A method for correcting estimated current values ​​of parallel branches of energy storage battery system

Abstract

The present invention discloses a method for correcting the estimated value of parallel branch current of an energy storage battery system, which obtains the trunk circuit and the estimated value of branch current, and corrects the estimated value of branch current. Compared with the prior art, the beneficial effect of the present invention is that by recording different changes of trunk current and the corresponding absolute error value, the proportional coefficient between the error variable and the trunk current is determined, and the error variable is subtracted from the estimated value of branch current to obtain the corrected branch current. The correction method used is novel, and the correction process is simple and intuitive.

Description

Technical Field

[0001] The invention relates to the technical field of batteries, and in particular to a method for correcting estimated current values ​​of parallel branches of an energy storage battery system. Background Art

[0002] Lithium-ion batteries are widely used in electric vehicles due to their high energy density, long cycle life and light weight. In the use of automotive lithium-ion batteries, hundreds or thousands of cells are usually used in series and parallel to meet the needs of high power output. However, due to the inevitable differences in the manufacturing process of each battery cell, the capacity, temperature, degree of aging and other characteristics of each battery cell will vary during use. These differences will cause different branch currents of parallel battery packs, which will in turn cause differences in the depth of charge and discharge of different battery cells. As time goes by, the differences between parallel batteries will further increase, thus affecting the service life of the battery pack and posing safety risks. Therefore, it is very important to estimate the branch current and make appropriate corrections to the estimated value.

[0003] At present, there are few studies on branch current estimation, and therefore even fewer studies on correcting the estimated value of branch current. Although BP neural network is used in the prior art to realize branch current estimation of battery system, there are still some shortcomings. For example, due to the sudden change of main current, the absolute error between the estimated value and the true value of branch current cannot be stably maintained at a low level. Under DST conditions, the maximum absolute error when using BP neural network for branch current estimation is about 2A, which is about 5% of the true value of branch current. The relative error is large, which affects the development of subsequent research. Summary of the invention

[0004] In order to solve the deficiencies in the prior art, the present invention provides a method for correcting the estimated value of the parallel branch current of an energy storage battery system. The estimated value of the branch current can be corrected through the functional relationship between the main current and the absolute error. The correction method used is novel and the correction process is simple and intuitive.

[0005] To achieve the above object, the present invention is implemented through the following technical scheme: A method for correcting the estimated value of the parallel branch current of an energy storage battery system, comprising the following steps:

[0006] A method for correcting an estimated value of a parallel branch current of an energy storage battery system comprises the following steps:

[0007] S1. Obtain the estimated values ​​of the main circuit and two branch currents:

[0008] S2. Correct the estimated values ​​of the two branch currents using the following method:

[0009] When S2.1 corrects the estimated value of branch 1 current:

[0010] If |I(t)|≤α, then the error variable D(t)=0, and the branch current I' 1 (t) = I' 1 (t)+D(t); let t=t+1, and correct the estimated value at the next moment;

[0011] If |I(t)|<-α, then the error variable Branch current I' 1 (t) = I' 1 (t)+D(t); let t=t+1, and correct the estimated value at the next moment;

[0012] If I(t)>α, then the error variable Branch current I' 1 (t) = I' 1 (t)+D(t); let t=t+1, and correct the estimated value at the next moment;

[0013] Here α = 1A, K 1 =100, K 2 =180;

[0014] When S2.2 corrects the estimated value of branch 2 current:

[0015] If |I(t)|≤α, then the error variable D(t)=0; branch current I' 2 (t) = I' 2 (t)-D(t); let t=t+1, and correct the estimated value at the next moment;

[0016] If I(t)>α, then the error variable Branch current I' 2 (t) = I' 2 (t)-D(t); let t=t+1, and correct the estimated value at the next moment;

[0017] If I(t)<-α, then the error variable Branch current I' 2 (t) = I' 2 (t)-D(t); let t=t+1, and correct the estimated value at the next moment;

[0018] Here α = 1A, K 3 =200, K 4 =40.

[0019] Furthermore, the estimated values ​​of the main circuit current and the two branch currents in step S1 are obtained by the following method:

[0020] S1.1, obtain the main circuit current and branch circuit voltage and current of the parallel battery pack under four dynamic working conditions: DST, FUDS, UDDS, and HPPC;

[0021] S1.2. Integrate the main current, two branch voltages and two branch currents under three working conditions into a main current data set I and two branch voltage data sets V. 1 、V 2 , and two branch current data sets I 1 , I 2 , and normalize the integrated data set;

[0022] S1.3, training BP neural network;

[0023] S1.4, the remaining main circuit current I' and the voltage V' of the two branches under one working condition 1 、V' 2 The input feature is input into the trained BP neural network, and the estimated result is denormalized to obtain the estimated value of branch current I' 1 , I' 2 .

[0024] Furthermore, the following method is used in S1.3 to train the BP neural network:

[0025] S1.3.1 Setting the parameters of BP neural network

[0026] S1.3.2 The main current data set I and the two branch voltage data sets V 1 、V 2 As input, the branch current data set I 1 , I 2 As output, the BP neural network is trained to obtain a trained BP neural network.

[0027] Furthermore, the data recorded in step S1 is obtained by the following method: current sensors are connected in series to the main circuit and two branches of the parallel battery pack, and voltage sensors are connected in parallel to the two branches, so as to obtain the main circuit current of the parallel battery pack and the voltage and current of the two branches.

[0028] Furthermore, the Min-Max normalization method is used in S1.2 to normalize the data set.

[0029] Compared with the prior art, the beneficial effects of the present invention are:

[0030] The present invention records different changes in the main current and the corresponding absolute error values, determines the proportionality coefficient between the error variable and the main current, and subtracts the error variable from the branch current estimate to obtain the corrected branch current. The correction method used is novel and the correction process is simple and intuitive. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Attached Figure 1 It is a parallel battery pack model of the present invention;

[0032] Attached Figure 2 It is the structure of the BP neural network of the present invention;

[0033] Attached Figure 3 is a flow chart of estimated branch current values ​​of the present invention;

[0034] Attached Figure 4 is a flow chart of the present invention for correcting the estimated value;

[0035] Attached Figure 5 It is a schematic diagram of the correction result of the present invention. DETAILED DESCRIPTION

[0036] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms fall within the scope limited by the application equally.

[0037] The present invention discloses a method for correcting the estimated value of the parallel branch current of an energy storage battery system.

[0038] A method for correcting an estimated value of a parallel branch current of an energy storage battery system comprises the following steps:

[0039] S1. Obtain the estimated values ​​of the main circuit and two branch currents:

[0040] S2. Correct the estimated values ​​of the two branch currents using the following method:

[0041] When S2.1 corrects the estimated value of branch 1 current:

[0042] If |I(t)|≤α, then the error variable D(t)=0, and the branch current I' 1 (t) = I' 1 (t)+D(t); let t=t+1, and correct the estimated value at the next moment;

[0043] If I(t)<-α, then the error variable Branch current I' 1 (t) = I' 1(t)+D(t); let t=t+1, and correct the estimated value at the next moment;

[0044] If I(t)>α, then the error variable Branch current I' 1 (t) = I' 1 (t)+D(t); let t=t+1, and correct the estimated value at the next moment;

[0045] Here α = 1A, K 1 =100, K 2 =180;

[0046] When S2.2 corrects the estimated value of branch 2 current:

[0047] If |I(t)|≤α, then the error variable D(t)=0; branch current I' 2 (t) = I' 2 (t)-D(t); let t=t+1, and correct the estimated value at the next moment;

[0048] If I(t)>α, then the error variable Branch current I' 2 (t) = I' 2 (t)-D(t); let t=t+1, and correct the estimated value at the next moment;

[0049] If I(t)<-α, then the error variable Branch current I' 2 (t) = I' 2 (t)-D(t); let t=t+1, and correct the estimated value at the next moment;

[0050] Here α = 1A, K 3 =200, K 4 =40.

[0051] As an optimization, the estimated values ​​of the main circuit current and the two branch currents in step S1 are obtained by the following method:

[0052] S1.1, obtain the main circuit current and branch circuit voltage and current of the parallel battery pack under four dynamic working conditions: DST, FUDS, UDDS, and HPPC;

[0053] S1.2. Integrate the main current, two branch voltages and two branch currents under three working conditions into a main current data set I and two branch voltage data sets V. 1 、V 2 , and two branch current data sets I 1 , I 2, and normalize the integrated data set;

[0054] S1.3, training BP neural network;

[0055] S1.4, the remaining main circuit current I' and the voltage V' of the two branches under one working condition 1 、V' 2 The input feature is input into the trained BP neural network, and the estimated result is denormalized to obtain the estimated value of branch current I' 1 , I' 2 .

[0056] As an optimization, the following method is used in S1.3 to train the BP neural network:

[0057] S1.3.1 Setting the parameters of BP neural network

[0058] S1.3.2 The main current data set I and the two branch voltage data sets V 1 、V 2 As input, the branch current data set I 1 , I 2 As output, the BP neural network is trained to obtain a trained BP neural network.

[0059] As an optimization, the data recorded in step S1 is obtained by the following method: current sensors are connected in series to the main circuit and two branches of the parallel battery pack, and voltage sensors are connected in parallel to the two branches, so as to obtain the main circuit current of the parallel battery pack and the voltage and current of the two branches.

[0060] As an optimization, the Min-Max normalization method is used in S1.2 to normalize the data set.

[0061] In order to more clearly understand the method of the present invention, the following is a detailed description with reference to the accompanying drawings:

[0062] like Figure 1 As shown, the parallel battery pack uses two lithium-ion cells with a nominal capacity of 30.244Ah. 1 and 29.927Ah lithium-ion battery Cell 2 In the experiment, current sensors are connected in series in the main circuit and one branch circuit respectively, and voltage sensors are connected in parallel in the two branches circuit respectively, so as to obtain the main circuit current of the parallel battery pack and the voltage of the two branches. The Arbin sampling time is fixed to 1s, and the dspace sampling time is fixed to 50ms.

[0063] like Figure 3The estimated values ​​of the branch circuits are obtained as shown in the following: S1.1, obtaining the trunk current and branch voltage and current of the parallel battery pack under four dynamic working conditions: DST, FUDS, UDDS, and HPPC;

[0064] S1.2. Integrate the main current and two branch voltages under the three working conditions of DST, UDDS and HPPC, as well as the two branch currents l into a main current data set I and two branch voltage data sets V 1 、V 2 , and two branch current data sets I 1 , I 2 The integrated data set is normalized and the Min-Max normalization method is used to achieve data set normalization.

[0065] S1.3, set the parameters of the BP neural network, and transform the main current data set I and the two branch voltage data sets V 1 、V 2 As input, the branch current data set I 1 , I 2 As output, the BP neural network is trained to obtain a trained BP neural network;

[0066] S1.4. The main circuit current I under FUDS working condition FUDS And the voltage of the two branches V 1-FUDS 、V 2-FUDS As input features, it is input into the trained BP neural network. After the estimation result is obtained, the result is denormalized to obtain the estimated value of branch current I under FUDS working condition. 1-FUDS , I 2-FUDS ;

[0067] S1.5. Correct the estimated branch current value.

[0068] As an optimization, the BP algorithm in S1.3 uses the chain rule to calculate the derivative from the last layer, and then back propagates through the entire network to obtain the derivative of the hidden layer using the chain rule. BP is a supervised learning because each training vector has a target vector to match. The BP neural network uses the BP algorithm, and its structure is shown in the attached figure. Figure 2 As shown in Figure 1. The structure of the BP neural network is divided into three parts: a single input layer, a single or multiple hidden layers, and a single output layer. The number of nodes in different types of layers can be set as needed. j represents the jth node in the input layer, j = 1, ..., m. i，j represents the weight between the i-th node in the hidden layer and the j-th node in the input layer, θ i is the threshold of the i-th node in the hidden layer. is the activation function of the hidden layer.k，i represents the weight between the kth node in the output layer and the ith node in the hidden layer, i = 1, ..., q. a k represents the threshold of the output layer node k, C represents the activation function of the output layer, o k is the output of node k.

[0069] The structure of BP neural network is divided into three parts: an input layer, one or more hidden layers and an output layer. The number of nodes in different types of layers can be set as needed. 1 , x i , x k , x m represents the input feature. 1 , f 2 Represents the activation function. 1 ,y k ,y l Represents output parameters. i，j , w k，i Represents the weights between different nodes. 1，1 ,θ 1，2 ,θ 1，i ,θ 1，j ,θ 1，q ,θ 2，1 ,θ 2，k ,θ 2，l Represents the threshold of different nodes. Set the parameters of the BP neural network, the number of input layer nodes is 3, the number of hidden layer nodes is 10, the number of output layer nodes is 2, the activation function between the input layer and the hidden layer is logsig, the activation function between the hidden layer and the output layer is purelin, the training algorithm is trainlm, the number of network iterations is set to 1000, the network training accuracy is 0.000027, the learning rate is set to 0.01, and the training ends when the error of the verification sample does not decrease for 20 consecutive times. Then the main current data set I and the two branch voltage data sets V 1 、V 2 As input, the branch current data set I 1 , I 2 As the output, the BP neural network is trained to obtain a trained BP neural network. -DST And the voltage of the two branches V 1-DST 、V 2-DST Input as input features into the trained BP neural network.

[0070] like Figure 4 The branch current estimate is corrected as shown in the figure: the error variable D(t) and the main current value I are introduced FUDS , let the initial value D(1) = 0, let the initial value of the time variable t be 2, and input the main current IFUDS (t);

[0071] When correcting the estimated current value of branch 1:

[0072] If, |I FUDS (t)|≤α, let D(t)=0, the estimated branch current

[0073] I 1-FUDS (t) = I 1-FUDS (t)+D(t); let t=t+1, and correct the estimated value at the next moment;

[0074] If I FUDS (t)<-α, let Branch current estimate

[0075] I 1-FUDS (t) = I 1-FUDS (t)+D(t). Let t=t+1, and correct the estimated value at the next moment;

[0076] If I FUDS (t)>α, let Branch current estimate

[0077] I 1-FUDS (t) = I 1-FUDS (t)+D(t); let t=t+1, and correct the estimated value at the next moment;

[0078] Here α = 1A, K 1 =100, K 2 =180.

[0079] When correcting the estimated current value of branch 2:

[0080] If, |I FUDS (t)|≤α, let D(t)=0, the estimated branch current

[0081] I 2-FUDS (t) = I 2-FUDS (t)-D(t); let t=t+1, and correct the estimated value at the next moment;

[0082] If I FUDS (t)<-α, let Branch current estimate

[0083] I 2-FUDS (t) = I 2-FUDS (t)-D(t); let t=t+1, and correct the estimated value at the next moment;

[0084] If I FUDS(t)>α, let Branch current estimate

[0085] I 2-FUDS (t) = I 2-FUDS (t)-D(t); let t=t+1, and correct the estimated value at the next moment;

[0086] Here α = 1A, K 3 =200, K 4 =40.

[0087] In order to more clearly understand the correction method of the present invention, the following further illustrates:

[0088] Assume that the estimated current values ​​of branch 1 at two consecutive moments are I 1-FUDS (t)=35.6A,I 1-FUDS (t+1)=37.8A. Main circuit current I under FUDS condition FUDS (t)=50.2A,I FUDS (t+1)=-26.7A. When correcting at time t, because I FUDS (t)>α, then Therefore, the correction value of the estimated branch current at this moment is I 1-FUDS (t) = 35.6 + 0.28 = 35.88A. When correcting at time t+1, because I FUDS (t+1)<-α Therefore, the correction value of the estimated branch current at this moment is I 1-FUDS (t+1)=37.8-(-0.67)=38.47A.

Claims

1. A correction method for estimating the current of parallel branches of an energy storage battery system, characterized in that: The following steps are involved: S1, obtaining the estimated values ​​of the main circuit and two branch currents; S2. The estimated values ​​of the two branch currents are corrected using the following method; When S2.1 corrects the estimated value of branch 1 current: If |I(t)|≤α, then the error variable D(t)=0, and the branch current I'1(t)=I'1(t)+D(t); let t=t+1, and correct the estimated value at the next moment; If I(t)<-α, then the error variable Branch current I'1(t) = I'1(t) + D(t); let t = t+1, and correct the estimated value at the next moment; If I(t)>α, then the error variable Branch current I'1(t) = I'1(t) + D(t); let t = t+1, and correct the estimated value at the next moment; Here α = 1A, K1 = 100, K2 = 180; When S2.2 corrects the estimated value of branch 2 current: If |I(t)|≤α, then the error variable D(t)=0; the branch current I'2(t)=I'2(t)-D(t); let t=t+1, and correct the estimated value at the next moment; If I(t)>α, then the error variable Branch current I'2(t) = I'2(t) - D(t); let t = t+1, and correct the estimated value at the next moment; If I(t)<-α, then the error variable Branch current I'2(t) = I'2(t) - D(t); let t = t+1, and correct the estimated value at the next moment; Here α=1A, K3=200, K4=40.

2. The method for correcting the current estimation of parallel branches of an energy storage battery system according to claim 1, characterized in that: In step S1, the estimated values ​​of the main circuit current and the two branch currents are obtained by the following method: S1.1, obtain the main circuit current and branch circuit voltage and current of the parallel battery pack under four dynamic working conditions: DST, FUDS, UDDS, and HPPC; S1.2, integrating the main current, two branch voltages and two branch currents under three of the working conditions into a main current data set I, two branch voltage data sets V1 and V2, and two branch current data sets I1 and I2, and normalizing the integrated data sets; S1.3, training BP neural network; S1.

4. The main current I' and the voltages V'1 and V'2 of the two branches under the remaining working condition are input as input features into the trained BP neural network. After obtaining the estimated results, the results are denormalized to obtain the estimated branch current values ​​I'1 and I'2.

3. A method for estimating and correcting parallel branch current of an energy storage battery system according to claim 2, characterized in that: The following method is used to train the BP neural network in S1.3: S1.3.1 Setting the parameters of BP neural network S1.3.2 Take the main current data set I and the two branch voltage data sets V1 and V2 as input, and the branch current data sets I1 and I2 as output, train the BP neural network, and obtain a trained BP neural network.

4. The method for correcting the current estimation of parallel branches of an energy storage battery system according to claim 1, characterized in that: The data recorded in step S1 is obtained by the following method: current sensors are connected in series to the main circuit and two branches of the parallel battery pack, and voltage sensors are connected in parallel to the two branches, so as to obtain the main circuit current of the parallel battery pack and the voltage and current of the two branches.

5. The method for correcting the current estimation of parallel branches of an energy storage battery system according to claim 1, characterized in that: In S1.2, the Min-Max normalization method is used to normalize the data set.

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