CN112540314A - UKF-based SOC estimation method for lithium battery of electric vehicle - Google Patents
UKF-based SOC estimation method for lithium battery of electric vehicle Download PDFInfo
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- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 title claims abstract description 49
- 229910052744 lithium Inorganic materials 0.000 title claims abstract description 49
- 238000000034 method Methods 0.000 title abstract description 13
- 238000001514 detection method Methods 0.000 claims abstract description 17
- 238000012937 correction Methods 0.000 claims abstract description 14
- 238000012545 processing Methods 0.000 claims abstract description 13
- 238000006243 chemical reaction Methods 0.000 claims description 7
- 230000010287 polarization Effects 0.000 claims description 6
- 230000000694 effects Effects 0.000 claims description 4
- 238000005259 measurement Methods 0.000 claims description 4
- 238000012935 Averaging Methods 0.000 claims description 3
- 239000003990 capacitor Substances 0.000 claims description 3
- 230000008878 coupling Effects 0.000 claims description 3
- 238000010168 coupling process Methods 0.000 claims description 3
- 238000005859 coupling reaction Methods 0.000 claims description 3
- 238000002955 isolation Methods 0.000 claims description 3
- 239000011159 matrix material Substances 0.000 claims description 3
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- 230000009466 transformation Effects 0.000 claims description 3
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- 238000004364 calculation method Methods 0.000 abstract description 2
- 238000013461 design Methods 0.000 description 2
- 238000007599 discharging Methods 0.000 description 2
- 238000010998 test method Methods 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 1
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- 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]
- G01R31/385—Arrangements for measuring battery or accumulator variables
- G01R31/387—Determining ampere-hour charge capacity or SoC
- G01R31/388—Determining ampere-hour charge capacity or SoC involving voltage measurements
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- 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
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- 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]
- G01R31/389—Measuring internal impedance, internal conductance or related variables
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Abstract
The invention discloses a UKF-based SOC estimation method for a lithium battery of an electric vehicle, which relates to the field of SOC estimation methods for the lithium battery, and comprises five steps of (A) establishing a Thevenin-based improved circuit model of the lithium battery according to the dynamic characteristics of the lithium battery; (B) establishing a functional relation between parameters of the Thevenin-based improved circuit model and SOC (system on chip), and establishing a state space model of the battery system; (C) carrying out discretization processing on the state space model of the battery system; (D) establishing a lithium battery SOC estimation model based on a UKF algorithm to estimate the SOC of the lithium battery; (E) and (3) building a hardware platform of the battery detection system, and verifying the applicability of the UKF algorithm. The method can effectively solve the problems of parameter change, inaccurate noise statistical characteristic and self-correction characteristic in the system state calculation process, reduce linear errors and improve the estimation precision of the SOC.
Description
Technical Field
The invention relates to the field of lithium battery SOC estimation methods, in particular to UKF-based electric vehicle lithium battery SOC estimation.
Background
The accurate estimation of the SOC is an important basis for the charge and discharge control and power optimization management of the battery of the electric automobile. Scholars at home and abroad carry out a great deal of research on the SOC estimation of the lithium battery and provide various scientific methods for SOC estimation. The discharging test method can obtain a relatively accurate SOC estimated value, but the ongoing work of the battery is interrupted, so that the discharging test method cannot be applied to actual vehicles; although the current integration method can estimate the SOC of the battery in real time, the initial value cannot be automatically determined, the error is further increased along with the time, and the estimation is inaccurate due to the accumulation of the error; the open-circuit voltage method can only be accurately estimated when the battery current is zero, and the battery is required to be left standing for a long enough time, so that the estimation cannot be carried out in real time.
At present, various design schemes of a lithium battery SOC estimation and detection platform are provided, a power resistor is used for detecting current, an external operational amplifier is used for A/D conversion, and then voltage and current values are obtained through measurement, so that the method is low in precision and complex in external circuit; the integrated chip is used for detecting voltage and current, the accuracy of the mode is high, however, an external circuit is complex, and the design cost is high.
Disclosure of Invention
The invention aims to solve the technical problems, and adopts the technical scheme that the SOC of the lithium battery of the electric automobile is estimated based on the UKF, so that the SOC of the lithium battery is estimated quickly and accurately.
The UKF-based SOC estimation method for the lithium battery of the electric automobile comprises the following steps:
(A) according to the dynamic characteristics of the lithium battery, establishing a Thevenin-based improved circuit model of the lithium battery;
the Thevenin-based improved circuit model comprises a KiBaM model and a Thevenin model of battery self-discharge effect, the KiBaM model estimates the capacity and the SOC of a battery, and the Thevenin model comprises the internal resistance R of the battery1Polarization resistance R2Polarization capacitance CpSaid polarization resistance R2And a polarization capacitor CpAfter being connected in parallel, the battery is connected with the internal resistance R of the battery1Are connected in series.
(B) Establishing a functional relation between parameters of the Thevenin-based improved circuit model and SOC (system on chip), and establishing a state space model of the battery system;
(C) carrying out discretization processing on the state space model of the battery system;
(D) establishing a lithium battery SOC estimation model based on a UKF algorithm to estimate the SOC of the lithium battery;
(E) and (3) building a hardware platform of the battery detection system, and verifying the applicability of the UKF algorithm.
Preferably, the battery system state space model is:
UL(t)＝f[SOC(t)]-R1·iL(t)-Up(t)
therein, SOC0For the last moment of the battery residual capacity, Q0Is the rated capacity of the battery, etaTIs a temperature correction coefficient, ηIIs a coulomb efficiency correction factor.
Preferably, the discretization state space model of the battery system is as follows:
UL(k)＝f(SOCk)-R1·iL(k)-Up(k)
preferably, the estimation of the lithium battery SOC by the lithium battery SOC estimation model based on the UKF algorithm comprises (1) prediction of SOC and (2) correction of SOC;
(1) prediction of SOC
Initializing SOC value and covariance at k-1 moment, and generating 2n +1 delta points near the state at the moment by UT conversion:
carrying out nonlinear backward propagation on the delta points obtained at the k-1 moment according to a state equation to obtain a delta point set of the state variables at the k moment:
the delta point set generated at the moment k is subjected to UT transformation, weighting and averaging to obtain the average value of the predicted value of the state variable at the moment k
Sum covariance
And carrying out nonlinear backward propagation on the delta point of the state variable at the moment k according to a measurement equation to obtain an output variable delta point set at the moment k:
UL(k)＝a4·(SOCk)4+a3·(SOCk)3+a2·(SOCk)2+a1·(SOCk)1+a0-R1·iL(k)-Up(k)
for the delta point set output at the moment k, the predicted value of the output variable at the moment k, namely the battery terminal voltage mean value and covariance can be obtained by weighting and solving the mean value and covariance through UT conversion:
(2) correction of SOC
Calculating a joint covariance matrix of the state variable and the output variable at the moment k:
at this time, the kalman filter gain: ke＝Pxy/Pyy
And (3) updating the state at the moment k:
and (3) updating the covariance of the state variable at the moment k:
preferably, the hardware platform of the battery detection system comprises a battery pack, a voltage and current detection module, an optical coupling isolation module, a central processing unit (TMS320F28335) and a display module.
Preferably, the central processing unit reads the voltage value of the battery terminal, reads the current of the charge-discharge loop and the temperature of the battery, and then sends the initial value and the data of the battery terminal voltage and the charge current obtained by detection to the unscented kalman filter algorithm for iteration and estimates the state of charge of the lithium battery in real time.
The invention has the beneficial effects that: according to the lithium battery SOC estimation based on the UKF algorithm, the battery system state space model is established according to the equivalent circuit model and is subjected to discretization processing, and the lithium battery SOC is accurately estimated based on the UKF algorithm, so that the problems of parameter change, inaccurate noise statistical characteristics and self-correction characteristics in the system state calculation process can be effectively solved, the linear error is reduced, and the SOC estimation precision is improved. The hardware platform for building the battery detection system realizes the processing of voltage and current signals by utilizing an A/D pin built in a Digital Signal Processor (DSP), has higher precision, simple external circuit and easy data processing, and realizes the correctness and adaptability of the UKF algorithm to the SOC of the lithium battery.
Drawings
FIG. 1 is a flow chart of steps of SOC estimation of a lithium battery of an electric vehicle based on UKF.
Fig. 2 is a circuit model based on Thevenin improved lithium battery.
FIG. 3 is a UKF-based SOC estimation flow chart of a lithium battery of an electric vehicle.
Fig. 4 is a general hardware platform structure diagram of the battery detection system.
Fig. 5 is a flowchart of the main program of the cpu.
Fig. 6 is a comparison of SOC values measured by the battery detection system hardware circuit.
Detailed Description
In order to make the technical means, the creation characteristics, the achievement purposes and the effects of the invention easy to understand, the invention is further described with the specific embodiments.
As shown in fig. 1 to 6, the UKF-based SOC estimation of the lithium battery of the electric vehicle includes the following steps:
(A) according to the dynamic characteristics of the lithium battery, establishing a Thevenin-based improved circuit model of the lithium battery;
the Thevenin-based improved circuit model comprises a KiBaM model and a Thevenin model of battery self-discharge effect, the KiBaM model estimates the capacity and the SOC of a battery, and the Thevenin model comprises the internal resistance R of the battery1Polarization resistance R2Polarization capacitance CpSaid polarization resistance R2And a polarization capacitor CpAfter being connected in parallel, the battery is connected with the internal resistance R of the battery1Are connected in series.
(B) Establishing a functional relation between parameters of the Thevenin-based improved circuit model and SOC (system on chip), and establishing a state space model of the battery system;
(C) carrying out discretization processing on the state space model of the battery system;
(D) establishing a lithium battery SOC estimation model based on a UKF algorithm to estimate the SOC of the lithium battery;
(E) and (3) building a hardware platform of the battery detection system, and verifying the applicability of the UKF algorithm.
In this embodiment, the battery system state space model is:
UL(t)＝f[SOC(t)]-R1·iL(t)-Up(t)
therein, SOC0For the last moment of the battery residual capacity, Q0Is the rated capacity of the battery, etaTIs a temperature correction coefficient, ηIIs a coulomb efficiency correction factor.
In this embodiment, the discretization state space model of the battery system is:
UL(k)＝f(SOCk)-R1·iL(k)-Up(k)
in the embodiment, the estimation of the lithium battery SOC by the lithium battery SOC estimation model based on the UKF algorithm comprises (1) prediction of SOC and (2) correction of SOC;
(1) prediction of SOC
Initializing SOC value and covariance at k-1 moment, and generating 2n +1 delta points near the state at the moment by UT conversion:
carrying out nonlinear backward propagation on the delta points obtained at the k-1 moment according to a state equation to obtain a delta point set of the state variables at the k moment:
the delta point set generated at the moment k is subjected to UT transformation, weighting and averaging to obtain the average value of the predicted value of the state variable at the moment k
Sum covariance
And carrying out nonlinear backward propagation on the delta point of the state variable at the moment k according to a measurement equation to obtain an output variable delta point set at the moment k:
UL(k)＝a4·(SOCk)4+a3·(SOCk)3+a2·(SOCk)2+a1·(SOCk)1+a0-R1·iL(k)-Up(k)
for the delta point set output at the moment k, the predicted value of the output variable at the moment k, namely the battery terminal voltage mean value and covariance can be obtained by weighting and solving the mean value and covariance through UT conversion:
(2) correction of SOC
Calculating a joint covariance matrix of the state variable and the output variable at the moment k:
at this time, the kalman filter gain: ke＝Pxy/Pyy
And (3) updating the state at the moment k:
and (3) updating the covariance of the state variable at the moment k:
in this embodiment, a hardware platform of the battery detection system includes a battery pack, a voltage and current detection module, an optical coupling isolation module, a central processing unit (TMS320F28335), and a display module.
In this embodiment, the central processing unit reads the voltage value of the battery terminal, reads the current of the charge-discharge loop, reads the temperature of the battery, and sends the initial value and the data of the battery terminal voltage and the charge current obtained by detection to the unscented kalman filter algorithm for iteration, thereby estimating the state of charge of the lithium battery in real time.
It will be appreciated by those skilled in the art that the invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The embodiments disclosed above are therefore to be considered in all respects as illustrative and not restrictive. All changes which come within the meaning and range of equivalency of the invention are to be embraced within their scope.
Claims (6)
1. UKF-based SOC estimation of electric automobile lithium batteries is characterized by comprising the following steps:
(A) according to the dynamic characteristics of the lithium battery, establishing a Thevenin-based improved circuit model of the lithium battery;
the improvements based on TheveninThe model circuit model comprises a KiBaM model and a Thevenin model of the battery self-discharge effect, the KiBaM model estimates the battery capacity and the battery SOC, and the Thevenin model comprises the battery internal resistance R1Polarization resistance R2Polarization capacitance CpSaid polarization resistance R2And a polarization capacitor CpAfter being connected in parallel, the battery is connected with the internal resistance R of the battery1Are connected in series.
(B) Establishing a functional relation between parameters of the Thevenin-based improved circuit model and SOC (system on chip), and establishing a state space model of the battery system;
(C) carrying out discretization processing on the state space model of the battery system;
(D) establishing a lithium battery SOC estimation model based on a UKF algorithm to estimate the SOC of the lithium battery;
(E) and (3) building a hardware platform of the battery detection system, and verifying the applicability of the UKF algorithm.
2. The UKF-based SOC estimation for lithium batteries of electric vehicles according to claim 1, wherein: the battery system state space model is as follows:
UL(t)＝f[SOC(t)]-R1·iL(t)-Up(t)
therein, SOC0For the last moment of the battery residual capacity, Q0Is the rated capacity of the battery, etaTIs a temperature correction coefficient, ηIIs a coulomb efficiency correction factor.
3. The UKF-based SOC estimation for lithium batteries of electric vehicles according to claim 2, wherein: the discretization state space model of the battery system comprises the following steps:
UL(k)＝f(SOCk)-R1·iL(k)-Up(k)。
4. the UKF-based SOC estimation for lithium batteries of electric vehicles according to claim 3, wherein: the lithium battery SOC estimation model based on the UKF algorithm estimates the SOC of the lithium battery, including (1) SOC prediction and (2) SOC correction;
(1) prediction of SOC
Initializing SOC value and covariance at k-1 moment, and generating 2n +1 delta points near the state at the moment by UT conversion:
carrying out nonlinear backward propagation on the delta points obtained at the k-1 moment according to a state equation to obtain a delta point set of the state variables at the k moment:
the delta point set generated at the moment k is subjected to UT transformation, weighting and averaging to obtain the average value of the predicted value of the state variable at the moment k
Sum covariance
And carrying out nonlinear backward propagation on the delta point of the state variable at the moment k according to a measurement equation to obtain an output variable delta point set at the moment k:
UL(k)＝a4·(SOCk)4+a3·(SOCk)3+a2·(SOCk)2+a1·(SOCk)1+a0-R1·iL(k)-Up(k)
for the delta point set output at the moment k, the predicted value of the output variable at the moment k, namely the battery terminal voltage mean value and covariance can be obtained by weighting and solving the mean value and covariance through UT conversion:
(2) correction of SOC
Calculating a joint covariance matrix of the state variable and the output variable at the moment k:
at this time, the kalman filter gain: ke＝Pxy/Pyy
And (3) updating the state at the moment k:
and (3) updating the covariance of the state variable at the moment k:
。
5. the UKF-based SOC estimation for lithium batteries of electric vehicles according to claim 1, wherein: the hardware platform of the battery detection system comprises a battery pack, a voltage and current detection module, an optical coupling isolation module, a central processing unit (TMS320F28335) and a display module.
6. The UKF-based SOC estimation for lithium batteries of electric vehicles according to claim 5, wherein: the central program of the central processing unit reads the voltage value of the battery terminal, reads the current of the charge-discharge loop, reads the temperature of the battery, and sends the initial value and the data of the battery terminal voltage and the charge current obtained by detection into an unscented Kalman filtering algorithm for iteration, thereby estimating the state of charge of the lithium battery in real time.
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| CN117110894A (en) * | 2023-09-06 | 2023-11-24 | 合肥工业大学 | SOC estimation method and system for power battery of electric automobile |
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Non-Patent Citations (1)
| Title |
|---|
| 喻业琴: "基于UKF的锂离子电池SOC估算方法", 中国优秀硕士学位论文全文数据库工程科技Ⅱ辑, 15 January 2015 (2015-01-15), pages 23 - 39 * |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117110894A (en) * | 2023-09-06 | 2023-11-24 | 合肥工业大学 | SOC estimation method and system for power battery of electric automobile |
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