Battery parameter online identification method and system

Abstract

The invention discloses a battery parameter online identification method and system, and belongs to the technical field of batteries. A forgetting matrix and an information matrix which is an inverse matrix with a covariance matrix are introduced, the information matrix is split into an unexcited information subspace matrix and an excited information subspace matrix, and only the excited information subspace matrix is forgotten. On the basis of a covariance matrix updating formula in an existing recursive least square algorithm with a forgetting factor, a forgetting matrix multiplied by an information matrix is used for replacing a covariance matrix in an original covariance matrix updating formula, so that a directional forgetting mechanism is formed; the error covariance matrix updating formula # imgabs0 # adopted by the method can selectively forget old information components which can be replaced by the new information according to the new information input at the current moment, other old information components are not influenced, therefore, the covariance explosion condition is avoided, the robustness is greatly improved, and the method is suitable for being popularized and applied. And the identification precision of the battery parameters is relatively high.

Description

Technical Field

[0001] The present invention belongs to the field of battery technology, and more specifically, relates to a battery parameter online identification method and system. Background Art

[0002] In the process of green, low-carbon and sustainable development of human society, battery technology, as a key energy storage solution, plays a vital role. Whether it is an energy storage battery or a power battery, real-time and accurate battery state estimation is an important prerequisite for safe, stable and efficient operation of the battery. However, the core battery states including state of charge, state of health and power state cannot be directly measured and can only be estimated through related physical quantities. Among them, the state estimation method based on voltage, current and temperature is the most common. Battery parameter identification is an indispensable step in the state estimation method based on voltage, current and temperature. The accuracy of battery parameter identification will directly affect the accuracy of battery state estimation. Therefore, it is of great significance to study a battery parameter identification method.

[0003] The online identification method of battery parameters based on the recursive least squares algorithm with forgetting factor is currently a commonly used online identification method for battery parameters. The recursive least squares algorithm has the advantages of good convergence and low real-time computing overhead, which is very consistent with the application requirements of online parameter identification of battery models. This method usually sets independent time-varying forgetting factors for different model parameters, and makes adaptive adjustments according to the real-time changes of the system to increase the weight of new data, reduce the influence of old data, and quickly track the latest state of the target system. However, when the current excitation is non-continuous excitation, covariance explosion will occur, resulting in periodic fluctuations in the results, poor robustness, and low accuracy of battery parameter identification. Summary of the invention

[0004] In view of the above defects or improvement needs of the prior art, the present invention provides a battery parameter online identification method and system to solve the technical problem of low battery parameter identification accuracy in the prior art.

[0005] In order to achieve the above objectives, in a first aspect, the present invention provides a method for online identification of battery parameters, comprising:

[0006] S1. At the current sampling time k, the battery is tested under the target dynamic condition to obtain the current I of the battery under the target dynamic condition. k and terminal voltage U k , and then get a k and b k ; k and b kare respectively used as the input vector and output value of the battery equivalent circuit model at time k; wherein the battery equivalent circuit model is a model obtained by discretizing the r-order equivalent circuit model of the battery; r is a positive integer; a k For-(U k-1 -U oc,k-1 ),-(U k-2 -U oc,k-2 ),…,-(U k-r -U oc,k-r ) and I k ,I k-1 ,…,I k-r The 2r+1 dimensional vector formed; b k =U k -U oc,k ; U oc,k is the open circuit voltage of the battery under the target dynamic condition at time k; k ≥ r;

[0007] S2. Calculate the measurement error of the battery equivalent circuit model at time k in, is the battery parameter estimation vector at time k-1, specifically a 2r+1 dimensional vector, which is constructed based on the estimated values ​​of each battery parameter to be identified in the battery equivalent circuit model at time k-1;

[0008] Using the error covariance matrix update formula Calculate the error covariance matrix P at time k k , and then calculate the system gain vector K at time k k =P k a k ;in, is the error covariance matrix after directional forgetting at time k-1, specifically: λ is the forgetting factor;

[0009] S3. Calculate the battery parameter estimation vector at time k Then, the estimated values ​​of the battery parameters to be identified at time k are obtained.

[0010] Further preferably, the discretization process includes:

[0011] Discretize the system transfer function of the r-order equivalent circuit model to obtain a discretized system transfer function;

[0012] The discretized system transfer function is converted into a discrete linear expression representing the battery voltage-current relationship as the above-mentioned battery equivalent circuit model.

[0013] Further preferably, when r=1, the battery parameters to be identified include: the ohmic internal resistance, polarization resistance and polarization capacitance of the battery.

[0014] Further preferably, the above-mentioned r-order equivalent circuit model is a first-order Thevenin equivalent circuit model, and its expression is:

[0015]

[0016] Where C1 is the polarization capacitance of the battery; U1 is the polarization voltage of the battery; t represents time; R1 is the polarization resistance of the battery; I is the current of the battery; U oc is the open circuit voltage of the battery; R0 is the ohmic internal resistance of the battery; U is the terminal voltage of the battery.

[0017] Further preferably, the above battery equivalent circuit model is:

[0018]

[0019] Among them, y k is the output value of the battery equivalent circuit model at time k, specifically U k -U oc,k ; φ k =[-y k-1 x k x k-1 ] is the input vector of the battery equivalent circuit model at time k; x k For I k θ k =[m 1,k n 1,k n 2,k ] is the battery parameter estimation vector at time k; e k is the measurement error of the battery equivalent circuit model at time k; m 1,k 、n 1,k and n 2,k The following relations are satisfied: n 1,k =R 0,k , T is the sampling period; R 0,k is the ohmic internal resistance of the battery to be identified at time k; R 1,k is the polarization resistance of the battery to be identified at time k; C 1,k is the polarization capacitance of the battery to be identified at time k.

[0020] Further preferably, the open circuit voltage U of the battery under the target dynamic condition at time k is oc,k Obtained through the following methods:

[0021] Get the battery state of charge SoC at time k under the target dynamic condition k, and according to the mapping relationship between the battery open circuit voltage and the state of charge, the open circuit voltage U of the battery at the target dynamic condition at time k is obtained. oc,k ;

[0022] The mapping relationship between the battery open circuit voltage and the state of charge is obtained by performing an open circuit voltage test on the battery under target dynamic conditions.

[0023] In a second aspect, the present invention provides a battery parameter online identification system, comprising: a memory and a processor, wherein the memory stores a computer program, and the processor executes the battery parameter online identification method provided by the first aspect of the present invention when executing the computer program.

[0024] In a third aspect, the present invention provides a battery management system, including: the battery parameter online identification system provided in the second aspect of the present invention.

[0025] In a fourth aspect, the present invention further provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is executed by a processor, the device where the storage medium is located is controlled to execute the battery parameter online identification method provided in the first aspect of the present invention.

[0026] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects:

[0027] 1. The present invention provides a method for online identification of battery parameters. Considering that the old information in the existing recursive least squares algorithm with a forgetting factor is forgotten indiscriminately, the identification result obtained is prone to periodic fluctuations when there is non-continuous excitation. The present invention introduces a directional forgetting mechanism to optimize the existing forgetting mechanism, and designs a directional forgetting recursive least squares algorithm to perform online identification of battery parameters. A forgetting matrix and an information matrix that is the inverse matrix of the covariance matrix are introduced, and the information matrix is ​​split into an unstimulated information subspace matrix and an stimulated information subspace matrix. Only the stimulated information subspace matrix is ​​forgotten. On the basis of the covariance matrix update formula in the existing recursive least squares algorithm with a forgetting factor, the covariance matrix in the original covariance matrix update formula is replaced by multiplying the information matrix by the forgetting matrix, and this is used as a directional forgetting mechanism to derive the error covariance matrix update formula adopted by the present invention. Through this design, the forgetting factor is changed from acting on the entire covariance matrix to acting selectively on the excited information subspace, and then it can selectively forget the old information components that can be replaced by the new information input at the current moment, while other old information components are not affected, thereby avoiding the occurrence of covariance explosion, greatly improving the robustness, and making the identification accuracy of battery parameters higher.

[0028] 2. Furthermore, the battery parameter online identification method provided by the present invention preferably adopts a first-order Thevenin equivalent circuit model, which has a simple structure and can simulate the dynamic and static voltage characteristics of the battery, thereby facilitating online parameter identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 A schematic diagram of the structure of an r-order equivalent circuit model of a battery provided in an embodiment of the present invention;

[0030] Figure 2 A flow chart of a battery parameter online identification method provided by an embodiment of the present invention;

[0031] Figure 3 A schematic diagram of the structure of a first-order Thevenin model of a battery provided by an embodiment of the present invention;

[0032] Figure 4 A schematic diagram of a current-voltage curve under a dynamic working condition provided by an embodiment of the present invention;

[0033] Figure 5 An OCV-SoC curve diagram of a battery provided by an embodiment of the present invention;

[0034] Figure 6 A comparison graph of online identification result curves of ohmic internal resistance, polarization resistance and time constant of a liquid metal battery provided in an embodiment of the present invention using the RLSDF algorithm and the RLSFF algorithm under DST conditions. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0036] In order to achieve the above objectives, in a first aspect, the present invention provides a method for online identification of battery parameters, comprising:

[0037] S1. At the current sampling time k, the battery is tested under the target dynamic condition to obtain the current I of the battery under the target dynamic condition. k and terminal voltage U k , and then get a k and b k ; k and b k are respectively used as the input vector and output value of the battery equivalent circuit model at time k; wherein the battery equivalent circuit model is a model obtained by discretizing the r-order equivalent circuit model of the battery; r is a positive integer; ak For-(U k-1 -U oc,k-1 ),-(U k-2 -U oc,k-2 ),…,-(U k-r -U oc,k-r ) and I k ,I k-1 ,…,I k-r The 2r+1 dimensional vector formed; b k =U k -U oc,k ; U oc,k is the open circuit voltage of the battery under the target dynamic condition at time k; k ≥ r;

[0038] The above discretization processing includes: discretizing the system transfer function of the r-order equivalent circuit model to obtain a discretized system transfer function; converting the discretized system transfer function into a discrete linear expression representing the battery voltage-current relationship as the above-mentioned battery equivalent circuit model.

[0039] Specifically, when the actual measurement error is not considered, the battery equivalent circuit model is:

[0040]

[0041] Among them, y k As the output value of the battery equivalent circuit model at time k, specifically y k =U k -U oc,k ; -y k-1 ,-y k-2 ,…,-y k-n and x k ,x k-1 ,…,x k-n The 2r+1-dimensional vector formed is used as the input vector θ of the battery equivalent circuit model at time k k ; -m 1,k ,-m 2,k ,…,-m r,k and -n 1,k ,-n 2,k ,…,-n r+1,k The 2n+1 dimensional vector φ k ;y k ,φ k and θ k The corresponding relationship satisfies It should be noted that m i,k and n j,k It is constructed based on the estimated values ​​of each battery parameter to be identified in the battery equivalent circuit model, and the specific form is determined by the specific r-order equivalent circuit model and the sampling period T.

[0042] When considering the actual measurement error, the battery equivalent circuit model is: Among them, e k is the measurement error of the battery equivalent circuit model at time k.

[0043] In the present invention, y k =b k ,θ k =a k , in, is the battery parameter estimation vector at the kth time to be identified.

[0044] It should be noted that the above-mentioned target dynamic conditions may be dynamic stress conditions (DST), highway fuel economy test (HWFET), China light vehicle test conditions (CLTC), New York city conditions (NYCC), urban dynamometer driving cycle conditions (UDDS) and the like, and are not limited here.

[0045] It should be noted that in an optional implementation manner, the open circuit voltage U of the battery at the target dynamic condition at time k is oc,k It can be obtained in the following ways:

[0046] Get the battery state of charge SoC at time k under the target dynamic condition k , and according to the mapping relationship between the battery open circuit voltage and the state of charge, the open circuit voltage U of the battery at the target dynamic condition at time k is obtained. oc,k ;

[0047] The mapping relationship between the battery open circuit voltage and the state of charge is obtained by testing the battery open circuit voltage under the target dynamic working conditions. k It can be calculated by the ampere-hour integration method.

[0048] S2. Calculate the measurement error of the battery equivalent circuit model at time k in, is a battery parameter estimation vector at time k-1, specifically a 2r+1 dimensional vector, which is constructed based on the estimated values ​​of each battery parameter to be identified in the battery equivalent circuit model at time k-1;

[0049] Using the error covariance matrix update formula Calculate the error covariance matrix P at time k k , and then calculate the system gain vector K at time k k =P k a k ;in, is the error covariance matrix after directional forgetting at time k-1, specifically: λ is the forgetting factor;

[0050] S3. Calculate the battery parameter estimation vector at time k Then, the estimated values ​​of the battery parameters to be identified at time k are obtained.

[0051] It should be noted that the r-order equivalent circuit model is usually the r-order RC equivalent circuit model of the battery, as shown in Figure 1 As shown, the model consists of an open circuit voltage source U oc , an ohmic internal resistance R0 and r polarization resistors R i and polarization capacitance C i It should be noted that r is a positive integer, and generally takes a value of 1, 2, 3, etc. according to user needs.

[0052] In an optional implementation, when r=1, the battery parameters to be identified include: the ohmic internal resistance, polarization resistance and polarization capacitance of the battery. The r-order equivalent circuit model is a first-order Thevenin equivalent circuit model, and its expression is:

[0053]

[0054] Where C1 is the polarization capacitance of the battery; U1 is the polarization voltage of the battery; t represents time; R1 is the polarization resistance of the battery; I is the current of the battery; U oc is the open circuit voltage of the battery; R0 is the ohmic internal resistance of the battery; U is the terminal voltage of the battery.

[0055] At this time, the above battery equivalent circuit model is:

[0056]

[0057] Among them, y k is the output value of the battery equivalent circuit model at time k, specifically U k -U oc,k ; φ k =[-y k-1 x k x k-1 ] is the input vector of the battery equivalent circuit model at time k; x k For I k θ k =[m 1,k n 1,k n 2,k ] is the battery parameter estimation vector at time k; e k is the measurement error of the battery equivalent circuit model at time k; m 1,k 、n 1,kand n 2,k The following relations are satisfied: n 1,k =R 0,k , T is the sampling period; R 0,k is the ohmic internal resistance of the battery to be identified at time k; R 1,k is the polarization resistance of the battery to be identified at time k; C 1,k is the polarization capacitance of the battery to be identified at time k.

[0058] It should be noted that the above is an example of the expression of the battery equivalent circuit model when the first-order Thevenin equivalent circuit model is used. The expression of the battery equivalent circuit model when other models are used is similar to it, and the only difference is that φ k and θ k The specific composition of k and θ k The corresponding r-order equivalent circuit model adopted can be derived through the above discretization process. The acquisition method is the same as the acquisition process when the first-order Thevenin equivalent circuit model is adopted, and will not be repeated here.

[0059] In summary, the present invention provides a battery parameter identification method, which introduces a directional forgetting recursive mechanism. In the process of model parameter identification, the forgetting factor no longer acts on the entire information matrix indiscriminately, but affects the update of the information matrix in the form of a forgetting matrix, thereby increasing the information content in the information matrix. According to the new information input at the current moment, the old information components that can be replaced by it are selectively forgotten, while other old information components are not affected, thereby reducing the sensitivity of the identification method to noise, improving the robustness of the identification, and thus improving the identification accuracy.

[0060] In order to further illustrate the battery parameter online identification method provided by the present invention, a specific embodiment is described in detail below:

[0061] This embodiment uses a Li-Bi liquid metal battery as a research object, where the actual capacity of the battery is 52Ah, the operating temperature is 550°C, and the charge and discharge cut-off voltages are 1.2V and 0.5V respectively. Based on the test data of dynamic working conditions, the method proposed in the present invention is used to perform online parameter identification of the battery model. The battery parameters to be identified in this embodiment include: the ohmic internal resistance, polarization resistance and polarization capacitance of the battery, and a first-order Thevenin model is used.

[0062] like Figure 2 As shown in the figure, the entire online identification process of battery parameters is as follows:

[0063] 1) Before formally executing the online identification process, construct a battery equivalent circuit model;

[0064] The first-order Thevenin model of liquid metal battery is established, such as Figure 3 As shown: The model consists of an open circuit voltage source U oc , an ohmic internal resistance R0 and an RC parallel network consisting of a polarization resistor R1 and a polarization capacitor C1. The mathematical expression of the model is as follows:

[0065]

[0066] Where, t represents time; I is the current of the battery; U oc is the open circuit voltage of the battery; U is the terminal voltage of the battery.

[0067] The system transfer function based on this model is established. If the current is taken as input and the voltage of the passive network is taken as output, the continuous transfer function H(s) of the system is:

[0068]

[0069] Discretize the continuous system. Use the first-order forward difference method to discretize the transfer function. Assuming the sampling period is T, the discretized transfer function is as follows:

[0070]

[0071] Where z is the discretized z-domain complex variable, T is the sampling period, and H(z) is the discretized system transfer function;

[0072] m1, n1, and n2 are all intermediate parameters for solving model parameters. Their specific values ​​are After identifying the intermediate parameters m1, n1, and n2, Solve for the equivalent circuit model parameters R0, R1, and C1.

[0073] The discrete form of the voltage-current relationship is further obtained as: k =-m1y k-1 +n1x k +n2x k-1 .

[0074] Rewriting it into vector form and considering the actual measurement error, the battery equivalent circuit model is obtained, which is:

[0075] y k =φ k θ k +e k

[0076] Among them, y k is the output value of the battery equivalent circuit model at time k, specifically U k -U oc,k ; φk =[-y k-1 x k x k-1 ] is the input vector of the battery equivalent circuit model at time k; x k For I k θ k =[m 1,k n 1,k n 2,k ] is the battery parameter estimation vector at time k; e k is the measurement error of the battery equivalent circuit model at time k; m 1,k 、n 1,k and n 2,k The following relations are satisfied: n 1,k =R 0,k , T is the sampling period; R 0,k is the ohmic internal resistance of the battery to be identified at time k; R 1,k is the polarization resistance of the battery to be identified at time k; C 1,k is the polarization capacitance of the battery to be identified at time k.

[0077] 2) During the formal online identification process, at the current time k, the battery is tested under the target dynamic condition to obtain the battery current I under the target dynamic condition. k and terminal voltage U k , and then get a k and b k .

[0078] In this embodiment, the battery is first charged at a constant current of 0.2C to a cut-off voltage of 1.2V, and then charged at a constant voltage until the charging rate drops to 0.05C to ensure that the battery is fully charged. Subsequently, after a period of rest, the battery voltage gradually stabilizes, and a dynamic stress condition (DST) is applied to the battery by a BlueDian CT3001D battery tester for 2 hours, and the terminal voltage and current data of the battery are recorded at a sampling frequency of 1Hz. The current-voltage curve under the dynamic condition is shown in FIG. Figure 4 shown.

[0079] Prior to this, the circuit was tested for open circuit voltage to determine the mapping relationship between the battery open circuit voltage and the state of charge, which is recorded as the battery OCV-SoC relationship. The liquid metal battery was charged and discharged at a constant current rate of 0.05C, and the voltage and SoC were recorded in real time. The test results are shown in Figure 2. Figure 5 As shown, the two dotted lines correspond to the voltage curves during the charging and discharging process. The final battery OCV-SoC relationship curve is obtained by averaging the voltage curves during the charging and discharging process, which is shown as a solid line in the figure. The functional relationship between OCV and SoC is processed by the table lookup method.

[0080] In this embodiment, a k =[-(U k-1 -U oc,k-1 )I k I k-1 ]; among them, U k-1 is the terminal voltage of the battery at time k-1; U oc,k-1 is the open circuit voltage of the battery at time k-1; I k is the battery current at time k; I k-1 is the battery current at time k.

[0081] b k =U k -U oc,k ; U oc,k is the open circuit voltage of the battery at time k; in this embodiment, the open circuit voltage U of the battery at time k oc,k Obtained through the following methods:

[0082] Get the battery state of charge SoC at time k k , and according to the mapping relationship between the battery open circuit voltage and the state of charge, the battery open circuit voltage U at time k is obtained oc,k ;

[0083] The mapping relationship between the battery open circuit voltage and the state of charge is obtained by testing the battery open circuit voltage. k It can be solved by the ampere-hour integration method, which is calculated by integrating the current over time.

[0084] 3) Use the error covariance matrix update formula to calculate the error covariance matrix at time k Then the system gain vector K at time k is calculated k =P k a k ;in, is the error covariance matrix after directional forgetting at time k-1, specifically: λ is the forgetting factor;

[0085] It should be noted that the covariance matrix update formula in the original recursive least squares algorithm with forgetting factor is: Under the covariance matrix update formula, when the current excitation is non-continuous, covariance explosion will occur, resulting in periodic fluctuations in the results and poor robustness, which makes the accuracy of battery parameter identification low.

[0086] In order to solve this problem, the present invention modifies it by changing the error covariance matrix P k The update process is changed to the information matrix R k The form is:

[0087]

[0088] P k With R k The information matrix R at time k-1 is inverse to each other. k-1 Split into unstimulated information subspace R 1,k-1 and the stimulated information subspace R 2,k-1 , by distinguishing R 1,k-1 and R 2,k-1 , realize the selective directional forgetting by the forgetting factor λ, specifically:

[0089] Solve the stimulated information subspace R 2,k-1 , and we get the following formula:

[0090]

[0091] Then we get the information matrix R of directional forgetting k The update formula is:

[0092]

[0093] R k The update process of the error covariance matrix obtained by inversion is:

[0094]

[0095] Among them, Λ is the forgetting matrix; is the error covariance matrix after directional forgetting at time k-1, specifically:

[0096]

[0097] In addition, this step also calculates the measurement error of the battery equivalent circuit model at time k in, is the battery parameter estimation vector at time k-1; the battery parameter estimation vector is a vector composed of the estimated values ​​of each battery parameter to be identified in the battery equivalent circuit model;

[0098] 3) Calculate the battery parameter estimation vector at time k Then, the estimated values ​​of the battery parameters to be identified at the current time k are obtained.

[0099] It should be noted that, when initializing, taking r=1 as an example, when k=1, P0, U0, U oc,0 All use preset initial values; in an optional implementation mode, P0, U0, Uoc,0 The corresponding initial values ​​are [0 0 0] T , 10 3 I, 0, 0.

[0100] The present invention records the solution algorithm for battery parameter identification as a directed forgetting recursive least squares algorithm (RLSDF). The RLSDF provided by the present invention and the traditional recursive least squares algorithm with forgetting factor (RLSFF) are used for battery parameter identification, and the identification results are compared, as shown in the following figure: Figure 6 The comparison results are shown. Figure 6 It can be seen that, thanks to the directed forgetting mechanism, the battery parameters identified by the online battery parameter identification method provided by the present invention are more stable compared to RLSFF. In RLSFF, the forgetting of old information is indiscriminate, and the current step will always lead to the same "mutation-convergence" process in the parameter estimation results, so the parameter estimation value will show periodic changes. In the present invention, due to the addition of a directed forgetting mechanism, the information stored in the information matrix is ​​richer, which is mathematically reflected in the information matrix having a larger rank, which can avoid excessive gains, thereby enhancing the robustness of the parameter estimation algorithm. Figure 6 It can be seen that the battery model parameters identified by the online battery parameter identification method provided by the present invention will not show severe jitter phenomenon, which is particularly evident in the identification of time constants. The present invention optimizes the update mechanism of the covariance matrix in the RLS algorithm to effectively limit the continuous increase of the gain under non-continuous excitation conditions, thereby achieving the purpose of enhancing the robustness of the algorithm and maintaining the stability of the parameter estimation results. According to the results of the preferred embodiment, compared with the traditional RLSFF algorithm, the online battery parameter identification method proposed in the present invention can provide more stable online parameter identification results, which is beneficial to enhancing the convergence performance of the filter or observer in battery state estimation.

[0101] In a second aspect, the present invention provides a battery parameter online identification system, comprising: a memory and a processor, wherein the memory stores a computer program, and the processor executes the battery parameter online identification method provided by the first aspect of the present invention when executing the computer program.

[0102] The related technical solution is the same as the battery parameter online identification method provided in the first aspect of the present invention, and will not be described in detail here.

[0103] In a third aspect, the present invention provides a battery management system, including: the battery parameter online identification system provided in the second aspect of the present invention.

[0104] The related technical solution is the same as the battery parameter online identification system provided in the second aspect of the present invention, and will not be described in detail here.

[0105] In a fourth aspect, the present invention further provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is executed by a processor, the device where the storage medium is located is controlled to execute the battery parameter online identification method provided in the first aspect of the present invention.

[0106] The related technical solution is the same as the battery parameter online identification method provided in the first aspect of the present invention, and will not be described in detail here.

[0107] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A battery parameter online identification method, characterized in that: include: S1. At the current sampling time k, the battery is tested under the target dynamic condition to obtain the current I of the battery under the target dynamic condition. k and terminal voltage U k , and then get a k and b k ; k and b k are respectively used as the input vector and output value of the battery equivalent circuit model at time k; wherein the battery equivalent circuit model is a model obtained by discretizing the r-order equivalent circuit model of the battery; r is a positive integer; a k For-(U k-1 -U oc,k-1 ),-(U k-2 -U oc,k-2 ),…,-(U k-r -U oc,k-r ) and I k ,I k-1 ,…,I k-r The 2r+1 dimensional vector formed; b k =U k -U oc,k ; U oc,k is the open circuit voltage of the battery under the target dynamic condition at time k; k ≥ r; S2. Calculate the measurement error of the battery equivalent circuit model at time k is a battery parameter estimation vector at time k-1, specifically a 2r+1-dimensional vector, which is constructed based on the estimated values ​​of each battery parameter to be identified in the battery equivalent circuit model at time k-1; Using the error covariance matrix update formula Calculate the error covariance matrix P at time k k , and then calculate the system gain vector K at time k k =P k a k ; is the error covariance matrix after directional forgetting at time k-1, specifically: λ is the forgetting factor; S3. Calculate the battery parameter estimation vector at time k Then, the estimated values ​​of the battery parameters to be identified at time k are obtained.

2. The method for online identification of battery parameters according to claim 1, characterized in that: The discretization process comprises: Discretizing the system transfer function of the r-order equivalent circuit model to obtain a discretized system transfer function; The discretized system transfer function is converted into a discrete linear expression representing the battery voltage-current relationship as the battery equivalent circuit model.

3. The method for online identification of battery parameters according to claim 2, characterized in that: When r=1, the battery parameters to be identified include: the battery's ohmic internal resistance, polarization resistance, and polarization capacitance.

4. The method for online identification of battery parameters according to claim 3, characterized in that: The r-order equivalent circuit model is a first-order Thevenin equivalent circuit model, and its expression is: Where C1 is the polarization capacitance of the battery; U1 is the polarization voltage of the battery; t represents time; R1 is the polarization resistance of the battery; I is the current of the battery; U oc is the open circuit voltage of the battery; R0 is the ohmic internal resistance of the battery; U is the terminal voltage of the battery.

5. The method for online identification of battery parameters according to claim 4, characterized in that: The battery equivalent circuit model is: Among them, y k is the output value of the battery equivalent circuit model at time k, specifically U k -U oc,k ; φ k =[-y k-1 x k x k-1 ] is the input vector of the battery equivalent circuit model at time k; x k For I k θ k =[m 1,k n 1,k n 2,k ] is the battery parameter estimation vector at time k; e k is the measurement error of the battery equivalent circuit model at time k; m 1,k 、n 1,k and n 2,k The following relations are satisfied: n 1,k =R 0,k , T is the sampling period; R 0,k is the ohmic internal resistance of the battery to be identified at time k; R 1,k is the polarization resistance of the battery to be identified at time k; C 1,k is the polarization capacitance of the battery to be identified at time k.

6. The method for online identification of battery parameters according to any one of claims 1 to 5, characterized in that: The open circuit voltage U of the battery under the target dynamic condition at time k oc,k Obtained through the following methods: Get the battery state of charge SoC at time k under the target dynamic condition k , and according to the mapping relationship between the battery open circuit voltage and the state of charge, the open circuit voltage U of the battery at the target dynamic condition at time k is obtained. oc,k ; The mapping relationship between the battery open circuit voltage and the state of charge is obtained by performing an open circuit voltage test on the battery under a target dynamic operating condition.

7. A battery parameter online identification system, characterized in that: include: A memory and a processor, wherein the memory stores a computer program, and the processor executes the battery parameter online identification method according to any one of claims 1 to 6 when executing the computer program.

8. A battery management system, characterized in that: include: The battery parameter online identification system as described in claim 7.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed by a processor, the device where the storage medium is located is controlled to execute the battery parameter online identification method according to any one of claims 1 to 6.

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