening equation x (0) (k)+ax (1) = b to solve; therefore, the time response function is,

[0079]

[0080] In this embodiment, x (0) (k)+az (1)(k) = b; therefore, the time response function is,

[0081]

[0082] In this embodiment, take x (1) (0) = x (0) (0); then

[0083]

[0084] So the prediction equation is,

[0085] Compared with the prior art, the beneficial effects of this embodiment are:

[0086] 1. When the battery is not charged, it can monitor the energy consumption of the battery in real time and know the working time of the battery, which can largely avoid the battery power shortage;

[0087] 2. Dynamically calculate the real-time battery usage based on the user's mobile phone, and more accurately calculate the battery power at a specific point in time that can support the use of the application (controller).

[0088] In conclusion, the above is only a preferred embodiment of the present invention, and all equivalent changes and modifications made according to the scope of the patent application of the present invention should fall within the scope of the patent of the present invention.

Claims

1. A method for predicting battery life based on a grey prediction model, comprising the following steps; Step S1, obtaining battery power data and controller power consumption data; Step S2, constructing a battery power remaining power sequence X(t); constructing a controller power consumption data usage time x(i) based on the battery power remaining power sequence X(t); Step S3, constructing a cumulative sequence model; Step S4, constructing a data matrix; Step S5, finding the inverse matrix; Step S6, obtaining an estimated value; Step S7, calculating the fitting value; and, Step S8: Testing and prediction.

2. The method for predicting battery life based on a grey prediction model according to claim 1 is characterized in that: In step S2, X(t)=[X(1),X(2),X(3),…X(t)]; t=1, 2…; Among them, X(t) represents the real-time battery power; Set x (0) The controller power consumption data usage time x(i) under the real-time battery power X(t); x (0) (n)=[x (0) (1),x (0) (2),x (0) (3),…x (0) (n)];n=1、2……; Among them, x (0) (n) represents the usage time of the power consumption data of the X(t) controller under the battery power.

3. The method for predicting battery life based on a grey prediction model according to claim 2 is characterized in that: In step S3, for x in step 2 (0) (n) performing accumulation; Among them, x (1) (k) is represented by x (0) (n) is generated by one accumulation.

4. The method for predicting battery life based on a grey prediction model according to claim 3 is characterized in that: In step S4, a grey derivative model is constructed; x (1) The grey derivative of (k) is dx (1) k = x (0) k = x (1) (k)-x (1) (k-1); Construct a data matrix model; Set x (0) (k)+az (1) (k) = b; Among them, x (0) It is expressed as grey derivative, a is expressed as development coefficient, and b is expressed as grey action; set up is the parameter vector to be estimated, that is Then the least squares estimated parameter sequence of the grey differential equation satisfies: in, Then it is called is the grey differential equation x (0) (k)+ax (1) = the whitening equation of b.

5. The method for predicting battery life based on a grey prediction model according to claim 4 is characterized in that: Whiten equation x (0) (k)+ax (1) = b to solve; therefore, the time response function is, 6. The method for predicting battery life based on a grey prediction model according to claim 4, characterized in that: x (0) (k)+az (1) (k) = b; therefore, the time response function is, 7. The method for predicting battery life based on a grey prediction model according to claim 6, characterized in that: Take x (1) (0) = x (0) (0); then So the prediction equation is,

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