CN111505504B - Battery state of charge estimation method and estimator - Google Patents
Battery state of charge estimation method and estimator Download PDFInfo
- Publication number
- CN111505504B CN111505504B CN202010364716.5A CN202010364716A CN111505504B CN 111505504 B CN111505504 B CN 111505504B CN 202010364716 A CN202010364716 A CN 202010364716A CN 111505504 B CN111505504 B CN 111505504B
- Authority
- CN
- China
- Prior art keywords
- state
- voltage value
- battery
- value
- state model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- Secondary Cells (AREA)
Abstract
The application provides a battery state of charge estimation method and an estimator, wherein the method comprises the following steps: at least two continuous state models in the battery time domain are determined based on the actual state data of the battery, and the state models can output voltage values according to the input current value of the target time; processing a first voltage value based on an actual voltage value at a target moment to obtain a first processing result, wherein the first voltage value is obtained by calculating a first state model according to a current value at the target moment, and the first processing result is used for adjusting a second state model so as to improve the calculation precision of the second state model on the voltage value; adjusting a second state model based on the first processing result, and obtaining a second voltage value output by the second state model to the target moment; and processing the first voltage value and the second voltage value to obtain the state of charge of the battery at the target moment. The method and the device can be used for estimating the state of charge of the battery in real time, efficiently and accurately.
Description
Technical Field
The embodiment of the application relates to the field of battery management, in particular to a battery state of charge estimation method and a battery state of charge estimator.
Background
In the field of battery management, a battery state estimator is a primary but very critical functional module, and is very important to guarantee the safe operation of a battery and improve the energy management efficiency. In the state of the battery to be estimated, the state of charge (SOC) can reflect the remaining energy/capacity of the battery during use, thereby alleviating user anxiety and ensuring that the battery operates under reliable conditions. Therefore, the online effective estimation of the state of charge has very important significance.
The current estimation method for the state of charge of the power battery mainly comprises the following types: 1) SOC is calculated directly from a known initial SOC state or Open Circuit Voltage (OCV) -SOC curve using, for example, a coulomb counting method or an Open circuit voltage method, which is highly dependent on the setting of an initial value and is susceptible to noise interference, and reliable and consistent results cannot be obtained under complicated operating conditions. 2) The SOC estimation is performed by using machine learning techniques such as support vector machine, gaussian process regression, etc. to build a data-driven model, which requires a large amount of experimental data for training and has poor performance under new conditions. 3) An observer and estimator are designed to achieve online estimation of battery SOC based on a battery state model such as an Equivalent Circuit Model (ECM), an Electrochemical Model (EM), or a data driven model. The method can be widely used in the field of real-time battery state of charge estimation by effectively combining the prior information of the battery state model and the adaptive correction capability of the estimator.
However, the state estimator consumes a certain amount of computational effort and device memory, and many general-purpose Micro-controller units (MCUs) on the market do not have complex and computationally intensive work capabilities. Advanced estimators such as Unscented Kalman Filter (UKF) and Particle Filter (PF) have difficulty obtaining satisfactory battery state of charge estimation results in these MCUs.
Based on the defects of the method, it is urgently needed to develop a battery state-of-charge estimation method with low cost, low complexity and satisfactory performance to estimate the state-of-charge of the battery in real time under the condition of not greatly replacing the MCU.
Disclosure of Invention
The embodiment of the application provides a method which has simple steps, is easy for equipment processing and can carry out real-time, efficient and accurate state of charge estimation on a battery, and an estimator applying the battery state of charge estimation method.
In order to solve the above technical problem, an embodiment of the present application provides a method for estimating a state of charge of a battery, including:
at least two continuous state models in the battery time domain are determined based on the actual state data of the battery, and the state models can correspondingly output voltage values according to the input current values at the target time;
processing a first voltage value based on an actual voltage value at a target moment to obtain a first processing result, wherein the first voltage value is obtained by calculating a first state model according to a current value at the target moment, and the first processing result is used for adjusting a second state model to improve the calculation accuracy of the second state model on the voltage value;
adjusting the second state model based on the first processing result, and obtaining a second voltage value output by the second state model based on the target moment;
and processing the first voltage value and the second voltage value to obtain the state of charge of the battery at the target moment.
Preferably, the determining at least two continuous state models in the time domain of the battery based on the actual state data of the battery comprises:
acquiring actual state data of the battery in different environments during testing;
determining a state model architecture;
and performing parameter identification on the state model architecture based on the actual state data to obtain the first state model and the second state model.
Preferably, the acquiring actual state data of the battery in tests under different environments includes:
at least acquiring continuous time domain current data, voltage data, environment temperature data and environment humidity data of the battery during specific hybrid power pulse tests at different temperatures and/or humidities; and
and at least acquiring continuous time domain current data, voltage data, environment temperature data and environment humidity data of the battery during driving cycle condition tests at different temperatures and/or humidities.
Preferably, the processing the first voltage value output by the first state model for the target time based on the actual voltage value at the target time to obtain the first processing result includes:
determining a first difference between the first voltage value and an actual voltage value;
determining at least a first accumulated value of a first difference value between a first difference value at the target moment and a voltage value correspondingly output by the first state model at the previous moment and an actual voltage value at the previous moment;
and when the first accumulated value is determined to meet a specific condition, calculating a first correction value serving as the first processing result based on the first accumulated value and the specific condition, and adjusting and updating the second state model.
Preferably, the adjusting the second state model based on the first processing result and obtaining the second voltage value output by the second state model to the target time includes:
preferably, the processing the first voltage value and the second voltage value to obtain the state of charge of the battery at the target time includes:
determining a second difference between the first voltage value and a second voltage value;
determining at least a second accumulated value of a second difference value between the first voltage value and a second voltage value at a previous time and a second difference value at the target time;
and when the second accumulated value is determined to meet a specific condition, calculating the state of charge of the battery at the target moment based on the second accumulated value and the specific condition.
Preferably, the method further comprises the following steps:
calculating to obtain a second correction value at the target moment based on the second accumulated value and a specific condition;
and inputting the calculated second correction value into the first state model to adjust the first state model and improve the calculation accuracy of the first state model on the voltage value.
The embodiment of the present invention also provides a battery state of charge estimator, including:
a processor, which is used for determining at least two continuous state models in the battery time domain based on the actual state data of the battery, wherein the state models can correspond to output voltage values according to the input current values at the target time;
the second-order sigma-delta modulator comprises a nonlinear model, and is used for processing a first voltage value output by a first state model to a target moment based on an actual voltage value of the target moment to obtain a first processing result, wherein the first voltage value is obtained by calculating the first state model according to a current value of the target moment, and the first processing result is used for adjusting a second state model to improve the calculation precision of the second state model to the voltage value;
adjusting a second state model based on the first processing result, and obtaining a second voltage value output by the second state model to the target moment;
and processing the first voltage value and the second voltage value to obtain the state of charge of the battery at the target moment.
As a preference, the first and second liquid crystal compositions are,
the processor is further configured to:
acquiring actual state data of the battery in different environments during testing;
determining a state model architecture;
and performing parameter identification on the state model architecture based on the actual state data to obtain the first state model and the second state model.
Preferably, the second-order sigma-delta modulator with the nonlinear model comprises two closed-loop feedback paths, each closed-loop feedback path comprises an integrator, a comparator, an internal filter, an external filter and a state model which are connected in sequence, and data output by the state model in one closed-loop feedback path is processed and then enters the closed-loop feedback path through the integrator in the other closed-loop feedback path.
Based on the disclosure of the above embodiments, it can be known that the embodiments of the present application have beneficial effects including:
(1) the lightweight estimation method for the state of charge of the battery can realize effective and accurate state of charge estimation under different battery aging conditions, and can meet the requirements of a common commercial battery management system.
(2) The battery state of charge estimation method can greatly reduce the calculation time and has good robustness, and the method has practicability in the field of electric vehicles or hybrid electric vehicles.
(3) The battery state of charge estimation method adopts an oversampling technology and is suitable for non-Gaussian conditions.
(4) The battery state of charge estimator can adaptively correct estimation deviation even under the condition of using a simple ideal state model by establishing a closed loop structure based on a second-order sigma-delta framework.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
Drawings
Fig. 1 is a flowchart of a state of charge estimation method according to an embodiment of the present invention.
FIG. 2 is a block diagram of a state of charge estimation method according to another embodiment of the present invention (in the diagram, ILF1 and ILF2 represent two internal low pass filters, ELF represents an external low pass filter, BSM1 and BSM2 represent a first state model and a second state model, respectively, VBSM2(K) Representing a first voltage value, D1(K) Represents a first difference, S1(K) Representing a first accumulated value, CM1Representing the comparator output signal, Z1(K) Represents the first correction value, Itest(k) Real-time current value representing K time point, D2(K) Represents the second difference, S2(K) Represents a second accumulated value, Z2(K) Represents a second correction value, VBSM1(K) Representing a second voltage value, soc (k) representing a battery state of charge value).
FIG. 3 is a flowchart illustrating a state of charge estimation method according to another embodiment of the present invention.
Fig. 4 is a block diagram of an estimator according to an embodiment of the present invention.
Detailed Description
Specific embodiments of the present application will be described in detail below with reference to the accompanying drawings, but the present application is not limited thereto.
It will be understood that various modifications may be made to the embodiments disclosed herein. The following description is, therefore, not to be taken in a limiting sense, but is made merely as an exemplification of embodiments. Other modifications will occur to those skilled in the art within the scope and spirit of the disclosure.
The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the disclosure and, together with a general description of the disclosure given above, and the detailed description of the embodiments given below, serve to explain the principles of the disclosure.
These and other characteristics of the present application will become apparent from the following description of preferred forms of embodiment, given as non-limiting examples, with reference to the attached drawings.
It should also be understood that, although the present application has been described with reference to some specific examples, a person of skill in the art shall certainly be able to achieve many other equivalent forms of application, having the characteristics as set forth in the claims and hence all coming within the field of protection defined thereby.
The above and other aspects, features and advantages of the present disclosure will become more apparent in view of the following detailed description when taken in conjunction with the accompanying drawings.
Specific embodiments of the present disclosure are described hereinafter with reference to the accompanying drawings; however, it is to be understood that the disclosed embodiments are merely examples of the disclosure that may be embodied in various forms. Well-known and/or repeated functions and structures have not been described in detail so as not to obscure the present disclosure with unnecessary or unnecessary detail. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the present disclosure in virtually any appropriately detailed structure.
The specification may use the phrases "in one embodiment," "in another embodiment," "in yet another embodiment," or "in other embodiments," which may each refer to one or more of the same or different embodiments in accordance with the disclosure.
Hereinafter, embodiments of the present application will be described in detail with reference to the accompanying drawings.
As shown in fig. 1, an embodiment of the present application provides a method for estimating a state of charge of a battery, including:
s100, at least two continuous state models in the battery time domain are determined based on the actual state data of the battery, and the state models can correspondingly output voltage values according to the input current value at the target moment;
s200, processing a first voltage value based on an actual voltage value at a target moment to obtain a first processing result, wherein the first voltage value is obtained by calculating a first state model according to a current value at the target moment, and the first processing result is used for adjusting a second state model to improve the calculation precision of the second state model on the voltage value;
s300, adjusting a second state model based on the first processing result, and obtaining a second voltage value output by the second state model to the target moment;
and S400, processing the first voltage value and the second voltage value to obtain the state of charge of the battery at the target moment.
Specifically, based on the disclosure of the above embodiments, the method for estimating the state of charge of the battery in a lightweight manner has the advantages that effective and accurate state of charge estimation can be realized under different aging conditions of the battery, the requirement of a general commercial battery management system can be met, the calculation time can be greatly reduced, good robustness is shown, and the method has higher practicability in the field of electric vehicles or hybrid electric vehicles. In addition, the battery state of charge estimation method of the application adopts an oversampling technology and is suitable for non-Gaussian situations.
Specifically, as shown in fig. 2 and fig. 3, in the embodiment of the present application, determining at least two continuous state models in the time domain of the battery based on the actual state data of the battery includes:
s110, collecting actual state data of the battery in different environments for testing;
s120, determining a state model architecture;
s130, performing parameter identification on the state model architecture based on the actual state data to obtain at least a first state model and a second state model with the same identification parameters.
For example, a power battery to be tested is selected firstly, then battery test experiments under different environments are designed, actual state data of the battery are collected, and a continuous state model in the battery time domain is established according to the collected battery state data. The method specifically comprises the following steps:
at least acquiring continuous time domain current data, voltage data, environment temperature data and environment humidity data of the battery during specific tests of hybrid power pulses at different temperatures and/or humidities; or
And at least acquiring continuous time domain current data, voltage data, environment temperature data and environment humidity data of the battery under different temperatures and/or humidities during driving cycle condition testing.
For example, Hybrid Pulse Power Characteristics (HPPC) tests, driving cycle condition tests, and collecting actual state data B of the battery are performed on selected power batteries at different temperaturestest(ii) a Obtaining a one-to-one correspondence relationship diagram including but not limited to the battery current I and the voltage V, a one-to-one correspondence relationship diagram between an Open Circuit Voltage (OCV) and a State of Charge (SOC), and a one-to-one correspondence relationship diagram between a battery internal Resistance (R) and an ambient Temperature (T); collecting continuous time domain current data I of battery under different driving conditionstestVoltage data VtestAnd ambient temperature data Ttest；
Then selecting a Rint equivalent circuit model (an ideal circuit model) as the type of the battery state model, and performing parameter identification on the selected Rint equivalent circuit model by using a least square method according to data obtained by the acquired I-V relation graph, OCV-SOC relation graph and R-T relation graph to finally obtain a continuous state model BSM in the time domain of the battery; the state model in this embodiment includes a first state model and a second state model, and the two state models have the same identification parameter. Of course, more state models may be provided, without limitation.
Further, after the state model is built, the state model may be adjusted and processed according to the input current value and the corresponding output voltage value to obtain a final estimated battery state of charge value. When the method is applied, an estimator for estimating the state of charge of the battery can be designed according to a second-order sigma-delta technology, so that the designed state of charge estimator is utilized to estimate the state of charge of the battery in real time. Specifically, as shown in S21 in fig. 3, a second-order sigma-delta battery state-of-charge estimator is provided, and the estimator in the embodiment of the present application has a double closed-loop feedback path, and the double closed-loop feedback path is composed of two integrators 1 and 2, two comparators 1 and 2, two Internal low-pass filters (Internal low-pass filters 1,2ILF1 and ILF2), one External low-pass filter (ELF), and two state models BSM1 and BSM2 having the same identification parameters, and the specific structure can be referred to fig. 2. The low-pass filter may be replaced by a high-order low-pass filter or a moving average filter.
Specifically, in this embodiment, processing the first voltage value output to the target time by the first state model based on the actual voltage value at the target time to obtain the first processing result includes:
s210, determining a first difference value between the first voltage value and an actual voltage value;
s220, at least determining a first accumulated value of a first difference value between a first difference value at the target moment and a voltage value correspondingly output by the first state model at the last moment and an actual voltage value at the last moment;
and S230, when the first accumulated value is determined to meet a specific condition, calculating a first correction value serving as the first processing result based on the first accumulated value and the specific condition, and adjusting and updating the second state model.
For example, as shown in FIG. 3 at S22-S24Aiming at the actual battery state data B collected in the stepstestInputting the collected real-time current I at each time point ktest(k) The output voltage V is obtained by means of a state model BSM2BSM2(K) I.e. the first voltage value, calculates the actual test voltage V at the point of time ktest(k) And state model BSM2 output voltage VBSM2(K) First difference D of1(K)＝Vtest(k)-VBSM2(K) The first difference D is integrated with an Integrator11(K) And accumulating, wherein the calculation formula is as follows:
S1(K)＝S1(K-1)+D1(K)
wherein S1(K) D at the k time point1Accumulated value of difference, i.e. first accumulated value, S1(K-1) is D at the K-1 st time point1Is added up.
The obtained first accumulated value S is compared with the Comparator Comparator11(k) Make a judgment if S1(k) If it is greater than 0, the Comparator1 outputs the signal CM1If 1, S1(k) Less than or equal to 0, the comparator outputs a signal CM1＝0；
Signal CM based on Comparator1 output1The first correction value Z for the current time k is calculated by an internal low-pass filter ILF11(k) (ii) a When a simple low-pass filter is used, Z1(k) The calculation formula of (a) is as follows:
Z1(K)＝Z1(K-1)*(1-α1)+CM1*α1
wherein Z1(k-1) is the output value, α, of ILF1 at time point k-11Set to 0.03.
Further, in this embodiment, adjusting the second state model based on the first processing result, and obtaining the second voltage value output by the second state model to the target time includes:
and S310, inputting the first processing result and the current value at the target moment into the second state model to obtain the second voltage value.
That is, continuing the previous embodiment, a first correction is determinedAfter the value, the first correction value and the current value I at the moment k are calculatedtest(k) Input into the state model BSM1 to obtain a second voltage value VBSM1(K)。
Further, in this embodiment, the processing the first voltage value and the second voltage value to obtain the state of charge of the battery at the target time includes:
s401, determining a second difference value between the first voltage value and the second voltage value;
s402, at least determining a second accumulated value of a second difference value between a second difference value at the target moment and a first voltage value and a second voltage value at the previous moment;
and S403, when the second accumulated value is determined to meet a specific condition, calculating the state of charge of the battery at the target moment based on the second accumulated value and the specific condition.
For example, as shown in S25-26 in FIG. 3, the difference D between the output voltages of BSM1 and BSM2 at time k is calculated2(k)＝VBSM1(k)-VBSM2(k) The difference D is integrated by an Integrator22(k) And accumulating, wherein the calculation formula is as follows:
S2(K)＝S2(K-1)+D2(K)
wherein S2(k) Is D at the k-th time point2Accumulated value of difference, S2(k-1) is D at the k-1 st time point2Accumulated values of the difference values;
signal CM output by Comparator2 based on step S252The correction value Z at the current time k is calculated by an internal low-pass filter ILF22(k) Simultaneously, estimating the state of charge (SOC), (k) of the battery at the current k moment by using an external low-pass filter ELF, wherein the ILF2 and the ELF comprise but are not limited to a simple low-pass filter, a high-order low-pass filter and a moving average filter; in this embodiment, a simple low-pass filter, Z, is used2(k) And SOC (k) is calculated as follows:
Z2(K)＝Z2(K-1)*(1-α2)+CM2*α2
SOC(k)＝SOC(k)*(1-β)+CM2*β
wherein Z2(k-1) is the output value of ILF2 at time point k-1, and SOC (k-1) is the estimated battery state of charge, α, at time point k-12Set to 0.3, β is set to 0.005;
further, the method in this embodiment further includes:
and S500, when the second accumulated value meets a specific condition, calculating the state of charge of the battery at the target moment based on the second accumulated value and the specific condition, and inputting the state of charge of the battery at the target moment into the first state model to obtain a second correction value of the voltage value at the next moment of the target moment.
That is, as shown by S27 in fig. 3, the second correction value Z is corrected2(K) The voltage values output by the state model BSM2 at the k +1 th time point are prepared for being input into the state model BSM 2.
For each driving cycle working condition of the battery, as shown in S28 in fig. 3, the actual current I _ test (k) and the voltage V _ test (k) of the battery are collected at each time point k, and the state of charge soc (k) is sequentially estimated for the current time point k in the working condition time domain by using the above steps, so that a real-time state of charge estimated value of the selected power battery under the driving cycle working condition can be obtained.
As shown in fig. 4, an embodiment of the present application also provides a battery state of charge estimator, including:
a processor 410, configured to determine at least two state models that are continuous in the battery time domain based on actual state data of the battery, where the state models are configured to output voltage values according to input current values at target time;
the second-order sigma-delta modulator 420 comprises a nonlinear model, and is used for processing a first voltage value based on an actual voltage value at a target moment to obtain a first processing result, wherein the first voltage value is obtained by calculating the first state model according to a current value at the target moment, and the first processing result is used for adjusting a second state model to improve the calculation accuracy of the second state model on the voltage value;
adjusting a second state model based on the first processing result, and obtaining a second voltage value output by the second state model to the target moment;
and processing the first voltage value and the second voltage value to obtain the state of charge of the battery at the target moment.
Specifically, based on the disclosure of the above embodiments, the beneficial effects include that the second-order sigma-delta framework-based closed-loop structure is established, the estimation deviation can be adaptively corrected even under the condition of using a simple Rint state model, effective and accurate state of charge estimation can be realized under different battery aging conditions, the requirements of a general commercial battery management system can be met, the calculation time can be greatly reduced, good robustness is presented, and the method has higher practicability in the field of electric vehicles or hybrid electric vehicles. In addition, the battery state of charge estimation method of the application adopts an oversampling technology and is suitable for non-Gaussian situations.
Specifically, in the embodiment of the present application, the determining, by the processor 410, at least two continuous state models in the time domain of the battery based on the actual state data of the battery includes:
acquiring actual state data of the battery in different environments during testing;
determining a state model architecture;
and performing parameter identification on the state model architecture based on the actual state data to obtain at least a first state model and a second state model with the same identification parameters.
For example, a power battery to be tested is selected firstly, then a battery test experiment under different environments is designed, actual state data of the battery is collected, and a continuous state model in the battery time domain is established according to the collected battery state data. The method specifically comprises the following steps:
at least acquiring continuous time domain current data, voltage data, environment temperature data and environment humidity data of the battery during specific hybrid power pulse tests at different temperatures and/or humidities; or
And at least acquiring continuous time domain current data, voltage data, environment temperature data and environment humidity data of the battery during driving cycle condition tests at different temperatures and/or humidities.
For example, a Hybrid Pulse Power Characteristic (HPPC) test and a driving cycle condition test are performed on the selected power battery at different temperatures, and actual state data B _ test of the battery is acquired; obtaining a one-to-one corresponding relationship diagram including but not limited to the battery current I and the voltage V, a one-to-one corresponding relationship diagram of an Open Circuit Voltage (OCV) and a State of Charge (SOC), and a one-to-one corresponding relationship diagram of a battery internal Resistance (R) and an ambient Temperature (T); collecting continuous time domain current data I _ test, voltage data V _ test and environment temperature data T _ test of a battery under different driving conditions;
selecting a Rint equivalent circuit model (an ideal circuit model) as a battery state model type, and performing parameter identification on the selected Rint equivalent circuit model by using a least square method according to data obtained by the acquired I-V relation diagram, OCV-SOC relation diagram and R-T relation diagram to finally obtain a BSM (state-based model) of the battery time domain; the state model in this embodiment includes a first state model and a second state model, and the two state models have the same identification parameter. Of course, more state models may be provided, without limitation.
Further, as shown in fig. 2, after the state model is built, the state model may be adjusted and processed according to the input current value and the corresponding output voltage value to obtain the final estimated battery state of charge value. When the method is applied, an estimator for assisting in estimating the state of charge of the battery can be designed according to a second-order sigma-delta technology, so that the designed state of charge estimator is utilized to estimate the state of charge of the battery in real time. Specifically, the estimator in the embodiment of the present application has a dual closed-loop feedback path, which is composed of the second-order sigma-delta modulator containing the nonlinear model in the embodiment and two state models, the closed-loop feedback path each includes an Integrator, a Comparator, an Internal filter, an External filter and a state model connected in sequence, wherein data output by the state model in one closed-loop feedback path is processed and then enters the closed-loop feedback path through the Integrator in the other closed-loop feedback path, as shown in fig. 2, which is composed of two integrators Integrator1 and Integrator2, two comparators composer 1 and composer 2, two Internal low-pass filters (Internal low-pass filter 1,2ILF1 and ILF2), an External low-pass filter (External low-pass filter ELF) and two state models BSM1 and BSM2 with the same identification parameter, the specific structure can be seen in fig. 2. The low-pass filter may be replaced by a high-order low-pass filter or a moving average filter.
Specifically, in this embodiment, the processing, by the modulator 420, the first voltage value output to the target time by the first state model based on the actual voltage value at the target time to obtain the first processing result includes:
determining a first difference between the first voltage value and an actual voltage value;
determining at least a first accumulated value of a first difference value between a first difference value at the target moment and a voltage value correspondingly output by the first state model at the previous moment and an actual voltage value at the previous moment;
and when the first accumulated value is determined to meet a specific condition, calculating a first correction value serving as the first processing result based on the first accumulated value and the specific condition, and adjusting and updating the second state model.
For example, with respect to the battery actual state data B collected in step S11testInputting the collected real-time current I at each time point ktest(k) The output voltage V is obtained by means of a state model BSM2BSM2(K) I.e. the first voltage value, calculates the actual test voltage V at the point of time ktest(k) And state model BSM2 output voltage VBSM2(K) First difference D of1(K)＝Vtest(k)-VBSM2(K) The first difference D is integrated with an Integrator11(K) And accumulating, wherein the calculation formula is as follows:
S1(K)＝S1(K-1)+D1(K)
wherein S1(K) Is D at the k-th time point1Difference valueAccumulated value, i.e. first accumulated value, S1(K-1) is D at the K-1 th time point1The accumulated value of the difference values.
The obtained first accumulated value S is compared with a Comparator Comparator11(k) Making a judgment if S1(k) If it is greater than 0, the Comparator1 outputs the signal CM1If 1, S1(k) Less than or equal to 0, the comparator outputs a signal CM1＝0；
Based on the signal CM output by the Comparator Comparator11The first correction value Z for the current time k is calculated by an internal low-pass filter ILF11(k) (ii) a When a simple low-pass filter is used, Z1(k) The calculation formula of (c) is as follows:
Z1(K)＝Z1(K-1)*(1-α1)+CM1*α1
wherein Z1(k-1) is the output value, α, of ILF1 at time point k-11Set to 0.03.
Further, in this embodiment, adjusting the second state model based on the first processing result, and obtaining the second voltage value output by the second state model to the target time includes:
and inputting the first processing result and the current value at the target moment into the second state model to obtain the second voltage value.
That is, continuing the above embodiment, the first correction value and the current value I at time k are set after the first correction value is determinedtest(k) Input into the state model BSM1 to obtain a second voltage value VBSM1(K)。
Further, in this embodiment, the processing the first voltage value and the second voltage value to obtain the state of charge of the battery at the target time includes:
determining a second difference between the first voltage value and a second voltage value;
determining at least a second accumulated value of a second difference between the second difference at the target time and the first voltage value and the second voltage value at the previous time;
and when the second accumulated value is determined to meet a specific condition, calculating the state of charge of the battery at the target moment based on the second accumulated value and the specific condition.
For example, the difference D between the output voltages of BSM1 and BSM2 at time k is calculated2(k)＝VBSM1(k)-VBSM2(k) The difference D is integrated by an Integrator22(k) And accumulating, wherein the calculation formula is as follows:
S2(K)＝S2(K-1)+D2(K)
wherein S2(k) D at the k time point2Accumulated value of difference, S2(k-1) is D at the k-1 th time point2Accumulated values of the difference values;
signal CM output by Comparator2 based on step S252The correction value Z at the current time k is calculated by an internal low-pass filter ILF22(k) Simultaneously, estimating the state of charge (SOC), (k) of the battery at the current k moment by using an external low-pass filter ELF, wherein the ILF2 and the ELF comprise but are not limited to a simple low-pass filter, a high-order low-pass filter and a moving average filter; in this embodiment, a simple low-pass filter, Z, is used2(k) And SOC (k) is calculated as follows:
Z2(K)＝Z2(K-1)*(1-α2)+CM2*α2
SOC(k)＝SOC(k)*(1-β)+CM2*β
wherein Z2(k-1) is the output value of ILF2 at time point k-1, and SOC (k-1) is the estimated battery state of charge, α, at time point k-12Set to 0.3, β is set to 0.005;
further, the modulator in the present embodiment is further configured to:
and when the second accumulated value is determined to meet a specific condition, calculating and obtaining a second correction value used for being input into the first state model to obtain the voltage value at the next moment of the target moment based on the second accumulated value and the specific condition.
I.e. the second correction value Z2(K) The voltage values output by the state model BSM2 at the k +1 th time point are prepared for being input into the state model BSM 2.
And aiming at each driving cycle working condition of the battery, acquiring the actual current I _ test (k) and the actual voltage V _ test (k) of the battery at each time point k, and sequentially estimating the state of charge (SOC) (k) of the current time point k in the working condition time domain by utilizing the steps to obtain the real-time state of charge estimated value of the selected power battery under the driving cycle working condition.
The above embodiments are only exemplary embodiments of the present application, and are not intended to limit the present application, and the protection scope of the present application is defined by the claims. Various modifications and equivalents may be made by those skilled in the art within the spirit and scope of the present application and such modifications and equivalents should also be considered to be within the scope of the present application.
Claims (9)
1. A method of estimating a state of charge of a battery, comprising:
at least two continuous state models in the battery time domain are determined based on the actual state data of the battery, and the state models can output corresponding voltage values according to the input current values at the target time;
processing a first voltage value based on an actual voltage value at a target moment to obtain a first processing result, wherein the first voltage value is obtained by calculating a first state model according to a current value at the target moment, and the first processing result is used for adjusting a second state model so as to improve the calculation precision of the second state model on the voltage value;
adjusting the second state model based on the first processing result, and obtaining a second voltage value output by the second state model based on the current value of the target moment;
processing the first voltage value and the second voltage value to obtain the state of charge of the battery at the target moment;
the processing the first voltage value and the second voltage value to obtain the state of charge of the battery at the target moment comprises:
determining a second difference between the first voltage value and a second voltage value;
calculating a second accumulated value according to the current second difference value at the target moment and a second difference value at the last moment between the first voltage value and the second voltage value at the last moment;
and when the second accumulated value meets a specific condition, calculating the state of charge of the battery at the target time based on the second accumulated value and the specific condition.
2. The method of claim 1, wherein determining at least two state models that are continuous in the time domain of the battery based on the actual state data of the battery comprises:
acquiring actual state data of the battery in different environments during testing;
determining a state model architecture;
and performing parameter identification on the state model architecture based on the actual state data to obtain the first state model and the second state model.
3. The method of claim 2, wherein collecting actual state data of the battery when tested in different environments comprises:
at least acquiring continuous time domain current data, voltage data, environment temperature data and environment humidity data of the battery when the battery is subjected to hybrid power pulse testing at different temperatures and/or humidities; and
and at least acquiring continuous time domain current data, voltage data, environment temperature data and environment humidity data when the battery is subjected to driving cycle condition tests at different temperatures and/or humidities.
4. The method of claim 1, wherein processing the first voltage value based on the actual voltage value at the target time to obtain a first processing result comprises:
determining a first difference between the first voltage value and an actual voltage value;
calculating to obtain a first accumulated value according to the current first difference at the target moment and a first difference at the last moment between the voltage value correspondingly output by the first state model at the last moment and the actual voltage value at the last moment;
and when the first accumulated value meets a specific condition, calculating a first correction value serving as the first processing result based on the first accumulated value and the specific condition, wherein the first correction value is used for adjusting and updating the second state model.
5. The method of claim 1, wherein adjusting the second state model based on the first processing result and obtaining a second voltage value that the second state model outputs based on the current value at the target time comprises:
inputting the first processing result into the second state model to obtain an adjusted second state model;
and inputting the current value at the target moment into the adjusted second state model to obtain the second voltage value.
6. The method of claim 5, further comprising:
calculating to obtain a second correction value at the target moment based on the second accumulated value and a specific condition;
and inputting the calculated second correction value into the first state model to adjust the first state model and improve the calculation accuracy of the first state model on the voltage value.
7. A battery state of charge estimator, comprising:
the processor is used for determining at least two continuous state models in the battery time domain based on the actual state data of the battery, and the state models can output corresponding voltage values according to the input current values at the target time;
the second-order sigma-delta modulator comprises a nonlinear model and is used for processing a first voltage value based on an actual voltage value at a target moment to obtain a first processing result, wherein the first voltage value is obtained by calculating a first state model according to a current value at the target moment, and the first processing result is used for adjusting a second state model to improve the calculation precision of the second state model on the voltage value;
adjusting the second state model based on the first processing result, and obtaining a second voltage value output by the second state model based on the current value of the target moment;
processing the first voltage value and the second voltage value to obtain the state of charge of the battery at the target moment;
the processing the first voltage value and the second voltage value to obtain the state of charge of the battery at the target moment comprises:
determining a second difference between the first voltage value and a second voltage value;
calculating a second accumulated value according to the current second difference value at the target moment and a second difference value at the last moment between the first voltage value and the second voltage value at the last moment;
and when the second accumulated value meets a specific condition, calculating the state of charge of the battery at the target time based on the second accumulated value and the specific condition.
8. The battery state of charge estimator of claim 7, wherein the processor is further configured to:
acquiring actual state data of the battery in different environments during testing;
determining a state model architecture;
and performing parameter identification on the state model architecture based on the actual state data to obtain the first state model and the second state model.
9. The battery state-of-charge estimator of claim 8, wherein said second order sigma-delta modulator with non-linear model comprises two closed loop feedback paths, each of said closed loop feedback paths comprising an integrator, a comparator, an internal filter, an external filter, and a state model connected in sequence, wherein data output by the state model in one of said closed loop feedback paths is processed and then enters the closed loop feedback path through the integrator in the other of said closed loop feedback paths.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010364716.5A CN111505504B (en) | 2020-04-30 | 2020-04-30 | Battery state of charge estimation method and estimator |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010364716.5A CN111505504B (en) | 2020-04-30 | 2020-04-30 | Battery state of charge estimation method and estimator |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111505504A CN111505504A (en) | 2020-08-07 |
| CN111505504B true CN111505504B (en) | 2022-07-19 |
Family
ID=71876571
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010364716.5A Active CN111505504B (en) | 2020-04-30 | 2020-04-30 | Battery state of charge estimation method and estimator |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111505504B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113189500B (en) * | 2021-04-28 | 2023-02-03 | 深圳市杉川锐创科技有限公司 | Battery electric quantity calculation method and device, computer equipment and storage medium |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103675703A (en) * | 2013-11-30 | 2014-03-26 | 西安交通大学 | Method for estimating charge state of battery |
| WO2016059126A1 (en) * | 2014-10-17 | 2016-04-21 | Jaguar Land Rover Limited | Battery condition monitoring |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP4830382B2 (en) * | 2005-07-19 | 2011-12-07 | 日産自動車株式会社 | Secondary battery charge rate estimation device |
| WO2016132813A1 (en) * | 2015-02-19 | 2016-08-25 | 三菱電機株式会社 | Battery state estimation device |
| EP3593155B1 (en) * | 2017-03-06 | 2021-04-07 | Volvo Truck Corporation | A battery cell state of charge estimation method and a battery state monitoring system |
-
2020
- 2020-04-30 CN CN202010364716.5A patent/CN111505504B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103675703A (en) * | 2013-11-30 | 2014-03-26 | 西安交通大学 | Method for estimating charge state of battery |
| WO2016059126A1 (en) * | 2014-10-17 | 2016-04-21 | Jaguar Land Rover Limited | Battery condition monitoring |
Non-Patent Citations (1)
| Title |
|---|
| 《基于分数阶理论的车用锂离子电池建模及荷电状态估计》;刘树林 等;《电工技术学报》;20170228;第32卷(第4期);第189-195页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111505504A (en) | 2020-08-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111581904B (en) | Lithium battery SOC and SOH collaborative estimation method considering cycle number influence | |
| CN110068772B (en) | Lithium ion battery state of charge estimation method based on improved fractional order model | |
| CN109061520B (en) | Power battery health and power state online estimation method and system | |
| WO2021197038A1 (en) | Method and device for determining state of charge of battery, and battery management system | |
| CN105548896B (en) | Power battery SOC line closed loop estimation method based on N-2RC model | |
| CN110261778B (en) | SOC estimation algorithm of lithium ion battery | |
| CN110196393B (en) | Combined on-line estimation method for lithium battery charge state, energy state and power state | |
| CN109669132B (en) | Battery state of charge estimation method based on variational Bayesian filtering | |
| He et al. | State of charge estimation by finite difference extended Kalman filter with HPPC parameters identification | |
| CN109633479B (en) | Lithium battery SOC online estimation method based on embedded type volume Kalman filtering | |
| CN112345939B (en) | Lithium ion battery model parameter identification method based on continuous impulse response | |
| CN111624496B (en) | Method and system for jointly estimating parameters and SOC (state of charge) of anti-noise lithium ion battery model | |
| CN109633453A (en) | Battery parameter on-line identification method, apparatus and computer readable storage medium | |
| CN112305440A (en) | Battery remaining capacity and health state joint estimation method | |
| CN114184962B (en) | Multi-algorithm fusion lithium ion battery SOC and SOH joint estimation method | |
| CN112433154A (en) | Lithium ion battery SOC estimation algorithm based on FFRLS and EKF | |
| CN114035072A (en) | Battery pack multi-state joint estimation method based on cloud edge cooperation | |
| CN110673037B (en) | Battery SOC estimation method and system based on improved simulated annealing algorithm | |
| CN111142025A (en) | Battery SOC estimation method and device, storage medium and electric vehicle | |
| CN112858916A (en) | Battery pack state of charge estimation method based on model and data drive fusion | |
| CN110716146A (en) | Estimation method of power battery open circuit voltage | |
| CN111505504B (en) | Battery state of charge estimation method and estimator | |
| WO2021217599A1 (en) | Method for estimating state of charge of battery, and estimator | |
| CN109782182B (en) | Online estimation method and device for energy state of series battery pack | |
| CN115047357A (en) | Combined estimation method for lithium ion battery state |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |