CN113176505A - On-line estimation method and device for state of charge and state of health of vehicle-mounted power battery and storage medium - Google Patents
On-line estimation method and device for state of charge and state of health of vehicle-mounted power battery and storage medium Download PDFInfo
- Publication number
- CN113176505A CN113176505A CN202110484111.4A CN202110484111A CN113176505A CN 113176505 A CN113176505 A CN 113176505A CN 202110484111 A CN202110484111 A CN 202110484111A CN 113176505 A CN113176505 A CN 113176505A
- Authority
- CN
- China
- Prior art keywords
- state
- battery
- single battery
- est
- charge
- 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.)
- Granted
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
- 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]
- G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
-
- 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]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Secondary Cells (AREA)
Abstract
The invention discloses an online estimation method, an online estimation device and a storage medium for the state of charge and the state of health of a power battery, wherein the online estimation method comprises the following steps: 1. acquiring the current, voltage and temperature of a single battery in real time in the charging and discharging process of the power battery; 2. in the discharging process, identifying parameters of each single battery on line by a recursive least square method based on forgetting factors; 3. in the charging and discharging process, estimating the terminal voltage of each single battery by using a Thevenin equivalent circuit model; 4. estimating the SOC of each single battery by using a self-adaptive extended Kalman filtering algorithm; 5. in the charging process, calculating the capacity of each single battery; 6. and calculating the charge state, the capacity and the health state of the battery pack. 7. And meanwhile, the calculated capacity of each single battery is applied to the capacity parameter of each single battery in the Thevenin equivalent circuit model, so that the joint estimation of the battery charge state and the health state of online closed-loop feedback correction is realized. The method can be used for accurately evaluating the inconsistency state among the battery pack monomers and provides a basis for making a power battery balance control strategy.
Description
Technical Field
The invention relates to the technical field of power battery management systems, and is suitable for on-line state estimation of a vehicle-mounted power battery management system.
Background
The estimation accuracy for estimating the state of the power battery based on the equivalent circuit model depends greatly on the accuracy of the parameters of the equivalent circuit model. The 0-order equivalent circuit model ignores the polarization effect of the power battery, the estimation result cannot show the real state of the power battery, and the 2-order equivalent circuit model and the above equivalent circuit model can accurately reflect the real state of the power battery, but more parameters need to be identified, and the practical application is greatly limited by working conditions.
In the actual use process of the power battery, inconsistency such as monomer capacity, charge state, ohmic internal resistance and the like exists among the grouped monomer batteries, and if normalized battery parameters are adopted for state estimation, fixed estimation errors caused by the inconsistency exist.
Patent document CN111060834A discloses a method for estimating the state of health of a power battery, which is to perform loop iteration calculation on the internal resistance and SOC of the battery through a kalman filter algorithm, and finally evaluate the state of health of the battery through the increase of the internal resistance. The method identifies and applies the battery state parameters on line through the least square method off-line in the implementation process. The main problems of the method are as follows: 1. the method for evaluating the health state of the battery through the internal resistance of the battery has certain limitation, the internal resistance of the battery has strong correlation with factors such as temperature, SOC state, charge-discharge multiplying power and the like, and the influence of the correlation factors on the internal resistance of the battery is not mentioned in the text; 2. the battery state parameters change along with the aging of the battery, and the estimation error increases along with the aging by adopting offline parameter identification and online application.
Patent document 2CN106842060A discloses a dynamic parameter-based power battery SOC estimation method and system, in which a second-order RC equivalent circuit model is used to perform online parameter identification by using a recursive least square method with forgetting factors, and then a kalman filtering algorithm is used to perform online estimation on the battery SOC. Compared with a Thevenin equivalent circuit model, the method has the defects that in the actual implementation process, more parameters need to be identified, the parameter identification is complex in online realization, and the identification result is unstable, so that the Kalman filtering estimation result is diffused.
Patent document CN105301509B discloses a joint estimation method of the state of charge, the state of health, and the power state of a lithium ion battery, in which the SOH calculation method obtains the change of Δ SOC based on a Rint equivalent circuit model in combination with the least square method parameter identification result and the OCV-SOC relationship, and the method has the following limitations: 1. the least square method parameter identification adopted by the method can obtain a better identification effect only under a dynamic working condition, and the identified parameters OCV and R0 have a length-offset relationship, so that the accuracy of OCV estimation cannot be completely ensured, namely the stability and the accuracy of delta SOC calculation in the SOH calculation process cannot be ensured; 2. the method has the advantages that the calculation of the delta Ah is carried out while the parameters OCV and R0 are identified under the dynamic working condition, the current fluctuation is large under the dynamic working condition, and if the accurate delta Ah is to be obtained, the acquisition precision of the current sensor is high in requirement. In the process of calculating the SOC in the literature, off-line parameter identification is carried out on the basis of a second-order equivalent circuit model in combination with a genetic algorithm, and meanwhile, parameters of the second-order equivalent circuit model are adjusted in combination with a capacity calculation result and an internal resistance identification result in the SOH calculation process.
Patent document CN107576919A discloses a system and a method for estimating a state of charge of a power battery based on an ARMAX model, wherein the method is based on a Thevenin battery model after the ARMAX model is dispersed, parameter identification is performed on the Thevenin battery model by a least square method, an OCV-SOC relationship is established, and SOC is obtained by looking up a table through identification parameters OCV. The method adopts the least square method to identify the parameters, can obtain better effect under the condition of dynamic discharge working condition, but the SOC estimation precision is difficult to ensure in the process of obtaining the SOC through table look-up by identifying the parameter OCV in the charging process, and only reaches about 5 percent.
The invention content is as follows:
aiming at the defects in the prior art, the invention provides a power battery state of charge and state of health combined estimation method, a device and a storage medium, which realize an online feedback correction function and effectively improve the estimation accuracy and the estimation stability of the state of charge and the state of health of a power battery management system.
In order to achieve the purpose, the invention adopts the following technical scheme:
an online estimation method for the state of charge and the state of health of a power battery comprises the following general scheme: in the discharging process, the online identification of each single battery parameter in the power battery pack is realized based on a recursive least square method with forgetting factors and by combining the acquired current, the single voltage and the battery temperature; in the charging and discharging process, the terminal voltage CellU _ Est of each single battery is estimated on line in real time based on the Thevenin equivalent circuit model and the parameters of each single battery identified in the discharging process; correcting the charge state of each single battery by combining a self-adaptive extended Kalman filter algorithm according to the estimated deviation between the terminal voltage CellU _ Est of each single battery and the collected terminal voltage CellU _ Test of each single battery to obtain the charge state CellSOC _ Est of each single battery; in the charging process, the capacity CellCap _ Est of each single battery is calculated according to the charge state variation quantity delta CellSOC and the charge capacity variation quantity delta Ah of each single battery. And calculating the SOC of the battery pack, the pack capacity PackCap and the SOH of the battery pack according to the SOC of each single battery cell CellSOC _ Est calculated in the charging and discharging process and the capacity CellCap _ Est of each single battery cell calculated in the charging process. And meanwhile, the calculated capacity CellCap _ Est of each single battery is applied to the capacity parameter of each single battery in the Thevenin equivalent circuit model, so that the combined estimation of the battery charge state and the health state of online closed-loop feedback correction is realized.
The estimation result based on the single battery charge state and the single battery capacity can be used for accurately estimating the inconsistency state among the battery pack monomers, and provides a basis for formulating a power battery balance control strategy.
The invention specifically comprises the following steps:
step 1, collecting current, voltage and battery temperature data of each single battery in real time in the charging and discharging process of the power battery.
And 2, in the discharging process, carrying out online identification on parameters of each single battery of the Thevenin battery model on the acquired state data of each single battery through a recursive least square method with forgetting factors, and correcting average parameters of each single battery in an initial state according to identification results.
And 3, in the charging and discharging processes, estimating the terminal voltage CellU _ Est of each single battery in real time on line based on the Thevenin equivalent circuit model and the parameters of each single battery identified in the discharging process.
And 4, correcting the charge state of each single battery by combining a self-adaptive extended Kalman filter algorithm according to the estimated deviation between the terminal voltage CellU _ Est of each single battery and the collected terminal voltage CellU _ Test of each single battery to obtain the charge state CellSOC _ Est of each single battery.
Step 5, in the charging process, calculating the capacity CellCap _ Est of each single battery according to the variation quantity delta CellSOC of the charge state of each single battery and the variation quantity delta Ah of the charge capacity of each single battery;
step 6, applying the calculated capacity CellCap _ Est of each single battery to the corrected capacity parameter of each single battery of the Thevenin equivalent circuit model, and realizing the joint estimation of the battery state of charge and the health state of the online closed-loop feedback correction;
and 7, calculating the SOC of the battery pack, the PackCap of the battery pack and the SOH of the battery pack according to the calculated SOC of each single battery cell CellSOC _ Est and the calculated Capacity CellCap _ Est of each single battery cell as output results.
Before step 1, the battery model is offline calibrated, namely, the individual batteries are offline identified after being subjected to HPPC test, and average state parameters are offline calibrated to the individual batteries, so that initial state data of the parameters of the individual batteries of the Thevenin battery model are obtained.
Preferably, the offline parameter identification method includes, but is not limited to, a genetic algorithm, a least square method, and the like, and the offline parameter calibration includes battery parameters identified in different SOCs and different temperature states, wherein the OCV-SOC relationship passes offline testing and calibration, and the OCV-SOC relationship is considered to remain unchanged during the full life cycle of each unit battery.
Preferably, in the step 1, the current, the voltage of each monomer and the temperature of the power battery are acquired by sensors in the charging and discharging processes of the power battery, the acquisition precision of the current sensor is +/-1%, the accuracy of the voltage sensor is +/-5 mV, and the acquisition precision of the temperature sensor is 2 ℃.
Preferably, in the step 2, the parameters of each single battery of the Thevenin equivalent circuit model are identified online in the discharging process according to the collected battery state data and a recursive least square method with forgetting factors, the online identification results comprise online identification results under different SOC and different temperature states, and a battery parameter table is established and stored in a BMS (battery management system) storage register.
Preferably, when the Thevenin equivalent circuit model estimates the state of charge CellSOC _ Est and the terminal voltage CellU _ Est of each single battery in step 3, the estimation results in different temperature states are distinguished.
Preferably, in the charging process in step 5, the variation Δ CellSOC [ i ] of the state of charge of each battery cell and the variation Δ Ah [ i ] of the charging capacity of each battery cell in the fixed SOC interval are recorded, and the following formula is used: each monomer capacity calculated by CellCap _ Est [ i ] ═ Δ Ah [ i ]/Δcellsoc [ i ] includes the estimation results in different temperature states.
Preferably, the individual capacity results calculated in step 5 are subjected to an average filtering process, and when the deviation of the estimation results is within a certain range, it is determined that the individual capacity estimation results are valid.
Another objective of the present invention is to provide a combined estimation device for state of charge and state of health of a power battery, which is used to at least partially solve the technical problems mentioned in the background.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
a combined estimation device for the state of charge and the state of health of a power battery comprises a memory and a processor, wherein the memory stores instructions used for enabling the processor to execute the combined estimation method for the state of charge and the state of health of the power battery.
Compared with the prior art, the combined estimation device for the state of charge and the state of health of the power battery and the combined estimation method for the state of charge and the state of health of the power battery have the same advantages, and are not described again.
Accordingly, the embodiment of the present invention further provides a machine-readable storage medium, where instructions are stored on the machine-readable storage medium, and the instructions are used to enable a machine to execute the above power battery state of charge and state of health joint estimation method.
Additional features and advantages of embodiments of the invention will be set forth in the detailed description which follows.
The invention has the following advantages:
1. the Thevenin equivalent circuit model is adopted to identify the battery parameters in real time through a least square method with forgetting factors under the discharge working condition, and the equivalent circuit model can be ensured to have higher estimation precision under the condition of not increasing the parameter identification complexity.
2. According to the battery parameters identified under the dynamic working condition, the state of charge of each single battery is estimated through the self-adaptive extended Kalman filtering algorithm in the charging and discharging process, and the accuracy and the stability of the estimation result of the state of charge of each single battery can be ensured.
3. The current fluctuation is small in the charging process, and the stability of delta Ah [ i ] in the monomer capacity calculation formula can be determined by calculating the capacity of each monomer battery in the charging process, so that the estimation precision of the monomer capacity is improved.
4. The state of charge and the capacity of each single battery in the battery pack are estimated, and the state of charge, the capacity and the health state of the battery pack are calculated on the basis, so that fixed errors caused by inconsistency among the single batteries are avoided, and the estimation accuracy of the state of charge and the health state of the battery pack is improved.
5. And each single battery in the battery pack establishes a respective state parameter database, and the capacity calculation result of each single battery is fed back to each single battery parameter database in real time, so that the combined estimation of the battery charge state and the health state of online closed-loop feedback correction is realized, and the estimation precision is improved.
6. The estimation results of the charge state and the capacity of the single battery can be used for evaluating the inconsistency state among the single batteries in the battery pack, and provide a basis for formulating a power battery balance control strategy.
Drawings
FIG. 1 is a flow chart of a power battery state of charge and state of health online estimation method;
fig. 2 is a simulation result (V) of the cell terminal voltage error.
Fig. 3 is a simulation result (%) of the battery pack state of charge estimation error.
Fig. 4 is a simulation result of the battery state of health estimation error.
Detailed Description
The invention is further described below with reference to the accompanying drawings:
the method for estimating the state of charge and the state of health of the power battery on line adopts a Thevenin battery model, firstly, the battery model is calibrated off line, and the method comprises the steps of carrying out HPPC test on single batteries, then identifying off line and calibrating average state parameters to each single battery off line. And establishes a battery parameter table to be stored in the BMS storable register.
Referring to fig. 1, the process of online estimation is as follows:
step 1: and acquiring current, voltage and battery temperature data of each single battery in real time in the charging and discharging process of the power battery. The current, each monomer voltage and the battery temperature of the power battery are acquired by sensors in the charging and discharging processes, the acquisition precision of the current sensor is +/-1%, the acquisition precision of the voltage sensor is +/-5 mV, and the acquisition precision of the temperature sensor is 2 ℃.
Step 2: in the discharging process, the online identification of the parameters of each single battery of the Thevenin battery model is carried out on the collected state data of each single battery through a recursive least square method with forgetting factors, and the average parameters of each single battery in the initial state are corrected according to the identification result.
The equation expression of the Thevenin equivalent circuit model is as follows:
UL＝Uoc+I*R0+Up
wherein U isLTerminal voltage, U, of the batteryocFor the open circuit voltage of the battery, I for the current in the charging and discharging process, UpIs a polarization voltage, R0Is ohmic internal resistance, RrIs the internal resistance of polarization, CrIs a polarization capacitor;
the state equation after the Thevenin equivalent circuit model is converted is as follows:
UL(k)＝a1I(k)+α2I(k_1)+a2*UL(k_1)
wherein
Cr＝Ts/(a2-a1a3)
The above formula is converted to a standard least squares representation of the form:
wherein
Recursive least squares recursion formula with forgetting factor:
in the charging and discharging process, each parameter of the Thevenin battery model is identified on line by a recursive least square method with forgetting factors, and the initial value is set as follows:
θ0＝[a1(0)a2(0)a3(0)]＝[0.001 0.001 0.001]
P0＝106*eye(4)。
the following table shows an illustration of the parameter identification results of Thevenin battery models in the specific implementation:
| SOC(％) | OCV(V) | R0(Ω) | Tau(/) | Rp(Ω) |
| 0 | 3.408541342 | 0.000584937 | 0.729542992 | 0.000360195 |
| 5 | 3.460781011 | 0.001136949 | 2.681386363 | 0.000691817 |
| 10 | 3.503883984 | 0.000688343 | 4.331066657 | 0.000190854 |
| 20 | 3.579773712 | 0.000544251 | 6.677394242 | 0.000103549 |
| 30 | 3.625299698 | 0.000517473 | 8.390261248 | 0.000103577 |
| 40 | 3.657590785 | 0.000447692 | 5.53318486 | 6.98442E-05 |
| 50 | 3.710070256 | 0.000500139 | 4.756370271 | 6.2807E-05 |
| 60 | 3.818327771 | 0.000512593 | 7.805621025 | 0.000140059 |
| 70 | 3.932567823 | 0.000518879 | 5.79527721 | 0.000109553 |
| 80 | 4.055127015 | 0.000524781 | 6.368853687 | 0.000119429 |
| 90 | 4.175781618 | 0.000538516 | 5.174495362 | 9.24342E-05 |
| 95 | 4.235755153 | 0.000551151 | 5.055800929 | 9.1513E-05 |
| 100 | 4.316759677 | 0.000615927 | 6.917729582 | 0.000132148 |
。
and step 3: in the charging and discharging process, the terminal voltage CellU _ Est of each single battery is estimated on line in real time based on the Thevenin equivalent circuit model and the parameters of each single battery identified in the discharging process. The simulation result of the terminal voltage error of the single battery is shown in fig. 2.
And 4, step 4: and correcting the charge state of each single battery by combining a self-adaptive extended Kalman filter algorithm according to the estimated deviation of the terminal voltage CellU _ Est of each single battery and the collected deviation of the terminal voltage CellU _ Est of each single battery to obtain the charge state CellSOC _ Est of each single battery.
The adaptive extended Kalman filtering algorithm process:
the state equation is as follows: x is the number ofk＝f(xk-1，uk-1)+ωk-1
The observation equation: y isk＝h(xk，uk)+vk
Wherein x is an n-dimensional system state vector; u is an r-dimensional system input vector; y is an m-dimensional system output vector; omegak-1Is system white noise, has a mean of zero and a covariance of Qk，vkFor white noise measurement, the mean is zero and the covariance is Rk；
State estimation time update
error covariance time update
P(k|k_1)＝A(k_1)P(k_1)A(k_1)T+Q
P (k | k _1) is
Corresponding error covariance, P (k _1) is
A corresponding error covariance, representing uncertainty of the state estimate;
kalman gain update
K(k)＝P(k|k_1)C(k)T[C(k)P(k|k_1)C(k)T+R]-1
K (k) is the Kalman gain at time k, representing the actual observed value ykThe specific gravity occupied when correcting the predicted value;
state estimation measurement update
Error covariance measurement update
P(k)＝[I-K(k)C(k)]P(k|k_1)
Wherein I is an identity matrix: .
And 5: in the charging process, the capacity CellCap _ Est of each single battery is calculated according to the state of charge variation delta CellSOC and the charging capacity variation delta Ah of each single battery in a fixed interval according to the following formula:
CellCap_Est[i]＝ΔAh[i]/ΔCellSOC[i]。
step 6: and applying the calculated capacity CellCap _ Est of each single battery to the correction of the capacity parameter of each single battery of the Thevenin equivalent circuit model, and realizing the joint estimation of the battery charge state and the health state of online closed-loop feedback correction.
And 7: calculating the SOC of the battery pack, the PackCap of the battery pack and the SOH of the battery pack according to the calculated states of charge (CellSOC _ Est) of the single batteries and the calculated capacities (CellCap _ Est) of the single batteries and the following formulas:
PackCap＝min{CellSOC_Est[i]*CellCap_Est[i]}+min{(1-CellSOC_Est[i])*CellCap_Est[i]}；
SOC＝min{(1-CellSOC_Est[i])*CellCap_Est[i]}/PackCap；
SOH＝PackCap/RatedCap；
wherein: CellSOC _ Est [ i ] is the state of charge of each single battery, CellCap _ Est [ i ] is the capacity of each single battery, PackCap is the capacity of the battery pack, SOC is the state of charge of the battery pack, SOH is the state of health of the battery pack, and RatedCap is the rated capacity of the battery pack.
And simultaneously converting the SOH in the current state into the SOH in the normal temperature state according to the calculation result of the battery temperature in the current state and the SOH according to the following formula:
SOH_norT＝k*SOH；
wherein k is a conversion coefficient and is calculated according to the following formula:
k＝a*DeltT+b；
DeltT is the temperature difference between the current temperature and the normal temperature, a and b are temperature difference fitting coefficients, and k, a and b are calibrated by off-line data fitting.
According to the scheme of the invention, the charge state and the health state of the battery pack are estimated on line in the discharging process of the power battery, and the estimation errors are kept within +/-2%. The simulation result of the state of charge estimation error of the battery pack is shown in fig. 3, and the simulation result of the state of health estimation error of the battery pack is shown in fig. 4.
Accordingly, the embodiment of the present invention further provides a machine-readable storage medium, where the machine-readable storage medium has instructions stored thereon, and the instructions are used to enable a machine to execute the above power battery state of charge and state of health joint estimation method. The machine-readable storage medium may be, for example, a usb disk, a removable hard disk, a Read Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and various media capable of storing program codes.
Further, an embodiment of the present invention further provides a power battery state of charge and state of health joint estimation apparatus, where the apparatus may include a memory and a processor, and the memory may store instructions, where the instructions enable the processor to execute the power battery state of charge and state of health joint estimation method according to any of the embodiments of the present invention.
The processor may be a Central Processing Unit (CPU), but may also be other general purpose processors, digital signal processors (dsps), application specific integrated circuits, off-the-shelf programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and the like.
The memory can be used for storing the computer program instructions, and the processor realizes various functions of the combined estimation device of the state of charge and the state of health of the power battery by operating or executing the computer program instructions stored in the memory and calling the data stored in the memory. The memory may include high speed random access memory and may also include non-volatile memory such as a hard disk, a memory, a plug-in hard disk, a Smart Memory Card (SMC), a Secure Digital (SD) card, a flash memory card, at least one magnetic disk storage device, a flash memory device, or other volatile solid state storage device.
Although the embodiments of the present invention have been described in detail with reference to the accompanying drawings, the embodiments of the present invention are not limited to the details of the above embodiments, and various simple modifications can be made to the technical solutions of the embodiments of the present invention within the technical idea of the embodiments of the present invention, and the simple modifications all belong to the protection scope of the embodiments of the present invention.
Claims (12)
1. A vehicle-mounted power battery state of charge and state of health online estimation method is characterized in that: the method comprises the following steps:
(1) collecting current, voltage and battery temperature data of each single battery in real time in the charging and discharging process of the power battery;
(2) in the discharging process, carrying out online identification on parameters of each single battery of the Thevenin battery model on the acquired state data of each single battery through a recursive least square method with forgetting factors, and correcting average parameters of each single battery in an initial state according to identification results;
(3) in the charging and discharging process, the terminal voltage CellU _ Est of each single battery is estimated on line in real time based on the Thevenin equivalent circuit model and the parameters of each single battery identified in the discharging process;
(4) correcting the charge state of each single battery by combining a self-adaptive extended Kalman filter algorithm according to the estimated deviation between the terminal voltage CellU _ Est of each single battery and the collected terminal voltage CellU _ Test of each single battery to obtain the charge state CellSOC _ Est of each single battery;
(5) in the charging process, calculating the capacity CellCap _ Est of each single battery according to the variation delta CellSOC of the state of charge of each single battery and the variation delta Ah of the charging capacity of each single battery;
(6) the calculated capacity CellCap _ Est of each single battery is applied to the correction of the capacity parameter of each single battery of the Thevenin equivalent circuit model, and the joint estimation of the battery charge state and the health state of online closed-loop feedback correction is realized;
(7) and calculating the SOC of the battery pack, the PackCap of the battery pack and the SOH of the battery pack according to the calculated SOC of each single battery cell CellSOC _ Est and the calculated capacity CellCap _ Est of each single battery cell as output results.
2. The method of claim 1, wherein: the Thevenin equivalent circuit model initial state parameters are obtained by performing HPPC test on the single batteries and then performing off-line identification, and the average state parameters are calibrated to each single battery in an off-line manner; the off-line parameter identification method comprises but is not limited to a genetic algorithm, a least square method and the like, off-line parameter calibration comprises battery parameters identified under different SOC and different temperature states, and a battery parameter table is established and stored in a BMS (battery management system) storage register; the OCV-SOC relationship is tested and calibrated in an off-line mode, and the OCV-SOC relationship is considered to be kept unchanged in the whole life cycle process of each single battery.
3. The method according to claim 1 or 2, characterized in that: the equation expression of the Thevenin equivalent circuit model is as follows:
UL＝Uoc+I*R0+Up
wherein U isLTerminal voltage, U, of the batteryocFor the open circuit voltage of the battery, I for the current in the charging and discharging process, UpIs a polarization voltage, R0Is ohmic internal resistance, RrIs the internal resistance of polarization, CrIs a polarization capacitor;
the state equation after the Thevenin equivalent circuit model is converted is as follows:
UL(k)＝a1I(k)+a2I(k_1)+a2*UL(k_1)
wherein R is0＝a1、
Cr＝Ts/(a2-a1a3)
The above formula is converted to a standard least squares representation of the form:
wherein
Recursive least squares recursion formula with forgetting factor:
4. the method according to claim 1 or 2, characterized in that: in the discharging process, each parameter of the Thevenin battery model is identified on line by a recursive least square method with forgetting factors, and initial values are set as follows:
θ0＝[a1(0)a2(0)a3(0)]＝[0.001 0.001 0.001]
P0＝106*I(4)
where I is the identity matrix.
5. The method according to claim 1 or 2, characterized in that: calculating the SOC, the PackCap and the SOH of the battery pack by the following formulas:
PackCap＝min{CellSOC_Est[i]*CellCap_Est[i]}+min{(1-CellSOC_Est[i])*CellCap_Est[i]}；
SOC＝min{(1-CellSOC_Est[i])*CellCap_Est[i]}/PackCap；
SOH＝PackCap/RatedCap；
wherein: CellSOC _ Est [ i ] is the state of charge of each single battery, CellCap _ Est [ i ] is the capacity of each single battery, PackCap is the capacity of the battery pack, SOC is the state of charge of the battery pack, SOH is the state of health of the battery pack, and RatedCap is the rated capacity of the battery pack.
6. The method according to claim 1 or 2, characterized in that: the adaptive extended Kalman filtering algorithm process:
the state equation is as follows: x is the number ofk＝f(xk-1，uk-1)+ωk-1
The observation equation: y isk＝h(xk，uk)+vk
Wherein x is an n-dimensional system state vector; u is an r-dimensional system input vector; y is an m-dimensional system output vector; omegak-1Is system white noise, has a mean of zero and a covariance of Qk，vkFor white noise measurement, the mean is zero and the covariance is Rk；
State estimation time update
error covariance time update
P(k|k_1)＝A(k_1)P(k_1)A(k_1)T+Q
P (k | k-1) is
Corresponding error covariance, P (k _1) is
A corresponding error covariance, representing uncertainty of the state estimate;
kalman gain update
K(k)＝P(k|k_1)C(k)T[C(k)P(k|k_1)C(k)T+R]-1
K (k) is the Kalman gain at time k, representing the actual observed value ykThe specific gravity occupied when correcting the predicted value;
state estimation measurement update
Error covariance measurement update
P(k)＝[I-K(k)C(k)]P(k|k_1)
Where I is the identity matrix.
7. The method according to claim 1 or 2, characterized in that: the current, each monomer voltage and the battery temperature of the power battery are acquired by sensors in the charging and discharging processes, the acquisition precision of the current sensor is +/-1%, the acquisition precision of the voltage sensor is +/-5 mV, and the acquisition precision of the temperature sensor is 2 ℃.
8. The method according to claim 1 or 2, characterized in that: and (3) when the Thevenin equivalent circuit model estimates the state of charge (CellSOC _ Est) of each single battery and the terminal voltage (CellU _ Est) of each single battery in the step (4), distinguishing estimation results in different temperature states.
9. The method according to claim 1 or 2, characterized in that: in the step (5), the charging capacity delta Ah [ i ] of a fixed delta CellSOC [ i ] interval is recorded in the charging process, and the charging capacity delta Ah [ i ] is calculated according to the formula: and the cell capacity calculated by CellCap _ Est [ i ] ═ Δ Ah [ i ]/Δ CellSOC [ i ], and the estimation results under different temperature states are distinguished.
10. The method according to claim 1 or 2, characterized in that: and (5) carrying out average filtering processing on the monomer capacity results calculated in the step (5), and judging that the monomer capacity estimation results are valid when the deviation of the estimation results is within a certain range.
11. An on-board power battery state of charge and state of health online estimation device, characterized in that the device comprises a memory and a processor, wherein the memory stores instructions for enabling the processor to execute the on-board power battery state of charge and state of health online estimation method according to any one of claims 1 to 10.
12. A machine-readable storage medium having stored thereon instructions for enabling a machine to execute the on-board power battery state of charge and state of health online estimation method according to any one of claims 1 to 10.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110484111.4A CN113176505B (en) | 2021-04-30 | 2021-04-30 | On-line estimation method and device for state of charge and state of health of vehicle-mounted power battery and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110484111.4A CN113176505B (en) | 2021-04-30 | 2021-04-30 | On-line estimation method and device for state of charge and state of health of vehicle-mounted power battery and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113176505A true CN113176505A (en) | 2021-07-27 |
| CN113176505B CN113176505B (en) | 2022-10-04 |
Family
ID=76925831
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110484111.4A Active CN113176505B (en) | 2021-04-30 | 2021-04-30 | On-line estimation method and device for state of charge and state of health of vehicle-mounted power battery and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113176505B (en) |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113866655A (en) * | 2021-09-27 | 2021-12-31 | 北京理工新源信息科技有限公司 | Power battery capacity assessment method fusing vehicle networking operation data and test data |
| CN113884915A (en) * | 2021-11-11 | 2022-01-04 | 山东省科学院自动化研究所 | Method and system for predicting state of charge and state of health of lithium ion battery |
| CN114035049A (en) * | 2021-11-08 | 2022-02-11 | 东软睿驰汽车技术（沈阳）有限公司 | SOH precision calculation method and device and electronic equipment |
| CN114200313A (en) * | 2021-11-29 | 2022-03-18 | 重庆长安汽车股份有限公司 | Lead-acid storage battery health analysis method and system and storage medium |
| CN114740389A (en) * | 2022-05-11 | 2022-07-12 | 上海采日能源科技有限公司 | Battery health assessment method and device, electronic equipment and readable storage medium |
| WO2023039826A1 (en) * | 2021-09-17 | 2023-03-23 | 华为数字能源技术有限公司 | Control method, control apparatus and electronic device |
| CN116413613A (en) * | 2021-12-30 | 2023-07-11 | 比亚迪股份有限公司 | SOC estimation method, system, vehicle and medium of power battery |
| CN116609686A (en) * | 2023-04-18 | 2023-08-18 | 江苏果下科技有限公司 | Battery cell consistency assessment method based on cloud platform big data |
| CN116840699A (en) * | 2023-08-30 | 2023-10-03 | 上海泰矽微电子有限公司 | Battery health state estimation method and device, electronic equipment and medium |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102445663A (en) * | 2011-09-28 | 2012-05-09 | 哈尔滨工业大学 | Method for estimating battery health of electric automobile |
| CN103020445A (en) * | 2012-12-10 | 2013-04-03 | 西南交通大学 | SOC (State of Charge) and SOH (State of Health) prediction method of electric vehicle-mounted lithium iron phosphate battery |
| CN104698382A (en) * | 2013-12-04 | 2015-06-10 | 东莞钜威新能源有限公司 | Method for predicting the SOC and SOH of battery pack |
| US20180183245A1 (en) * | 2015-01-28 | 2018-06-28 | Hangzhou Gold Electronic Equipment Inc., Ltd. | Improved maintenance method of power battery pack |
| US20190170826A1 (en) * | 2017-12-06 | 2019-06-06 | Cadex Electronics Inc. | Battery state-of-health determination upon charging |
| CN109870651A (en) * | 2019-01-22 | 2019-06-11 | 重庆邮电大学 | A kind of electric automobile power battery system SOC and SOH joint estimation on line method |
| CN110261779A (en) * | 2019-06-25 | 2019-09-20 | 西安石油大学 | A kind of ternary lithium battery charge state cooperates with estimation method with health status online |
| CN110261778A (en) * | 2019-05-27 | 2019-09-20 | 南京理工自动化研究院有限公司 | A kind of lithium ion battery SOC estimation algorithm |
| CN111239611A (en) * | 2019-10-21 | 2020-06-05 | 浙江零跑科技有限公司 | Calculation method for calibrating PACKSOC based on single battery capacity |
| CN111999659A (en) * | 2020-09-30 | 2020-11-27 | 重庆长安新能源汽车科技有限公司 | Characteristic value method-based SOH estimation method for lithium iron phosphate battery and storage medium |
| CN212514930U (en) * | 2020-06-29 | 2021-02-09 | 武汉新能源研究院有限公司 | Electric automobile power battery package on-line measuring equipment |
| CN112433154A (en) * | 2019-08-25 | 2021-03-02 | 南京理工大学 | Lithium ion battery SOC estimation algorithm based on FFRLS and EKF |
| CN112684348A (en) * | 2021-01-21 | 2021-04-20 | 山东大学 | SOC prediction method and system based on strong tracking algorithm and adaptive Kalman filtering |
-
2021
- 2021-04-30 CN CN202110484111.4A patent/CN113176505B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102445663A (en) * | 2011-09-28 | 2012-05-09 | 哈尔滨工业大学 | Method for estimating battery health of electric automobile |
| CN103020445A (en) * | 2012-12-10 | 2013-04-03 | 西南交通大学 | SOC (State of Charge) and SOH (State of Health) prediction method of electric vehicle-mounted lithium iron phosphate battery |
| CN104698382A (en) * | 2013-12-04 | 2015-06-10 | 东莞钜威新能源有限公司 | Method for predicting the SOC and SOH of battery pack |
| US20180183245A1 (en) * | 2015-01-28 | 2018-06-28 | Hangzhou Gold Electronic Equipment Inc., Ltd. | Improved maintenance method of power battery pack |
| US20190170826A1 (en) * | 2017-12-06 | 2019-06-06 | Cadex Electronics Inc. | Battery state-of-health determination upon charging |
| CN109870651A (en) * | 2019-01-22 | 2019-06-11 | 重庆邮电大学 | A kind of electric automobile power battery system SOC and SOH joint estimation on line method |
| CN110261778A (en) * | 2019-05-27 | 2019-09-20 | 南京理工自动化研究院有限公司 | A kind of lithium ion battery SOC estimation algorithm |
| CN110261779A (en) * | 2019-06-25 | 2019-09-20 | 西安石油大学 | A kind of ternary lithium battery charge state cooperates with estimation method with health status online |
| CN112433154A (en) * | 2019-08-25 | 2021-03-02 | 南京理工大学 | Lithium ion battery SOC estimation algorithm based on FFRLS and EKF |
| CN111239611A (en) * | 2019-10-21 | 2020-06-05 | 浙江零跑科技有限公司 | Calculation method for calibrating PACKSOC based on single battery capacity |
| CN212514930U (en) * | 2020-06-29 | 2021-02-09 | 武汉新能源研究院有限公司 | Electric automobile power battery package on-line measuring equipment |
| CN111999659A (en) * | 2020-09-30 | 2020-11-27 | 重庆长安新能源汽车科技有限公司 | Characteristic value method-based SOH estimation method for lithium iron phosphate battery and storage medium |
| CN112684348A (en) * | 2021-01-21 | 2021-04-20 | 山东大学 | SOC prediction method and system based on strong tracking algorithm and adaptive Kalman filtering |
Non-Patent Citations (5)
| Title |
|---|
| ASLESH KUMAR AVADHANULA 等: "Comparative Study of Mathematical Models and Data Driven Models for Battery Performance Parameter Estimation", 《2020 THIRD INTERNATIONAL CONFERENCE ON ADVANCES IN ELECTRONICS, COMPUTERS AND COMMUNICATIONS》 * |
| 张绍虹 等: "磷酸铁锂蓄电池IC曲线在健康状态估计算法中的应用", 《2020中国汽车工程学会年会论文集（2）》 * |
| 李嘉波 等: "基于自适应扩展卡尔曼滤波的锂离子电池荷电状态估计", 《储能科学与技术》 * |
| 董喜乐: "锂离子电池模型参数和荷电状态联合在线估计方法研究", 《中国优秀博硕士学位论文全文数据库(硕士)工程科技Ⅱ辑》 * |
| 谢俊淋 等: "纯电动汽车动力锂电池SOC估计策略综述", 《汽车文摘》 * |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023039826A1 (en) * | 2021-09-17 | 2023-03-23 | 华为数字能源技术有限公司 | Control method, control apparatus and electronic device |
| CN113866655A (en) * | 2021-09-27 | 2021-12-31 | 北京理工新源信息科技有限公司 | Power battery capacity assessment method fusing vehicle networking operation data and test data |
| CN114035049A (en) * | 2021-11-08 | 2022-02-11 | 东软睿驰汽车技术（沈阳）有限公司 | SOH precision calculation method and device and electronic equipment |
| CN113884915A (en) * | 2021-11-11 | 2022-01-04 | 山东省科学院自动化研究所 | Method and system for predicting state of charge and state of health of lithium ion battery |
| CN114200313A (en) * | 2021-11-29 | 2022-03-18 | 重庆长安汽车股份有限公司 | Lead-acid storage battery health analysis method and system and storage medium |
| CN114200313B (en) * | 2021-11-29 | 2024-03-08 | 重庆长安汽车股份有限公司 | Lead-acid storage battery health analysis method, system and storage medium |
| CN116413613A (en) * | 2021-12-30 | 2023-07-11 | 比亚迪股份有限公司 | SOC estimation method, system, vehicle and medium of power battery |
| CN114740389A (en) * | 2022-05-11 | 2022-07-12 | 上海采日能源科技有限公司 | Battery health assessment method and device, electronic equipment and readable storage medium |
| CN116609686A (en) * | 2023-04-18 | 2023-08-18 | 江苏果下科技有限公司 | Battery cell consistency assessment method based on cloud platform big data |
| CN116609686B (en) * | 2023-04-18 | 2024-01-05 | 江苏果下科技有限公司 | Battery cell consistency assessment method based on cloud platform big data |
| CN116840699A (en) * | 2023-08-30 | 2023-10-03 | 上海泰矽微电子有限公司 | Battery health state estimation method and device, electronic equipment and medium |
| CN116840699B (en) * | 2023-08-30 | 2023-11-17 | 上海泰矽微电子有限公司 | Battery health state estimation method and device, electronic equipment and medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113176505B (en) | 2022-10-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113176505B (en) | On-line estimation method and device for state of charge and state of health of vehicle-mounted power battery and storage medium | |
| CN106054085B (en) | A method of based on temperature for estimating battery SOC | |
| CN111060820B (en) | Lithium battery SOC and SOP estimation method based on second-order RC model | |
| CN113156321B (en) | Estimation method of lithium ion battery state of charge (SOC) | |
| CN111679199B (en) | Lithium ion battery SOC estimation method and device | |
| CN109581225A (en) | The energy state evaluation method and battery management system of battery on-line parameter identification | |
| CN110333450B (en) | Battery open-circuit voltage estimation method and system | |
| CN110348062B (en) | Construction method of equivalent circuit model of lithium ion battery | |
| CN112858916B (en) | Battery pack state of charge estimation method based on model and data driving fusion | |
| CN111707953A (en) | Lithium battery SOC online estimation method based on backward smoothing filtering framework | |
| CN115128481B (en) | Battery state estimation method and system based on neural network and impedance identification correction | |
| CN111142025A (en) | Battery SOC estimation method and device, storage medium and electric vehicle | |
| CN115113070A (en) | Estimation method of battery SOC and related device | |
| CN113447821B (en) | Method for evaluating state of charge of battery | |
| CN106896325B (en) | Battery parameter online identification method and system | |
| CN114740385A (en) | Self-adaptive lithium ion battery state of charge estimation method | |
| CN116930794A (en) | Battery capacity updating method and device, electronic equipment and storage medium | |
| CN111044924B (en) | Method and system for determining residual capacity of all-condition battery | |
| CN110726937B (en) | Method for determining a state noise covariance matrix and corresponding device | |
| CN110412472B (en) | Battery state of charge estimation method based on normal gamma filtering | |
| CN113125965B (en) | Method, device and equipment for detecting lithium separation of battery and storage medium | |
| Chen et al. | Estimation of state of charge for lithium-ion battery considering effect of aging and temperature | |
| CN118671624A (en) | SOC error calibration method and system for lithium iron phosphate energy storage battery | |
| CN112946480A (en) | Lithium battery circuit model simplification method for improving SOC estimation real-time performance | |
| CN117607693A (en) | Power battery pack SOC online estimation method considering inconsistency |
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 | ||
| CP01 | Change in the name or title of a patent holder | ||
| CP01 | Change in the name or title of a patent holder |
Address after: 401133 room 208, 2 house, 39 Yonghe Road, Yu Zui Town, Jiangbei District, Chongqing Patentee after: Deep Blue Automotive Technology Co.,Ltd. Address before: 401133 room 208, 2 house, 39 Yonghe Road, Yu Zui Town, Jiangbei District, Chongqing Patentee before: CHONGQING CHANGAN NEW ENERGY AUTOMOBILE TECHNOLOGY Co.,Ltd. |