CN112557925B - Lithium ion battery SOC estimation method and device - Google Patents

Abstract

The embodiment of the invention provides a lithium ion battery SOC estimation method and device, wherein the method comprises the following steps: determining the mapping relation between each model parameter in the battery equivalent circuit model and the temperature and SOC value; establishing a discrete state space model of the battery according to the battery equivalent circuit model and the mapping relation between each model parameter and the temperature and SOC value; the SOC value of the battery discrete state space model is estimated through an iterative extended Kalman filtering algorithm, and the SOC value of the lithium ion battery is determined, so that multiple iterations are adopted in each time step, the deviation caused by adopting a first-order Taylor approximation can be effectively reduced, and the algorithm precision is improved.

Description

Lithium-ion battery SOC estimation method and device

Technical Field

The present invention relates to the technical field of battery management, and in particular to a method and device for estimating the SOC of a lithium-ion battery.

Background Art

Battery SOC (full name: State of Charge, Chinese: state of charge or remaining power) estimation is an important part of the battery management system of electric vehicles. Since the estimation of battery SOC is affected by many factors (such as charge and discharge rate, temperature, cycle life, self-discharge, etc.), it is difficult to ensure the accuracy of SOC estimation in practical applications. The battery management system of electric vehicles estimates the battery SOC will directly affect the control of electric vehicles. On the one hand, the inaccurate estimation of battery SOC may limit the use of electric vehicles and make it difficult to exert their optimal performance; on the other hand, it may cause battery abuse or even thermal runaway, bringing great safety hazards to the use of electric vehicles. Accurate estimation of SOC is of great significance for accurately estimating the remaining power of the power battery, ensuring that the SOC is maintained within a reasonable range, and preventing the power battery from being overcharged or over-discharged.

There are errors in the estimation process. The existing technology can solve the problem of error accumulation in the SOC estimation process, but it cannot completely cover the operating temperature of the battery, and it cannot flexibly iterate according to the error at the current estimation moment.

Summary of the invention

In view of the problems existing in the prior art, an embodiment of the present invention provides a method and device for estimating the SOC of a lithium-ion battery.

In a first aspect, an embodiment of the present invention provides a method for estimating SOC of a lithium-ion battery, comprising:

Determine the mapping relationship between each model parameter and temperature and SOC value in the battery equivalent circuit model;

Establishing a discrete state space model of the battery according to the battery equivalent circuit model and the mapping relationship between the model parameters and the temperature and SOC value;

The SOC value of the discrete state space model of the battery is estimated by iterative extended Kalman filter algorithm to determine the SOC value of the lithium-ion battery.

Optionally, establishing a battery discrete state space model according to the battery equivalent circuit model and the mapping relationship between each model parameter and the temperature and the SOC value includes:

At least two Kalman filters are run in parallel, and the battery equivalent circuit model and the mapping relationship between the model parameters and the temperature and SOC value are combined to establish the battery discrete state space equation and the system measurement update equation, and define the measurement matrix.

Optionally, estimating the SOC value of the discrete state space model of the battery by iterative extended Kalman filter algorithm to determine the SOC value of the lithium-ion battery includes:

Initialize the iterative extended Kalman filter algorithm at the initial moment to determine the initial state estimate and the initial state error covariance matrix at the initial moment;

All Kalman filters are run in parallel, and multiple calculation operations are performed in a loop. Each calculation operation executes the iterative extended Kalman filter algorithm multiple times. Each time the iterative extended Kalman filter algorithm is executed, the execution time is updated and the measurement update iteration is performed until the iterative extended Kalman filter algorithm reaches the cutoff condition, the calculation operation is terminated, and the SOC value of the lithium-ion battery is determined.

Optionally, determining the mapping relationship between each model parameter in the battery equivalent circuit model and the temperature and SOC value includes:

Establishing a battery equivalent circuit model of a lithium-ion battery and establishing a characteristic equation of the battery equivalent circuit model;

Perform pulse discharge operation based on the battery equivalent circuit model and determine the model parameter values under each operation;

Construct an initial mapping expression between model parameters and temperature and SOC value, determine the undetermined coefficients in the mapping expression between each model parameter and temperature and SOC value by curve fitting method, and determine the mapping relationship between each model parameter and temperature and SOC value according to the undetermined coefficients.

In a second aspect, an embodiment of the present invention provides a lithium-ion battery SOC estimation device, comprising:

A determination module, used to determine the mapping relationship between each model parameter in the battery equivalent circuit model and the temperature and SOC value;

A construction module, used to establish a battery discrete state space model according to the battery equivalent circuit model and the mapping relationship between each model parameter and the temperature and SOC value;

The estimation module is used to estimate the SOC value of the battery discrete state space model through the iterative extended Kalman filter algorithm to determine the SOC value of the lithium-ion battery.

Optionally, the building module is specifically used to:

At least two Kalman filters are run in parallel, and the battery equivalent circuit model and the mapping relationship between the model parameters and the temperature and SOC value are combined to establish the battery discrete state space equation and the system measurement update equation, and define the measurement matrix.

Optionally, the estimation module is specifically used to:

Initialize the iterative extended Kalman filter algorithm at the initial moment to determine the initial state estimate and the initial state error covariance matrix at the initial moment;

All Kalman filters are run in parallel, and multiple calculation operations are performed in a loop. Each calculation operation executes the iterative extended Kalman filter algorithm multiple times. Each time the iterative extended Kalman filter algorithm is executed, the execution time is updated and the measurement update iteration is performed until the iterative extended Kalman filter algorithm reaches the cutoff condition, the calculation operation is terminated, and the SOC value of the lithium-ion battery is determined.

Optionally, the determining module is specifically configured to:

Establishing a battery equivalent circuit model of a lithium-ion battery and establishing a characteristic equation of the battery equivalent circuit model;

Perform pulse discharge operation based on the battery equivalent circuit model and determine the model parameter values under each operation;

Construct an initial mapping expression between model parameters and temperature and SOC value, determine the undetermined coefficients in the mapping expression between each model parameter and temperature and SOC value by curve fitting method, and determine the mapping relationship between each model parameter and temperature and SOC value according to the undetermined coefficients.

In a third aspect, an embodiment of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the lithium-ion battery SOC estimation method as described above when executing the computer program.

In a fourth aspect, an embodiment of the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the lithium-ion battery SOC estimation method as described above.

The lithium-ion battery SOC estimation method and device provided by the embodiment of the present invention establish a mapping relationship between the battery equivalent circuit model and each model parameter and the temperature and SOC value, so that the established battery discrete state space model is widely applicable to lithium-ion batteries, and then the SOC value of the battery discrete state space model is estimated by an iterative extended Kalman filter algorithm to determine the SOC value of the lithium-ion battery, so that multiple iterations are used in each time step, which can effectively reduce the deviation caused by using the first-order Taylor approximation, thereby improving the algorithm accuracy.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, a brief introduction will be given below to the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

FIG1 is a schematic flow chart of an embodiment of a method for estimating SOC of a lithium-ion battery according to the present invention;

FIG2 is a schematic diagram of an equivalent circuit model established based on a lithium-ion battery according to an embodiment of the present invention;

3 is a schematic diagram of a flow chart of estimating the SOC value of a battery discrete state space model according to an embodiment of the present invention;

4 is a schematic diagram of the structure of a lithium-ion battery SOC estimation device provided in an embodiment of the present invention;

FIG. 5 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention.

DETAILED DESCRIPTION

In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

FIG1 shows a schematic flow chart of a method for estimating SOC of a lithium-ion battery according to an embodiment of the present invention. Referring to FIG1 , the method includes:

S11, determining the mapping relationship between each model parameter in the battery equivalent circuit model and the temperature and SOC value;

S12, establishing a battery discrete state space model according to the battery equivalent circuit model and the mapping relationship between each model parameter and the temperature and SOC value;

S13. Estimate the SOC value of the discrete state space model of the battery by iterative extended Kalman filter algorithm to determine the SOC value of the lithium-ion battery.

Regarding step S11 to step S13, it should be noted that, in the embodiment of the present invention, to estimate the SOC (remaining power) of the lithium-ion battery, it is necessary to design the lithium-ion battery for circuit equivalent design so that the estimation of the lithium-ion battery is set in the circuit detection process. The battery equivalent circuit model is shown in FIG2 (taking the second order as an example, but the order is not limited to the second order). In FIG2, the battery equivalent circuit includes a series ohmic resistor _R0 and two RC units, each RC unit consisting of a resistor and a capacitor connected in parallel.

During the circuit detection process, the model parameters in the circuit model can be determined, and the model parameters will have a certain mutual influence relationship with the temperature and SOC value. To this end, it is necessary to determine the mapping relationship between the model parameters in the battery equivalent circuit model and the temperature and SOC value.

In this embodiment, the SOC value of the lithium-ion battery can be estimated by using a certain estimation model. Here, a battery discrete state space model is established based on the mapping relationship between the battery equivalent circuit model and each model parameter and the temperature and SOC value. The battery discrete state space model can realize the estimation of the SOC value of the lithium-ion battery.

During the estimation process, the SOC value of the discrete state space model of the battery is estimated by an iterative extended Kalman filtering algorithm to determine the SOC value of the lithium-ion battery. Kalman filtering is an algorithm that uses the linear system state equation to optimally estimate the system state through system input and output observation data. Since the observation data includes the influence of noise and interference in the system, the optimal estimation can also be regarded as a filtering process. In this embodiment, the SOC value of the discrete state space model of the battery is estimated by an iterative extended Kalman filtering algorithm, and multiple iterations are used at each time step, which can effectively reduce the deviation caused by the use of the first-order Taylor approximation, thereby improving the accuracy of the algorithm.

A lithium-ion battery SOC estimation method provided by an embodiment of the present invention establishes a mapping relationship between a battery equivalent circuit model and each model parameter and temperature and SOC value, so that the established battery discrete state space model is widely applicable to lithium-ion batteries, and then estimates the SOC value of the battery discrete state space model through an iterative extended Kalman filter algorithm to determine the SOC value of the lithium-ion battery, so that multiple iterations are used in each time step, which can effectively reduce the deviation caused by using a first-order Taylor approximation, thereby improving the accuracy of the algorithm.

In a further embodiment of the above-mentioned embodiment method, the processing process of establishing a battery discrete state space model according to the battery equivalent circuit model and the mapping relationship between each model parameter and the temperature and SOC value is mainly explained as follows:

At least two Kalman filters are run in parallel, and the battery equivalent circuit model and the mapping relationship between each model parameter and the temperature and SOC value are combined to establish the battery discrete state space equation and the system measurement update equation, and define the measurement matrix.

In this regard, it should be noted that at least two Kalman filters are run in parallel. Taking M optimal Kalman filters as an example, based on the Kalman filter principle, combined with the battery equivalent circuit model and the mapping relationship between each model parameter and the SOC value, the discrete state space equation of the lithium-ion battery is established, and the parameters are expressed in matrix form:

x _k ＝F _k-1 x _k-1 +G _k-1 u _k-1 (1)

in:

x＝[U ₀ ,U ₁ ,U ₂ ,SOC] ^T (2)

u＝[I,I,I,I] ^T (3)

_U0 is the voltage across the ohmic internal resistance _R0 in Figure 2, Δt is the sampling interval, and C _cap is the rated capacity of the battery.

In addition to establishing the discrete state space equations for lithium-ion batteries, it is also necessary to establish system measurement update equations and define the measurement matrix.

The established system measurement update equation is:

z _k ＝h _k +v _k ＝U _OCV (T,SOC)-U ₀ -U ₁ -U ₂ +v _k (6)

The measurement matrix is defined as:

_U0 is the voltage across the ohmic internal resistance _R0 in Figure 2, Δt is the sampling interval, and C _cap is the rated capacity of the battery.

In addition to establishing the discrete state space equations for lithium-ion batteries, it is also necessary to establish system measurement update equations and define the measurement matrix.

In a further embodiment of the above-mentioned embodiment method, the SOC value of the discrete state space model of the battery is estimated by iterative extended Kalman filter algorithm, and the processing process of determining the SOC value of the lithium-ion battery is explained as follows:

The iterative extended Kalman filter algorithm at the initial moment is initialized to determine the initial state estimate and the initial state error covariance matrix at the initial moment.

All Kalman filters are run in parallel, and multiple calculation operations are performed in a loop. Each calculation operation executes the iterative extended Kalman filter algorithm multiple times. Each time the iterative extended Kalman filter algorithm is executed, the execution time is updated and the measurement update iteration is performed until the iterative extended Kalman filter algorithm reaches the cutoff condition, the calculation operation is terminated, and the SOC value of the lithium-ion battery is determined.

In this regard, it should be noted that, in an embodiment of the present invention, a schematic diagram of a process for estimating the SOC value of the discrete state space model of the battery is shown in FIG3. Referring to FIG3, at the beginning of the estimation process, the iterative extended Kalman filter algorithm at the initial moment is first initialized, which can be expressed by the following relationship:

为初始状态估计值，

为初始状态误差协方差矩阵，x₀为模型参数值。Among them, ∧ represents estimation,

is the estimated value of the initial state,

is the initial state error covariance matrix, and _x0 is the model parameter value.

The loop calculation operation starts at loop time k=1, and continues to loop k=1, 2, ..., and continues to execute the following steps: loop multiple calculation operations, the number of loops i=1, 2, ..., M, the specific number of loops can be set according to actual needs, and there is no limit here. Each time the loop calculation operation is executed, multiple iterations of the extended Kalman filter algorithm are executed, and each loop calculation is executed. The execution time is updated, such as the time update of the i-th iteration of the extended Kalman filter in Figure 3, as follows:

Initialize the iterative extended Kalman filter as follows:

Each time a cyclic calculation operation is executed, a measurement update is performed, such as the measurement update of the i-th Kalman algorithm in FIG3 .

The meanings of the parameters in the above formulas (10)-(17) are all general meanings commonly used in the Kalman filter algorithm.

Until the iterative extended Kalman filter algorithm reaches the cutoff condition, the calculation operation is terminated to determine the SOC value of the lithium-ion battery.

In a further embodiment of the above-mentioned embodiment method, the processing of determining the mapping relationship between each model parameter in the battery equivalent circuit model and the temperature and SOC value is mainly explained, as follows:

Establish a battery equivalent circuit model for lithium-ion batteries and establish the characteristic equation of the battery equivalent circuit model;

Perform pulse discharge operation based on the battery equivalent circuit model and determine the model parameter values under each operation;

Construct the initial mapping relationship expression between model parameters and temperature and SOC value, determine the undetermined coefficients in the mapping relationship expression between each model parameter and temperature and SOC value by curve fitting method, and determine the mapping relationship between each model parameter and temperature and SOC value according to the undetermined coefficients.

In this regard, it should be noted that, in an embodiment of the present invention, a battery equivalent circuit model of a lithium-ion battery is first established (as shown in FIG. 2 ), and the equivalent circuit includes an ohmic resistor R ₀ and two RC units connected in series, and each RC unit is composed of a resistor and a capacitor connected in parallel. Determine the characteristic relationship between the terminal voltage U and the open circuit voltage U _OCV in the battery equivalent circuit model. For the second-order battery equivalent circuit model shown in FIG. 2 , a characteristic equation of the lithium-ion battery model is established to describe the characteristic relationship between the terminal voltage U and the open circuit voltage U _OCV , and the characteristic equation is as follows:

U ₀ ＝IR ₀ (20)

U＝U _OCV -U ₀ -U ₁ -U ₂ (21)

Wherein, _U0 is the terminal voltage across the ohmic internal resistance _R0 , _U1 - _U2 are the voltages across the corresponding two RC units, and I is the current.

Solving equations (18)-(21), we can obtain the expression of the terminal voltage of the equivalent circuit:

Wherein, U ₁ (0) and U ₂ (0) are the initial values of the voltages across the two RC units at the beginning of the timing.

According to the current equivalent circuit, a pulse discharge test is carried out. Pulse discharge is an existing test method, and the pulse discharge time, current, etc. are all existing specifications (for example, according to the Freedom battery test manual). In the selection of pulse discharge SOC intervals, SOC selects at least 20 points in the range of 0 to 1, and the temperature range is -10 to 55°C. When the temperature is below 10°C, a pulse discharge test is carried out every 5-10°C, and when the temperature T is above 10°C, a pulse discharge test is carried out every 5-15°C. When the temperature is below 10°C, the static equilibrium time after pulse discharge is greater than 3h, and when the temperature is above 10°C, the static equilibrium time after pulse discharge is greater than 1h. When conducting pulse discharge tests at different temperatures, the capacity standard of SOC is adjusted to the capacity that the battery can discharge at the current temperature, rather than the rated capacity at room temperature.

According to the structure of FIG2 , when the battery is left to stand for a long enough time after pulse discharge, the terminal voltage of the battery is the open circuit voltage U _ocv .

According to the structure of Figure 2, at the moment when the pulse discharge ends, the voltage change is completely caused by the ohmic internal resistance _R0 . Therefore, the ohmic internal resistance _R0 is obtained using the following formula:

Where _UL is the voltage mutation at the end of pulse discharge, and I is the pulse discharge current value.

From the circuit structure of Figure 2, it can be seen that the voltage across the ohmic internal resistance becomes zero at the moment the pulse discharge ends, but the voltage across the three RC units does not become zero. Therefore, the voltage characteristic equation is:

The values of R ₁ , C ₁ , R ₂ , and C ₂ at corresponding temperatures and SOCs can be obtained through nonlinear fitting, and a two-dimensional relationship between model parameters and temperature and SOC can be established.

f(x,y)＝p ₀₀ +p ₁₀ x+p ₀₁ y+p ₂₀ x ² +p ₁₁ xy+p ₀₂ y ² +p ₃₀ x ³ +p ₂₁ x ² y+p ₁₂ xy ² +p ₀₃ y ³ +p ₄₀ x ⁴ +p ₃₁ x ³ y+p ₂₂ x ² y ² +p ₁₃ xy ³ +p ₀₄ y ⁴ +p ₅₀ x ⁵ +p ₄₁ x ⁴ y+p ₃₂ x ³ y ² +p ₂₃ x ² y ³ +p ₁₄ xy ⁴ +p ₀₅ y ⁵

(f＝U _ocv ,R ₀ ,R ₁ ,R ₂ ,C ₁ ,C ₂ )

(x＝T,y＝SOC)

Based on the surface fitting method, the unknown coefficients in the expression (such as p ₀₀ , p ₁₀ , etc.) are determined.

Once the pending system is determined, the mapping relationship between each model parameter and the temperature and SOC value is determined based on the pending coefficients.

The lithium-ion battery SOC estimation method provided in the above embodiment establishes a mapping relationship between the battery equivalent circuit model and each model parameter and the temperature and SOC value, so that the established battery discrete state space model is widely applicable to lithium-ion batteries, and then estimates the SOC value of the battery discrete state space model through an iterative extended Kalman filter algorithm to determine the SOC value of the lithium-ion battery, so that multiple iterations are used in each time step, which can effectively reduce the deviation caused by using the first-order Taylor approximation, thereby improving the algorithm accuracy.

FIG4 shows a schematic diagram of the structure of a lithium-ion battery SOC estimation device provided by an embodiment of the present invention. Referring to FIG4 , the device includes a determination module 41, a construction module 42 and an estimation module 43, wherein:

A determination module 41 is used to determine the mapping relationship between each model parameter in the battery equivalent circuit model and the temperature and SOC value;

A construction module 42 is used to establish a battery discrete state space model according to the battery equivalent circuit model and the mapping relationship between each model parameter and the temperature and SOC value;

The estimation module 43 is used to estimate the SOC value of the battery discrete state space model by iterative extended Kalman filter algorithm to determine the SOC value of the lithium-ion battery.

In a further embodiment of the above embodiment device, the building module is specifically used for:

At least two Kalman filters are run in parallel, and the battery equivalent circuit model and the mapping relationship between the model parameters and the temperature and SOC value are combined to establish the battery discrete state space equation and the system measurement update equation, and define the measurement matrix.

In a further embodiment of the above embodiment device, the estimation module is specifically used for:

Initialize the iterative extended Kalman filter algorithm at the initial moment to determine the initial state estimate and the initial state error covariance matrix at the initial moment;

All Kalman filters are run in parallel, and multiple calculation operations are performed in a loop. Each calculation operation executes the iterative extended Kalman filter algorithm multiple times. Each time the iterative extended Kalman filter algorithm is executed, the execution time is updated and the measurement update iteration is performed until the iterative extended Kalman filter algorithm reaches the cutoff condition, the calculation operation is terminated, and the SOC value of the lithium-ion battery is determined.

In a further embodiment of the above embodiment device, the determining module is specifically used to:

Establishing a battery equivalent circuit model of a lithium-ion battery and establishing a characteristic equation of the battery equivalent circuit model;

Perform pulse discharge operation based on the battery equivalent circuit model and determine the model parameter values under each operation;

Construct an initial mapping expression between model parameters and temperature and SOC value, determine the undetermined coefficients in the mapping expression between each model parameter and temperature and SOC value by curve fitting method, and determine the mapping relationship between each model parameter and temperature and SOC value according to the undetermined coefficients.

Since the principle of the device described in the embodiment of the present invention is the same as that of the method described in the above embodiment, a more detailed explanation is not repeated here.

It should be noted that, in the embodiment of the present invention, the relevant functional modules can be implemented by a hardware processor.

The lithium-ion battery SOC estimation device provided in the above embodiment establishes a mapping relationship between the battery equivalent circuit model and each model parameter and the temperature and SOC value, so that the established battery discrete state space model is widely applicable to lithium-ion batteries, and then estimates the SOC value of the battery discrete state space model through an iterative extended Kalman filter algorithm to determine the SOC value of the lithium-ion battery, so that multiple iterations are used in each time step, which can effectively reduce the deviation caused by using the first-order Taylor approximation, thereby improving the algorithm accuracy.

FIG5 illustrates a schematic diagram of the physical structure of an electronic device. As shown in FIG5 , the electronic device may include: a processor 51, a communication interface 52, a memory 53 and a communication bus 54, wherein the processor 51, the communication interface 52 and the memory 53 communicate with each other through the communication bus 54. The processor 51 may call the logic instructions in the memory 53 to execute the following method: determine the mapping relationship between each model parameter in the battery equivalent circuit model and the temperature and SOC value; establish a battery discrete state space model according to the battery equivalent circuit model and the mapping relationship between each model parameter and the temperature and SOC value; estimate the SOC value of the battery discrete state space model by iterative extended Kalman filter algorithm to determine the SOC value of the lithium-ion battery.

In addition, the logic instructions in the above-mentioned memory 53 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

An embodiment of the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it is implemented to execute the methods provided in the above embodiments, for example, including: determining the mapping relationship between each model parameter in the battery equivalent circuit model and the temperature and SOC value; establishing a battery discrete state space model based on the battery equivalent circuit model and the mapping relationship between each model parameter and the temperature and SOC value; estimating the SOC value of the battery discrete state space model by an iterative extended Kalman filtering algorithm to determine the SOC value of the lithium-ion battery.

Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM/RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (8)

1. A lithium ion battery SOC estimation method, comprising:

determining the mapping relation between each model parameter in the battery equivalent circuit model and the temperature and SOC value;

establishing a discrete state space model of the battery according to the battery equivalent circuit model and the mapping relation between the parameters of each model and the temperature and the SOC value;

estimating the SOC value of the battery discrete state space model through an iterative extended Kalman filtering algorithm to determine the SOC value of the lithium ion battery;

the step of establishing a discrete state space model of the battery according to the battery equivalent circuit model and the mapping relation between the parameters of each model and the temperature and the SOC value comprises the following steps:

and (3) parallel running at least two Kalman filters, combining the battery equivalent circuit model and the mapping relation between the parameters of each model and the temperature and SOC values, establishing a battery discrete state space equation and a system measurement update equation, and defining a measurement matrix.

2. The method according to claim 1, wherein the estimating the SOC value of the battery discrete state space model by the iterative extended kalman filter algorithm, determining the SOC value of the lithium ion battery, comprises:

initializing an iterative extended Kalman filtering algorithm at an initial moment, and determining an initial state estimated value and an initial state error covariance matrix at the initial moment;

and running all Kalman filtering in parallel, circularly executing a plurality of computing operations, executing the iterative extended Kalman filtering algorithm for a plurality of times in each computing operation, executing time updating and measuring updating iteration when executing the iterative extended Kalman filtering algorithm each time, and ending the computing operation until the iterative extended Kalman filtering algorithm reaches a cut-off condition to determine the SOC value of the lithium ion battery.

3. The method for estimating SOC of a lithium ion battery according to claim 1, wherein determining a mapping relationship between each model parameter in the battery equivalent circuit model and the temperature and SOC value includes:

establishing a battery equivalent circuit model of a lithium ion battery, and establishing a characteristic equation of the battery equivalent circuit model;

performing pulse discharge operation based on a battery equivalent circuit model, and determining model parameter values under each operation;

and constructing an initial mapping relation expression of the model parameters, the temperature and the SOC value, determining undetermined coefficients in the mapping relation expression of the model parameters, the temperature and the SOC value through a curve fitting method, and determining the mapping relation of the model parameters, the temperature and the SOC value according to the undetermined coefficients.

4. A lithium ion battery SOC estimation apparatus, comprising:

the determining module is used for determining the mapping relation between each model parameter in the battery equivalent circuit model and the temperature and SOC value;

the construction module is used for constructing a battery discrete state space model according to the battery equivalent circuit model and the mapping relation between the parameters of each model and the temperature and the SOC value;

the estimation module is used for estimating the SOC value of the battery discrete state space model through an iterative extended Kalman filtering algorithm to determine the SOC value of the lithium ion battery;

the construction module is specifically configured to:

and (3) parallel running at least two Kalman filters, combining the battery equivalent circuit model and the mapping relation between the parameters of each model and the temperature and SOC values, establishing a battery discrete state space equation and a system measurement update equation, and defining a measurement matrix.

5. The lithium ion battery SOC estimation apparatus of claim 4, wherein the estimation module is specifically configured to:

initializing an iterative extended Kalman filtering algorithm at an initial moment, and determining an initial state estimated value and an initial state error covariance matrix at the initial moment;

and running all Kalman filtering in parallel, circularly executing a plurality of computing operations, executing the iterative extended Kalman filtering algorithm for a plurality of times in each computing operation, executing time updating and measuring updating iteration when executing the iterative extended Kalman filtering algorithm each time, and ending the computing operation until the iterative extended Kalman filtering algorithm reaches a cut-off condition to determine the SOC value of the lithium ion battery.

6. The lithium ion battery SOC estimation apparatus of claim 4, wherein the determination module is specifically configured to:

establishing a battery equivalent circuit model of a lithium ion battery, and establishing a characteristic equation of the battery equivalent circuit model;

performing pulse discharge operation based on a battery equivalent circuit model, and determining model parameter values under each operation;

and constructing an initial mapping relation expression of the model parameters, the temperature and the SOC value, determining undetermined coefficients in the mapping relation expression of the model parameters, the temperature and the SOC value through a curve fitting method, and determining the mapping relation of the model parameters, the temperature and the SOC value according to the undetermined coefficients.

7. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the lithium ion battery SOC estimation method of any of claims 1 to 3 when the computer program is executed by the processor.

8. A non-transitory computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements the lithium ion battery SOC estimation method according to any of claims 1 to 3.

Images