CN114839550B - UKF-AUKF-based lithium battery SOC joint estimation method - Google Patents
UKF-AUKF-based lithium battery SOC joint estimation method Download PDFInfo
- Publication number
- CN114839550B CN114839550B CN202210395241.5A CN202210395241A CN114839550B CN 114839550 B CN114839550 B CN 114839550B CN 202210395241 A CN202210395241 A CN 202210395241A CN 114839550 B CN114839550 B CN 114839550B
- Authority
- CN
- China
- Prior art keywords
- soc
- lithium battery
- battery
- model
- estimation
- 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
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 title claims abstract description 74
- 229910052744 lithium Inorganic materials 0.000 title claims abstract description 74
- 238000000034 method Methods 0.000 title claims description 29
- 238000001914 filtration Methods 0.000 claims abstract description 15
- 238000002474 experimental method Methods 0.000 claims description 17
- 238000007599 discharging Methods 0.000 claims description 6
- 230000010287 polarization Effects 0.000 claims description 6
- 238000005070 sampling Methods 0.000 claims description 6
- 239000003990 capacitor Substances 0.000 claims description 3
- 125000004122 cyclic group Chemical group 0.000 claims description 3
- 238000005259 measurement Methods 0.000 claims description 3
- 230000009466 transformation Effects 0.000 claims description 3
- 230000003044 adaptive effect Effects 0.000 abstract description 2
- 230000001351 cycling effect Effects 0.000 description 3
- 238000011160 research Methods 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
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]
- G01R31/385—Arrangements for measuring battery or accumulator variables
- G01R31/387—Determining ampere-hour charge capacity or 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]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Tests Of Electric Status Of Batteries (AREA)
Abstract
The invention discloses an accurate estimation of a lithium battery state of charge (SOC) which is a guarantee of safe running of an electric automobile. In order to reduce errors caused by the fact that a battery model does not conform to actual battery parameter time-varying characteristics under actual complex working conditions, an unscented Kalman filtering algorithm (UKF) is adopted to conduct online parameter identification on a second-order equivalent model of the lithium battery, then an adaptive unscented Kalman filtering Algorithm (AUKF) is combined to estimate the SOC of the lithium battery, time-varying parameters are fed back to the model of SOC estimation, SOC estimation accuracy and adaptability to various working conditions are improved, comparison analysis is conducted on the UDS working conditions through the UKF-AUKF and an online double-extended Kalman filtering algorithm (DEKF), and experimental results prove accuracy and robustness of the UKF-AUKF.
Description
Technical Field
The invention relates to the field of lithium battery state of charge estimation, in particular to a method for estimating SOC based on UKF online parameter identification and AUKF.
Background
In order to meet the time requirement of sustainable development, new energy electric vehicles are rapidly developed. Lithium batteries are the core of the operation of electric vehicles, and therefore estimation of the state of lithium batteries is becoming important. The method comprises the steps of carrying out a great deal of research on lithium battery SOC estimation at home and abroad, wherein most of the research is based on a model at present, firstly, an electrochemical model is used for estimating the battery SOC by analyzing and calculating the chemical reaction of the lithium battery, and the method needs a certain related chemical theory and is relatively complex in calculation; secondly, a black box model is established through a large amount of data driving, such as a neural network and the like, but the method requires a large amount of data training models, and has poor effect under complex working conditions and environments; and thirdly, the equivalent circuit model is used for estimating the SOC by combining a circuit model formed by resistance and capacitance with various filtering algorithms, the calculated amount of the method is small, the accuracy is high, and the estimation accuracy under various complex working conditions can be ensured, so that the method is widely applied.
The model accuracy directly influences the estimation accuracy of the SOC, so that Cao et al propose the offline parameter identification based on the RLS, and the identification accuracy of the model is ensured; on-line parameter identification is carried out on the basis of the RLS of the constraint condition, the UKF is used for estimating the battery SOC, so that the model precision is further improved, but the RLS algorithm has fewer data points and relatively low precision; parameter identification is carried out by adopting a recursive least square method with forgetting factors, the battery SOC is estimated by using an EKF, historical data is abandoned by setting the forgetting factors, and the problem of data redundancy is effectively solved, but the forgetting factors of the algorithm are fixed and possibly suitable for the current working condition, but errors can be increased sharply for other working conditions, and the method has no universality for different working conditions; the double-extended Kalman filtering algorithm performs parameter and SOC joint estimation, realizes on-line parameter identification, and greatly improves the adaptability of the model to different working conditions, but the linearization process in the EKF causes continuous existence of errors due to omitting part of higher-order terms, and the noise influence of the actual working conditions cannot be processed.
Disclosure of Invention
Aiming at the problem that the influence of the lithium battery parameter time-varying characteristic and noise on the SOC estimation is great under the actual working condition. According to the invention, the parameter identification is carried out on the equivalent circuit model by adopting the UKF algorithm under the macroscopic time scale, and the SOC of the lithium battery is estimated by combining the UKF algorithm under the microscopic time scale, so that the problem of larger error caused by the fixation of the traditional offline parameter identification model is solved, the problem of low algorithm precision caused by the EKF omitting the higher-order item is solved, the environmental and working condition noise is further filtered, and the robustness and the accuracy of the algorithm are improved.
The technical scheme adopted by the invention is as follows:
Step 1: the experimental platform is built by adopting a battery pack formed by connecting ten 18650 ternary lithium batteries in parallel, the experimental platform is shown in fig. 1, a temperature control box is used for setting the working temperature of the lithium batteries, the constant temperature is set at 25 ℃, and a PC end sends instructions to a programmable electronic load and a programmable DC power supply through a serial port to perform charge and discharge experiments on the batteries. Meanwhile, the data of the lithium battery is collected by a data collection card and is transmitted to the PC end in real time.
Step 2: the lithium battery model is the basis of SOC estimation, and the calculated amount and the precision are weighed, and a second-order equivalent circuit model is selected. And establishing a lithium battery loop equation, and then establishing a state space equation of online parameters of the lithium battery according to Laplace transformation.
Step 3: the parameter identification of the model is mainly completed through two experiments, namely, the relation between U oc and SOC is determined through a constant current discharge experiment, and R 0、R1、C1、R2、C2 is identified on line through a UDDS experiment. And determining the functional relation between each parameter of the equivalent circuit and the SOC.
Step 4: and under the macroscopic time scale, carrying out lithium battery parameter identification by using an unscented Kalman filtering algorithm.
Step 5: and when the parameters of the lithium battery model are updated, switching to a microscopic time scale, and estimating the SOC of the lithium battery by using a self-adaptive unscented Kalman filtering algorithm.
Step 6: and verifying the joint estimation accuracy under the pulse discharge working condition and the UDDS working condition.
Further, the step 2 includes:
Step 2.1: establishing a second-order equivalent circuit model of the lithium battery, and listing a loop equation and an observation equation according to the second-order equivalent circuit model:
U0＝Uoc(SOC)-R0I-U1-U2 (2)
Wherein: c 1、C2 is polarization capacitor, U oc is open circuit voltage, U 0 is terminal voltage, T s is sampling time, Q n is battery capacity, R 1、R2 is polarization resistance, and R 0 is ohmic internal resistance of the battery.
Step 2.2: the differential discretization of formulas (1) and (2) is obtained:
step 2.3: for a nonlinear system:
Wherein, is the system noise value and is the measurement noise value.
Further, the step 3 includes:
Step 3.1: discharging the fully charged lithium battery with 30A constant pulse current for 6 minutes, namely 0.1 SOC value, standing the battery for four hours after discharging, recording the open-circuit voltage of the battery, and repeating the operation for 10 times;
Step 3.2: according to the data points of the relation between 10 groups of U oc and the SOC, which are measured through experiments, the SOC is used as a variable, and eight-order fitting is carried out on the data points of U oc and the SOC through a formula (5), so that a function of U oc and the SOC is obtained:
Uocv(SOC)＝p1SOC8+p2SOC7+p3SOC6+p4SOC5+p5SOC4+p6SOC3+p7SOC2+p8SOC+p9
(5)
step 3.3: and the actual working condition is identified by adopting the urban circulation working condition in the United states as an on-line parameter. And under the condition that the battery works, the battery SOC value is reduced by 5% after one-time working condition is satisfied, and 20 times of circulation are performed until SOC=0.
Step 3.4: the incubator is set to 25 ℃, the battery pack is excited by adopting UDDS working condition current, and the measured voltage of the battery pack under the working condition is acquired by the data acquisition card, so that the current and the voltage under the actual working condition are obtained.
Further, the step 4 includes:
Step 4.1: first, according to the state space equation of the formula (3), the state space equation with the parameter variable and the state variable as independent variables is expressed:
In the above formula, θ= [ R 0 R1 C1 R2 C2]T, macroscopic scale L=60deg.S, microscopic scale sequence L epsilon (1-L), w k,lvk,l is process noise and observation noise of the system, and ρ k is model parameter process noise.
Step 4.2: initializing parameter variables and parameter variable covariance, and determining parameters alpha=0.01, k i =0, beta=2 and M=5 of the UKF algorithm.
Step 4.3: calculating sampling points at the moment k:
step 4.4: calculating weights:
Step 4.5: parameter predictor and system variance predictor P xx:
Step 4.6: the prediction parameters are updated, the observed values and the observed variance predicted values P yy are updated, and the parameter variable covariance and the unscented kalman gain k are simultaneously calculated.
Step 4.7: updating the observed value of the system:
step 4.8: parameter updating of the system:
further, the step 5 includes:
Step 5.1: UKF-AUKF is used for jointly estimating the SOC of the lithium battery, UKF is adopted for carrying out on-line parameter identification under a macroscopic time scale, AUKF is used for estimating the SOC of the lithium battery under a microscopic time scale, and on-line parameter identification and SOC joint estimation of the lithium battery based on the UKF-AUKF are realized.
Step 5.2: for the initial value of the circuit model parameters, the advantage of accurate offline parameter identification is absorbed, and the model parameters obtained by using the recursive least square offline parameter identification are used as the initial value of the lithium battery joint estimation
Step 5.3: establishing a battery equivalent circuit model and a state space equation of an initial state of a lithium battery
Step 5.4: performing SOC estimation by using a 60s microcosmic time scale sequence, performing primary parameter identification by taking the switching time scale as a macroscopic time scale after the SOC estimation reaches 60s, updating the identified parameters into a state space equation, and switching
And carrying out SOC estimation for a microscopic time scale, and realizing joint estimation of on-line parameter identification and SOC by cyclic reciprocation. Further, the step 6 includes:
Step 6.1: the accuracy of the model is mainly embodied in two aspects, namely, the error of the voltage of the model terminal and the true value and the estimation accuracy of the SOC.
Step 6.2: and verifying the on-line parameter identification precision of the joint estimation algorithm and the lithium battery SOC estimation precision by using the pulse discharge working condition and the UDDS working condition.
Step 6.3: in order to further explore the precision of the joint estimation algorithm, the traditional RLS off-line parameter identification RLS-EKF algorithm is adopted, the EKF on-line parameter identification DEKF algorithm is adopted, and the UKF-AUKF algorithm is adopted for comparison.
Compared with the prior lithium battery state of charge estimation, the method provided by the invention has the following advantages and beneficial effects:
1. according to the invention, the on-line parameter identification is carried out on the second-order equivalent model of the lithium battery through the UKF algorithm, the SOC of the lithium battery is estimated by combining with the AUKF, the average absolute error of the SOC is reduced to 0.0051, the problems of the time-varying parameters of the lithium battery and the noise influence in the SOC estimation process under the complex working condition are solved, and the accuracy of the battery model is greatly improved; the accuracy and stability of the method are further verified through comparison and analysis of various parameter identification methods and SOC estimation algorithms under the pulse discharge working condition and the UDDS working condition.
2. Compared with the traditional offline parameter identification, the method has the advantages that the accuracy of online parameter identification is obviously higher, and the model of online parameter identification is adaptively updated along with the change of the working condition and is more suitable for the actual working condition of the lithium battery as can be seen from the RLS-EKF and DEKF results; UKF-AUKF precision is highest, and compared with DEKF, its SOC estimation error is littleer, and along with the abrupt change of operating mode, its error fluctuation is little, extremely stable, and the robustness is good.
Drawings
Fig. 1 is a flowchart of on-line parameter identification and SOC joint estimation of a lithium battery according to an embodiment of the present invention.
Fig. 2 is a graph of a fit of open circuit voltage and state of charge of a lithium battery in accordance with one embodiment of the present invention.
Fig. 3 is a graph of experimental voltage and current for a lithium battery according to an embodiment of the present invention under UDDS cycling conditions.
Fig. 4 is a graph of SOC estimation error for each algorithm for a lithium battery in accordance with an embodiment of the present invention under pulsed discharge conditions.
Fig. 5 is a graph of SOC estimation for various algorithms for a lithium battery in accordance with an embodiment of the present invention under UDDS cycling conditions.
Fig. 6 is a graph of SOC estimation errors for various algorithms for a lithium battery in accordance with an embodiment of the present invention under UDDS cycling conditions.
Detailed Description
According to the invention, the on-line parameter identification is carried out on the second-order equivalent model of the lithium battery through the UKF algorithm, and the SOC of the lithium battery is estimated by combining with the AUKF, so that the problems of time-varying parameters of the lithium battery under complex working conditions and noise influence in the SOC estimation process are solved, and along with the abrupt change of the working conditions, the error fluctuation is small, the stability is extremely stable, and the robustness is good.
The invention is described in further detail below with reference to the accompanying drawings.
As shown in FIG. 1, the method for jointly estimating the state of charge of the lithium battery based on the online parameter identification of the unscented Kalman filtering algorithm and the adaptive unscented Kalman filtering mainly comprises the following steps:
step 1: and (5) building an experiment platform for experiment.
Step 2: the lithium battery model is the basis of SOC estimation, and the calculated amount and the precision are weighed, and a second-order equivalent circuit model is selected. And establishing a lithium battery loop equation by using circuit knowledge, and then establishing a state space equation of the online parameters of the lithium battery according to Laplace transformation.
Step 3: the parameter identification of the model is mainly completed through two experiments, namely, the relation between Uoc and SOC is determined through a constant current discharge experiment, and R 0、R1、C1、R2、C2 is identified on line through a UDDS experiment. And determining the functional relation between each parameter of the equivalent circuit and the SOC.
Step 4: and under the macroscopic time scale, carrying out lithium battery parameter identification by using an unscented Kalman filtering algorithm.
Step 5: and when the parameters of the lithium battery model are updated, switching to a microscopic time scale, and estimating the SOC of the lithium battery by using a self-adaptive unscented Kalman filtering algorithm.
Step 6: and verifying the joint estimation accuracy under the pulse discharge working condition and the UDDS working condition.
Further, the step 2 includes:
Step 2.1: establishing a second-order equivalent circuit model of the lithium battery, and listing a loop equation and an observation equation according to the second-order equivalent circuit model:
U0＝Uoc(SOC)-R0I-U1-U2 (13)
Wherein: c 1、C2 is polarization capacitor, U oc is open circuit voltage, U 0 is terminal voltage, T s is sampling time, Q n is battery capacity, R 1、R2 is polarization resistance, and R 0 is ohmic internal resistance of the battery.
Step 2.2: the differential discretization of formulas (12), (13) yields:
Step 2.3: for a nonlinear system
Wherein, is the system noise value and is the measurement noise value.
Further, the step 3 includes:
Step 3.1: discharging the fully charged lithium battery with 30A constant pulse current for 6 minutes, namely 0.1 SOC value, standing the battery for four hours after discharging, recording the open-circuit voltage of the battery, and repeating the operation for 10 times; the fitted U oc -SOC curve is shown in FIG. 2.
Step 3.2: according to the data points of the relation between the 10 groups of U oc and the SOC, which are measured through experiments, the SOC is used as a variable, and eight-order fitting is carried out on the data points of the U oc and the SOC through a formula (16), so that a function of the U oc and the SOC is obtained:
Uocv(SOC)＝p1SOC8+p2SOC7+p3SOC6+p4SOC5+p5SOC4+p6SOC3+p7SOC2+p8SOC+p9
(16)
step 3.3: and the actual working condition is identified by adopting the urban circulation working condition in the United states as an on-line parameter. And under the condition that the battery works, the battery SOC value is reduced by 5% after one-time working condition is satisfied, and 20 times of circulation are performed until SOC=0.
Step 3.4: the incubator is set to 25 ℃, the battery pack is excited by adopting UDDS working condition current, the measured voltage of the battery pack under the working condition is acquired through the data acquisition card, the current and the voltage under the actual working condition are obtained, and the experimental voltage and the current curve of the UDDS circulation working condition are shown in figure 3.
Further, the step 4 includes:
step 4.1: first, according to the state space equation of the formula (14), the state space equation with the parameter variable and the state variable as independent variables is expressed:
In the above formula, θ= [ R 0 R1 C1 R2 C2]T, macroscopic scale L=60deg.S, microscopic scale sequence L epsilon (1-L), w k,lvk,l is process noise and observation noise of the system, and ρ k is model parameter process noise.
Step 4.2: initializing parameter variables and parameter variable covariance, and determining parameters alpha=0.01, k i =0, beta=2 and M=5 of the UKF algorithm.
Step 4.3: calculating a sampling point at k moment
Step 4.4: calculating weights
Step 4.5: parameter predictors and system variance predictors
Step 4.6: the prediction parameters are updated and the prediction parameters are updated,
Step 4.7: the observed value and observed variance prediction value P yy are updated,
Step 4.8: parameter variable covariance and unscented Kalman gain K
Step 4.9: system observations updates
Step 4.10: parameter updating of a system
Further, the step 5 includes:
Step 5.1: UKF-AUKF is used for jointly estimating the SOC of the lithium battery, UKF is adopted for carrying out on-line parameter identification under a macroscopic time scale, AUKF is used for estimating the SOC of the lithium battery under a microscopic time scale, and on-line parameter identification and SOC joint estimation of the lithium battery based on the UKF-AUKF are realized.
Step 5.2: for the initial value of the circuit model parameters, the advantage of accurate offline parameter identification is absorbed, and the model parameters obtained by using the recursive least square offline parameter identification are used as the initial value of the lithium battery joint estimation
Step 5.3: establishing a battery equivalent circuit model and a state space equation of an initial state of a lithium battery
Step 5.4: and (3) carrying out SOC estimation by using a microscopic time scale sequence of 60s, carrying out primary parameter identification by switching the time scale to a macroscopic time scale after the SOC estimation reaches 60s, updating the identified parameters into a state space equation, and then switching the parameters to the microscopic time scale to carry out SOC estimation, so that the combination estimation of online parameter identification and SOC is realized by cyclic reciprocation.
Further, the step 6 includes:
Step 6.1: the accuracy of the model is mainly embodied in two aspects, namely, the error of the voltage of the model terminal and the true value and the estimation accuracy of the SOC.
Step 6.2: and verifying the on-line parameter identification precision of the joint estimation algorithm and the lithium battery SOC estimation precision by using the pulse discharge working condition and the UDDS working condition.
Step 6.3: in order to further explore the precision of the joint estimation algorithm, the traditional RLS off-line parameter identification RLS-EKF algorithm is adopted, the EKF on-line parameter identification DEKF algorithm is adopted, and the UKF-AUKF algorithm is adopted for comparison.
The invention estimates the lithium battery SOC by comparing with the EKF algorithm under RLS offline parameter identification, the SOC estimation error is shown in figure 4, the UKF-AUKF SOC error is smaller as a whole, the fluctuation is less, and the robustness is good. The model accuracy obtained by two kinds of parameter identification is not great in the standing stage, but when the current changes, the UKF online parameter identification always can track the parameter change quickly, and the model identified by RLS is always constant, so that the error can be increased and fluctuation occurs. In order to further explore the precision of the joint estimation algorithm, the model obtained by identifying each parameter under the UDDS working condition is subjected to SOC estimation, namely, an RLS-EKF algorithm of traditional RLS off-line parameter identification is adopted, an EKF on-line parameter identification DEKF algorithm is adopted, and the UKF-AUKF algorithm is adopted, the battery SOC estimation is shown in fig. 5, the SOC estimation error of each joint estimation is shown in fig. 6, the UKF-AUKF is most close to a true value, the estimation precision of the DEKF algorithm is slightly poor, and the estimation precision of the RLS-EKF is the worst; when the SOC algorithm is ensured to be the EKF algorithm, DEKF is obviously superior to the RLS-EKF, the accuracy of the online parameter identification model is proved, and compared with the SOC estimation method based on the offline parameter identification, the SOC estimation method based on the online parameter identification of DEKF and UKF-AUKF is higher in accuracy. Compared with DEKF, the UKF-AUKF has smaller SOC estimation error, further illustrates that the UKF-AUKF solves the linearization process of expanding by a Taylor formula in an EKF algorithm, and avoids error caused by omission of a higher term due to linearization; the algorithm has small error fluctuation under the complex working condition, which proves that UKF-AUKF can filter noise well.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any changes or substitutions easily contemplated by those skilled in the art within the scope of the present invention should be included in the scope of the present invention.
Claims (1)
1. The method for jointly estimating the lithium battery state of charge based on the unscented Kalman filtering algorithm on-line parameter identification and the self-adaptive unscented Kalman filtering comprises the following steps:
Step 1: setting up an experiment platform for experiment;
step 2: the lithium battery model is the basis of SOC estimation, the calculated amount and the precision are weighed, and a second-order equivalent circuit model is selected; establishing a lithium battery loop equation by using circuit knowledge, and then establishing a state space equation of online parameters of the lithium battery according to Laplace transformation;
Step 3: the parameter identification of the model is completed through two experiments, namely, the relation between U oc and SOC is determined through a constant current discharge experiment, and R 0、R1、C1、R2、C2 is identified on line through a UDDS experiment; determining the functional relation between each parameter of the equivalent circuit and the SOC;
Step 4: under the macroscopic time scale, carrying out lithium battery parameter identification by using an unscented Kalman filtering algorithm;
Step 5: when the parameters of the lithium battery model are updated, switching to a microscopic time scale, and estimating the SOC of the lithium battery by using a self-adaptive unscented Kalman filtering algorithm;
Step 6: verifying joint estimation accuracy under the pulse discharge working condition and the UDDS working condition;
further, the step 2 includes:
Step 2.1: establishing a second-order equivalent circuit model of the lithium battery, and listing a loop equation and an observation equation according to the second-order equivalent circuit model:
U0＝Uoc(SOC)-R0I-U1-U2 (2)
Wherein: c 1、C2 is a polarization capacitor, U oc is an open-circuit voltage, U 0 is a terminal voltage, T s is sampling time, Q n is battery capacity, R 1、R2 is a polarization resistor, and R 0 is ohmic internal resistance of the battery;
step 2.2: the differential discretization of formulas (1) and (2) is obtained:
Step 2.3: for a nonlinear system
Where ω k is the system noise value and v k is the measurement noise value;
further, the step 3 includes:
Step 3.1: discharging the fully charged lithium battery with 30A constant pulse current for 6 minutes, namely 0.1 SOC value, standing the battery for four hours after discharging, recording the open-circuit voltage of the battery, and repeating the operation for 10 times; fitting a U oc -SOC curve;
Step 3.2: according to the data points of the relation between 10 groups of U oc and the SOC, which are measured through experiments, the SOC is used as a variable, and eighth-order fitting is carried out on the data points of U oc and the SOC, so that a function of U oc and the SOC is obtained;
step 3.3: the method comprises the steps of adopting the urban circulation working condition in the United states as an on-line parameter to identify an actual working condition; under the condition that the battery works, the battery SOC value is reduced by 5% under the condition of one-time circulation, and 20 times of circulation is carried out until SOC=0;
step 3.4: the constant temperature box is set to 25 ℃, the battery pack is excited by adopting UDDS working condition current, the measured voltage of the battery pack under the working condition is acquired by the data acquisition card, the current and the voltage under the actual working condition are obtained, and the UDDS circulating working condition experimental voltage and current curves are obtained;
Further, the step 4 includes:
Step 4.1: first, according to the state space equation of the formula (3), the state space equation with the parameter variable and the state variable as independent variables is expressed:
In the above, θ= [ R 0 R1 C1 R2 C2]T, macroscopic scale L=60deg.S, microscopic scale sequence L epsilon (1-L), w k,lvk,l is process noise and observation noise of the system, and ρ k is model parameter process noise;
step 4.2: initializing parameter variables and parameter variable covariance, and determining parameters alpha=0.01, k i =0, beta=2 and M=5 of the UKF algorithm;
step 4.3: calculating a sampling point at k moment
Step 4.4: calculating weights
Step 4.5: parameter predictors and system variance predictors
Step 4.6: the prediction parameters are updated and the prediction parameters are updated,
Step 4.7: the observed value and observed variance prediction value P yy are updated,
Step 4.8: parameter variable covariance and unscented Kalman gain K
Step 4.9: system observations updates
Step 4.10: parameter updating of a system
Further, the step 5 includes:
step 5.1: UKF-AUKF jointly estimates the SOC of the lithium battery, UKF is adopted to conduct on-line parameter identification under a macroscopic time scale, AUKF is adopted to estimate the SOC of the lithium battery under a microscopic time scale, and on-line parameter identification and SOC joint estimation of the lithium battery based on the UKF-AUKF are achieved;
Step 5.2: for the initial value of the circuit model parameters, the advantage of accurate offline parameter identification is absorbed, and the model parameters obtained by using the recursive least square offline parameter identification are used as the initial value of the lithium battery joint estimation
Step 5.3: establishing a battery equivalent circuit model and a state space equation of an initial state of a lithium battery
Step 5.4: performing SOC estimation by using a microscopic time scale sequence of 60s, performing primary parameter identification by switching the time scale to a macroscopic time scale after the SOC estimation reaches 60s, updating the identified parameters into a state space equation, performing SOC estimation by switching the identified parameters to the microscopic time scale, and performing joint estimation of on-line parameter identification and SOC by cyclic reciprocation;
Further, the step 6 includes:
step 6.1: the accuracy of the model is embodied in two aspects, namely, the error of the voltage of the model terminal and the true value and the estimation accuracy of the SOC;
Step 6.2: verifying the on-line parameter identification precision of the joint estimation algorithm and the lithium battery SOC estimation precision by using the pulse discharge working condition and the UDDS working condition;
step 6.3: in order to further explore the precision of the joint estimation algorithm, the traditional RLS off-line parameter identification RLS-EKF algorithm is adopted, and the EKF on-line parameter identification DEKF algorithm and the UKF-AUKF algorithm are adopted for comparison.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210395241.5A CN114839550B (en) | 2022-04-14 | 2022-04-14 | UKF-AUKF-based lithium battery SOC joint estimation method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210395241.5A CN114839550B (en) | 2022-04-14 | 2022-04-14 | UKF-AUKF-based lithium battery SOC joint estimation method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114839550A CN114839550A (en) | 2022-08-02 |
| CN114839550B true CN114839550B (en) | 2024-05-10 |
Family
ID=82565291
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210395241.5A Active CN114839550B (en) | 2022-04-14 | 2022-04-14 | UKF-AUKF-based lithium battery SOC joint estimation method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114839550B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112858928B (en) * | 2021-03-08 | 2024-02-06 | 安徽理工大学 | Lithium battery SOC estimation method based on online parameter identification |
| CN113138344B (en) * | 2021-04-23 | 2024-03-05 | 安徽理工大学 | SOC estimation method based on fractional order extended Kalman filtering algorithm |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103744026A (en) * | 2013-12-19 | 2014-04-23 | 广西科技大学 | Storage battery state of charge estimation method based on self-adaptive unscented Kalman filtering |
| CN107064811A (en) * | 2017-03-01 | 2017-08-18 | 华南理工大学 | A kind of lithium battery SOC On-line Estimation methods |
| WO2019113999A1 (en) * | 2017-12-14 | 2019-06-20 | 江苏大学 | Gas-liquid dynamic model-based accurate lithium-ion battery soc estimation method |
| CN112858928A (en) * | 2021-03-08 | 2021-05-28 | 安徽理工大学 | Lithium battery SOC estimation method based on online parameter identification |
| CN113030752A (en) * | 2021-04-12 | 2021-06-25 | 安徽理工大学 | Online parameter identification and SOC joint estimation method based on forgetting factor |
| CN114167298A (en) * | 2021-10-28 | 2022-03-11 | 上海空间电源研究所 | Lithium ion battery SOC estimation method and system based on improved EKF |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11360147B2 (en) * | 2020-03-03 | 2022-06-14 | Karma Automotive Llc | Method of determining the state of charge of a battery used in an electric vehicle |
-
2022
- 2022-04-14 CN CN202210395241.5A patent/CN114839550B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103744026A (en) * | 2013-12-19 | 2014-04-23 | 广西科技大学 | Storage battery state of charge estimation method based on self-adaptive unscented Kalman filtering |
| CN107064811A (en) * | 2017-03-01 | 2017-08-18 | 华南理工大学 | A kind of lithium battery SOC On-line Estimation methods |
| WO2019113999A1 (en) * | 2017-12-14 | 2019-06-20 | 江苏大学 | Gas-liquid dynamic model-based accurate lithium-ion battery soc estimation method |
| CN112858928A (en) * | 2021-03-08 | 2021-05-28 | 安徽理工大学 | Lithium battery SOC estimation method based on online parameter identification |
| CN113030752A (en) * | 2021-04-12 | 2021-06-25 | 安徽理工大学 | Online parameter identification and SOC joint estimation method based on forgetting factor |
| CN114167298A (en) * | 2021-10-28 | 2022-03-11 | 上海空间电源研究所 | Lithium ion battery SOC estimation method and system based on improved EKF |
Non-Patent Citations (3)
| Title |
|---|
| Magnetic and Cryogenic Design of MICE Coupling Solenoid Magnet System;Li Wang 等;IEEE Transactions on Applied Superconductivity;20190630;第19卷(第3期);全文 * |
| 基于AUKF算法的锂电池SOC估算;边东生;现代机械;20220228(第1期);全文 * |
| 基于UKF-AUKF锂电池在线参数辨识和SOC联合估计;卢云帆 等;电源技术;20221031;第46卷(第10期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114839550A (en) | 2022-08-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114839550B (en) | UKF-AUKF-based lithium battery SOC joint estimation method | |
| CN111965547B (en) | Battery system sensor fault diagnosis method based on parameter identification method | |
| Li et al. | Review of lithium-ion battery state of charge estimation | |
| CN110261779A (en) | A kind of ternary lithium battery charge state cooperates with estimation method with health status online | |
| CN111948560A (en) | Lithium battery health state estimation method based on multi-factor evaluation model | |
| CN111337832A (en) | Power battery multidimensional fusion SOC and SOH online joint estimation method | |
| CN111060824B (en) | Charge state estimation method based on model and algorithm step-by-step fusion | |
| CN112989690B (en) | Multi-time-scale state-of-charge estimation method for lithium battery of hybrid electric vehicle | |
| CN112444749A (en) | Lithium battery state of charge joint estimation method based on temperature correction model | |
| CN115598541B (en) | Battery energy state evaluation method based on forgetting factor adaptive feedback correction | |
| CN112269133B (en) | SOC estimation method based on pre-charging circuit model parameter identification | |
| CN112946481A (en) | Based on federation H∞Filtering sliding-mode observer lithium ion battery SOC estimation method and battery management system | |
| CN114114038A (en) | Lithium battery SOC and available capacity joint estimation method under full-life and full-temperature conditions | |
| CN116718920B (en) | Lithium battery SOC estimation method based on RNN (RNN-based optimized extended Kalman filter) | |
| CN112946480B (en) | Lithium battery circuit model simplification method for improving SOC estimation real-time performance | |
| Zhang et al. | State of charge estimation of lithium battery based on FFRLS-SRUKF algorithm | |
| CN116298913A (en) | Lithium battery state of charge estimation method based on online and offline identification combination | |
| CN116449219A (en) | Lithium battery model parameter identification method for improving VFFRLS | |
| CN115144768A (en) | Power lithium battery SOC online estimation method | |
| CN114660463A (en) | Lithium battery SOC estimation method based on self-adaptive weighted volume particle filter | |
| Guo et al. | Online parameter identification for fractional order model of lithium ion battery via adaptive genetic algorithm | |
| Xing et al. | State of charge estimation for electric vehicle lithium-ion batteries based on model parameter adaptation | |
| Zixuan et al. | State of charge estimation for the vanadium redox flow battery based on the Sage–Husa adaptive extended Kalman filter | |
| Li et al. | State Estimation of Lithium-Ion Battery at Different Temperatures Based on DEKF and RLS | |
| Fan et al. | SOC Estimation Based on Modified Covariance Extended Kalman Filter for Power Batteries of Electric Vehicles |
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 |