CN111007400A - Lithium battery SOC estimation method based on self-adaptive double-extended Kalman filtering method - Google Patents

Lithium battery SOC estimation method based on self-adaptive double-extended Kalman filtering method Download PDF

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CN111007400A
CN111007400A CN201911158088.9A CN201911158088A CN111007400A CN 111007400 A CN111007400 A CN 111007400A CN 201911158088 A CN201911158088 A CN 201911158088A CN 111007400 A CN111007400 A CN 111007400A
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乌江
焦朝勇
陈猛
陈继忠
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Xian Polytechnic University
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Abstract

The invention discloses an SOC estimation method based on a self-adaptive dual-extended Kalman filtering method, which comprises the steps of firstly establishing a second-order RC equivalent circuit model of a lithium battery; then determining open-circuit voltages and battery equivalent model parameters at different SOC positions of the lithium battery through a pulse charging and discharging experiment to obtain a functional relation between the open-circuit voltages and the SOC and relations between other model parameters and the different SOC positions, wherein the other model parameters comprise ohmic internal resistance, electrochemical polarization capacitance, concentration difference polarization resistance and concentration difference polarization capacitance; establishing a state space equation with the SOC and the polarization voltage as state variables and a state space equation with the ohmic internal resistance as the state variables; and finally, carrying out iterative computation to obtain the SOC value of the lithium battery in real time. The method solves the problem of unknown noise statistical characteristics in the prior art, and simultaneously estimates the ohmic internal resistance of the battery by using a Kalman filtering algorithm, thereby improving the model precision.

Description

Lithium battery SOC estimation method based on self-adaptive double-extended Kalman filtering method
Technical Field
The invention belongs to the technical field of lithium battery state estimation, and particularly relates to an SOC estimation method based on a self-adaptive dual-extended Kalman filtering method.
Background
With the development of clean energy, lithium batteries are increasingly applied to the fields of wind, light energy storage, electric vehicles and the like. In order to ensure safe and effective operation of the battery, a battery management system needs to be established, so that parameters such as voltage, current and temperature of the battery are monitored in real time, and State information such as State of Charge (SOC) and health State of the battery is accurately estimated. Wherein accurately estimating the SOC is the basis of other state estimations; the overcharge and the overdischarge can be avoided, and the service life of the battery is prolonged; the system can also help the user to make a correct product use plan, and has very important significance.
The current methods for estimating SOC mainly include ampere-hour integration method, open-circuit voltage method, neural network method, Extended Kalman Filter (EKF). The ampere-hour integration method is simple in principle and easy to implement, but cannot provide an initial value of the SOC, and has the problem that errors are accumulated more and more by integral operation. The Open circuit voltage method (OCV) obtains the SOC value by means of an SOC-OCV relationship curve between the Open circuit voltage and the SOC, and is simple and direct, but cannot be used in the battery charging and discharging process due to the need of measuring the Open circuit voltage. The neural network method does not need to consider the internal structure of the battery, but the premise is that a large amount of experimental data are used for training, the estimation precision depends on the pertinence and comprehensiveness of the training data, and the algorithm is generally complex and difficult to realize. The EKF algorithm can effectively combine an ampere-hour integration method and an open-circuit voltage method, the result of the ampere-hour integration method is corrected in real time, the accumulated error is eliminated, the estimation result does not depend on the initial value of the SOC, and the EKF algorithm is the mainstream method researched at present. However, the EKF algorithm assumes the system noise as determined white gaussian noise during the estimation process, which is not in accordance with the actual situation and may result in a large estimation error. The EKF algorithm is a model-based algorithm, the accuracy of the model greatly influences the accuracy of the estimation result, but the accuracy of the model is reduced due to the continuous change of the internal parameters of the battery, and the accuracy of the algorithm estimation is also influenced.
Disclosure of Invention
The invention aims to provide a lithium battery SOC estimation method based on a self-adaptive double-extended Kalman filtering method, which solves the problem of unknown noise statistical characteristics in the prior art, and simultaneously estimates the ohmic internal resistance of a battery by using a Kalman filtering algorithm, thereby improving the model precision.
The technical scheme adopted by the invention is that a lithium battery SOC estimation method based on a self-adaptive double-extended Kalman filtering method is implemented according to the following steps:
step 1, establishing a second-order RC equivalent circuit model of a lithium battery;
step 2, determining open-circuit voltages and battery equivalent model parameters at different SOC positions of the lithium battery through a pulse charge-discharge experiment, then obtaining a specific function relation between the open-circuit voltages and the SOC through function fitting, and constructing relations between other model parameters and the different SOC positions, wherein the other model parameters comprise ohmic internal resistance, electrochemical polarization capacitance, concentration difference polarization resistance and concentration difference polarization capacitance values;
step 3, respectively establishing a state space equation with SOC and polarization voltage as state variables and a state space equation with ohmic internal resistance as the state variables according to an equivalent circuit model of the lithium battery;
and 4, estimating the SOC of the battery by using a self-adaptive extended Kalman filtering algorithm, estimating the ohmic internal resistance of the battery by using the Kalman filtering algorithm, and finally performing iterative computation to obtain the SOC value of the lithium battery in real time.
The present invention is also characterized in that,
the second-order RC equivalent circuit model of the lithium battery in the step 1 comprises two RC parallel circuits which are connected in series, wherein the electrochemical polarization resistance R of one RC parallel circuitLAnd electrochemical polarization capacitance CLThe concentration difference polarization resistor R of the other RC parallel circuit is connected with an open-circuit voltage source in the charging and discharging directions and then connected with voltageSAnd concentration difference polarization capacitance CSCommon series ohmic internal resistance RoAnd then voltage is switched on.
Step 2, dividing the open-circuit voltage into two parts according to charging and discharging directions, and discharging the open-circuit voltage when the discharge state is inUOCAnd the diodes connected in series work to charge an open-circuit voltage U'OCNot working; when in a charging state, charging an open-circuit voltage U'OCAnd the diode connected in series works, discharges open circuit voltage UOCThe lithium battery charging or discharging circuit does not work, and in the two processes of charging or discharging the lithium battery, each process selects a respective open-circuit voltage source U due to different directions of charging or discharging currentOCOr U'OCRespectively obtaining the relation between the open-circuit voltage and the SOC in the charging and discharging processes and the corresponding relation between the ohmic internal resistance, the electrochemical polarization capacitance, the concentration difference polarization resistance and the concentration difference polarization capacitance value and the SOC in the discharging process;
the lithium battery SOC and the electrochemical polarization voltage U which are established in the step 3 and take the SOC and the polarization voltage as state variablesLPolarization voltage U different from concentrationSThe state space equation of (a) is:
Figure BDA0002285343920000031
the equation for the terminal voltage of the lithium battery is as follows:
U(k)＝UOC(SOCk)-US(k)-UL(k)-Ro(k)i(k)+vk
where Δ T is the sampling time, RoIs the ohmic internal resistance, R, of the batteryL、CLElectrochemical polarization resistance and polarization capacitance, R, of the cellS、CSConcentration difference polarization resistance and polarization capacitance of the battery, tauL、τSRespectively represents electrochemical polarization time constant and concentration difference polarization time constant, whereinL＝RLCL，τS＝RSCS，USIs RSVoltage across, ULIs RLVoltage across, UOCIs the open circuit voltage of the battery, i is the operating current of the battery, U is the operating voltage of the battery, wkFor SOC estimation process noise, vkMeasurement noise for SOC estimation, CNRepresenting the current capacity of the lithium battery, and k representing the current iterative calculation stepNumber, SOC (k) represents the state of charge SOC value in the kth calculation.
The established state space equation with the ohmic internal resistance as the state variable is as follows:
Ro,k＝Ro,k-1+rk
U(k)＝UOC(SOCk)-US(k)-UL(k)-Ro(k)i(k)+qk
wherein r iskTo obtain ohmic internal resistance RoEstimation of process noise, q, for state variables by Kalman filteringkTo obtain ohmic internal resistance RoEstimation of measurement noise, R, for state variables by means of Kalman filteringo,kFor the ohmic internal resistance estimated value in the k iterative calculation, Ro,k-1The ohmic internal resistance estimation value of the previous time, namely the k-1 time.
Step 4 is specifically implemented according to the following steps:
step 4.1, setting initial values of state error covariance P in state of charge (SOC) and Kalman filtering algorithm of lithium battery, and selecting system noise value as 10-4Obtaining an initial value of the ohmic internal resistance according to the initial value of the SOC and the corresponding relation between the other model parameters and the SOC in the step 2;
step 4.2, obtaining the electrochemical polarization internal resistance R in the equivalent circuit model according to the estimation result of the SOC at the moment and by combining the corresponding relation between other model parameters and the SOC in the step 2LAnd a capacitor CLConcentration difference polarization resistance RSAnd a capacitor CSA value of (d); replacing the SOC value which is not in the corresponding relation table by a parameter value corresponding to the SOC close to the left side;
step 4.3, calculating the state of charge SOC and the ohmic internal resistance RoRespectively iterating and calculating the state predicted values of the SOC and the ohmic internal resistance and the error covariance predicted value by respective state equations;
4.4, calculating Kalman filtering gain P of the SOC and the ohmic internal resistance;
step 4.5, substituting the state predicted values of the SOC and the ohmic internal resistance into an observation equation to obtain a predicted value of the observed quantity;
step 4.6, calculating the system measurement innovation;
4.7, obtaining a state estimator at the current moment according to the measurement information and respective Kalman filtering gains of the SOC and the ohmic internal resistance, and updating the error covariance;
step 4.8, adjusting the process noise covariance of SOC estimation;
and 4.9, substituting the state quantity and the error covariance obtained in the step 4.3, the gain P obtained in the step 4.4, the observation quantity predicted value obtained in the step 4.5 and the innovation obtained in the step 4.6 into the step 4.2 to obtain a value of the SOC of the lithium battery, and starting a new round of cycle iteration.
Step 4.3 is specifically as follows:
the state prediction value formula is calculated as follows:
Figure BDA0002285343920000051
wherein the content of the first and second substances,
Figure BDA0002285343920000052
is a predicted value of the state at the current moment,
Figure BDA0002285343920000053
is the state quantity of the previous moment, ukF is an input variable at the current moment, f is a state prediction function, and k represents the iterative computation step number at the current moment;
the error covariance predictor is calculated as:
Pk/k-1＝Fk-1Pk-1Fk-1 T+Qk
wherein, Pk/k-1For the state prediction value at the current time, Pk-1Is a state quantity of the previous moment, QkIs the process noise covariance, F, at the current timek-1By state prediction function
Figure BDA0002285343920000054
And solving a Jacobian matrix for the state predicted value at the previous moment to obtain the state predicted value.
Step 4.4 is specifically as follows:
calculating the Kalman filtering gain as follows:
Kk＝Pk/k-1Gk T(GkPk/k-1Gk T+R)-1
wherein, Pk/k-1For the state prediction value at the present time, KkFor the Kalman filter gain at the current time, R is the measurement noise covariance, GkAnd solving the Jacobian matrix of the state predicted value at the current moment by the observation function.
Step 4.6 is specifically as follows:
the computing system measures innovation:
υj＝yj-yj/j-1
wherein upsilon isjRepresents the system measurement information, yjIndicating the system measurements at a previous time, yj/j-1The predicted value of the system measurement at a certain previous moment is shown, and j represents the iterative computation step number at a certain historical moment.
Step 4.7 is specifically as follows:
calculating the state estimator at the current moment:
Figure BDA0002285343920000061
wherein, ykG is an observation function;
updating the error covariance:
Pk＝(E-KkGk)Pk/k-1
wherein E represents an identity matrix, Pk/k-1For the state prediction value at the present time, KkFor the Kalman filter gain at the present moment, GkAnd solving the Jacobian matrix of the state predicted value at the current moment by the observation function.
Step 4.8 is specifically as follows:
adjusting the process noise covariance of state of charge, SOC, estimation:
Figure BDA0002285343920000062
Qk＝KkSkKk T
wherein m represents the window width of the statistical measurement information history information, SkMean value, u, representing the first m measured valuesjIndicating system measurement information, KkFor the Kalman filter gain, Q, of the current timekAnd k represents the iterative computation step number at the current moment, and j represents the iterative computation step number at a certain historical moment.
The invention has the beneficial effects that:
(1) compared with the traditional ampere-hour integral method, the estimation result of the method does not depend on the initial value of the SOC, and can be quickly converged to the true value after a plurality of iterations even if the initial value is inaccurate. The invention has the function of inhibiting system noise and can eliminate the influence of integral accumulation error.
(2) Compared with the SOC-OCV curve in a single direction, the SOC-OCV curve in the charging and discharging directions is adopted during charging and discharging, so that the equivalent model of the battery can be applied under the working condition that charging and discharging are continuously and alternately carried out, and the estimation precision of the algorithm is not reduced due to the change of the current direction.
(3) Compared with the general extended Kalman filtering method, the method for adaptively adjusting the noise covariance is adopted on the basis of the extended Kalman filtering method, the problem that the noise statistical characteristic is unknown in practice is solved, the method can adapt to different working conditions, and the estimation precision is improved. In addition, considering that filtering divergence is easily caused by simultaneously adjusting the process noise covariance and measuring the noise covariance, the method only adjusts the process noise covariance, and reduces the possibility of filtering divergence.
(4) Compared with the method of taking fixed values for equivalent circuit model parameters, the method adopts the Kalman filtering algorithm to dynamically estimate the ohmic internal resistance which is greatly influenced by factors such as current, temperature, aging condition and the like, can track the change of main parameters in the battery in real time, and has higher estimation precision under the condition that the internal and external conditions of the battery are changed.
Drawings
FIG. 1 is a second-order RC equivalent circuit model of a lithium battery;
FIG. 2 is a schematic diagram of an algorithm;
FIG. 3 is a flow chart of an algorithm;
FIG. 4 is a dynamic stress test condition monocycle current;
fig. 5 is a graph of the estimation and error of SOC using the algorithm of the present invention.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
The invention relates to a lithium battery SOC estimation method based on a self-adaptive double-extended Kalman filtering method, which is implemented according to the following steps:
step 1, as shown in fig. 1, establishing a second-order RC equivalent circuit model of the lithium battery;
the second-order RC equivalent circuit model of the lithium battery in the step 1 comprises two RC parallel circuits which are connected in series, wherein the electrochemical polarization resistance R of one RC parallel circuitLAnd electrochemical polarization capacitance CLThe concentration difference polarization resistor R of the other RC parallel circuit is connected with an open-circuit voltage source in the charging and discharging directions and then connected with voltageSAnd concentration difference polarization capacitance CSCommon series ohmic internal resistance RoAnd then voltage is switched on.
Step 2, determining open-circuit voltages and battery equivalent model parameters at different SOC positions of the lithium battery through a pulse charge-discharge experiment, then obtaining a specific function relation between the open-circuit voltages and the SOC through function fitting, and constructing relations between other model parameters and the different SOC positions, wherein the other model parameters comprise ohmic internal resistance, electrochemical polarization capacitance, concentration difference polarization resistance and concentration difference polarization capacitance values;
in an equivalent circuit model of the battery, the open-circuit voltage is divided into two parts according to the charging and discharging directions, each part has different relations with the SOC due to different current directions, and the relations between the open-circuit voltage and the SOC in the charging and discharging processes need to be obtained respectively.
Step 2, dividing the open-circuit voltage into two parts according to charging and discharging directions, and discharging the open-circuit voltage U when the discharge state is inOCAnd the diodes connected in series work to charge an open-circuit voltage U'OCNot working; when in a charging state, charging an open-circuit voltage U'OCAnd the diode connected in series works, discharges open circuit voltage UOCThe lithium battery charging or discharging circuit does not work, and in the two processes of charging or discharging the lithium battery, each process selects a respective open-circuit voltage source U due to different directions of charging or discharging currentOCOr U'OCRespectively obtaining the relation between the open-circuit voltage and the SOC in the charging and discharging processes and the corresponding relation between the ohmic internal resistance, the electrochemical polarization capacitance, the concentration difference polarization resistance and the concentration difference polarization capacitance value and the SOC in the discharging process;
step 3, respectively establishing a state space equation with SOC and polarization voltage as state variables and a state space equation with ohmic internal resistance as the state variables according to an equivalent circuit model of the lithium battery;
lithium battery SOC and electrochemical polarization voltage U which are established in step 3 and take SOC and polarization voltage as state variablesLPolarization voltage U different from concentrationSThe state space equation of (a) is:
Figure BDA0002285343920000091
the equation for the terminal voltage of the lithium battery is as follows:
U(k)＝UOC(SOCk)-US(k)-UL(k)-Ro(k)i(k)+vk
where Δ T is the sampling time, RoIs the ohmic internal resistance, R, of the batteryL、CLElectrochemical polarization resistance and polarization capacitance, R, of the cellS、CSConcentration difference polarization resistance and polarization capacitance of the battery, tauL、τSRespectively representing an electrochemical polarization time constant and a concentration difference polarization time constant, whichMiddle tauL＝RLCL，τS＝RSCS，USIs RSVoltage across, ULIs RLVoltage across, UOCIs the open circuit voltage of the battery, i is the operating current of the battery, U is the operating voltage of the battery, wkIs SOC process noise, vkMeasuring noise for SOC, CNRepresenting the current capacity of the lithium battery, k representing the number of iterative calculation steps at the current moment, and SOC (k) representing the SOC value in the k-th calculation.
The established state space equation with the ohmic internal resistance as the state variable is as follows:
Ro,k＝Ro,k-1+rk
U(k)＝UOC(SOCk)-US(k)-UL(k)-Ro(k)i(k)+qk
wherein r iskTo obtain ohmic internal resistance RoProcess noise, q, estimated for the Kalman filtering of state variableskTo obtain ohmic internal resistance RoMeasurement noise, R, estimated for the Kalman Filter method of the State variableso,kFor the ohmic internal resistance estimated value in the k iterative calculation, Ro,k-1The ohmic internal resistance estimation value of the previous time, namely the k-1 time.
And 4, estimating the SOC of the battery by using a self-adaptive extended Kalman filtering algorithm, estimating the ohmic internal resistance of the battery by using the Kalman filtering algorithm, and finally performing iterative computation to obtain the SOC value of the lithium battery in real time. The basic schematic diagram of the algorithm is shown in fig. 2, and according to the schematic diagram, the execution flow of the algorithm can be obtained, as shown in fig. 3.
Step 4 is specifically implemented according to the following steps:
step 4.1, setting initial values of state error covariance P in state of charge (SOC) and Kalman filtering algorithm of lithium battery, and selecting system noise value as 10-4Obtaining an initial value of the ohmic internal resistance according to the initial value of the SOC and the corresponding relation between the other model parameters and the SOC in the step 2;
step 4.2, according to the estimation result of the SOC at the moment, combining other model parameters and the SOC in the step 2Obtaining electrochemical polarization internal resistance R in the equivalent circuit model according to the corresponding relationLAnd a capacitor CLConcentration difference polarization resistance RSAnd a capacitor CSA value of (d); replacing the SOC value which is not in the corresponding relation table by a parameter value corresponding to the SOC close to the left side;
step 4.3, calculating the state of charge SOC and the ohmic internal resistance RoRespectively iterating and calculating state predicted values of the SOC and the ohmic internal resistance and error covariance predicted values by respective state equations;
4.4, calculating Kalman filtering gain P of the SOC and the ohmic internal resistance;
step 4.5, substituting the state predicted values of the SOC and the ohmic internal resistance into an observation equation to obtain a predicted value of the observed quantity;
step 4.6, calculating the system measurement innovation;
step 4.7, obtaining the state estimation quantity of the current moment by the Kalman filtering gains of the measurement information, the state of charge (SOC) and the ohmic internal resistance, and updating the error covariance;
step 4.8, adjusting the process noise covariance of SOC estimation;
and 4.9, substituting the state quantity and the error covariance obtained in the step 4.3, the gain P obtained in the step 4.4, the observation quantity predicted value obtained in the step 4.5 and the innovation obtained in the step 4.6 into the step 4.2 to obtain a value of the SOC of the lithium battery, and starting a new round of cycle iteration.
Step 4.3 is specifically as follows:
the state prediction value formula is calculated as follows:
Figure BDA0002285343920000111
wherein the content of the first and second substances,
Figure BDA0002285343920000112
is a predicted value of the state at the current moment,
Figure BDA0002285343920000113
is the previous oneAmount of state of etching, ukF is an input variable at the current moment, f is a state prediction function, and k represents the iterative computation step number at the current moment;
the error covariance predictor is calculated as:
Pk/k-1＝Fk-1Pk-1Fk-1 T+Qk
wherein, Pk/k-1For the state prediction value at the current time, Pk-1Is a state quantity of the previous moment, QkIs the process noise covariance, F, at the current timek-1By state prediction function
Figure BDA0002285343920000114
And solving a Jacobian matrix for the state predicted value at the previous moment to obtain the state predicted value.
Step 4.4 is specifically as follows:
calculating the Kalman filtering gain as follows:
Kk＝Pk/k-1Gk T(GkPk/k-1Gk T+R)-1
wherein, Pk/k-1For the state prediction value at the present time, KkFor the Kalman filter gain at the current time, R is the measurement noise covariance, GkAnd solving the Jacobian matrix of the state predicted value at the current moment by the observation function.
Step 4.6 is specifically as follows:
the computing system measures innovation:
υj＝yj-yj/j-1
wherein upsilon isjRepresents the system measurement information, yjIndicating the system measurements at a previous time, yj/j-1The predicted value of the system measurement at a certain previous moment is shown, and j represents the iterative computation step number at a certain historical moment.
Step 4.7 is specifically as follows:
calculating the state estimator at the current moment:
Figure BDA0002285343920000121
wherein, ykG is an observation function;
updating the error covariance:
Pk＝(E-KkGk)Pk/k-1
wherein E represents an identity matrix, Pk/k-1For the state prediction value at the present time, KkFor the Kalman filter gain at the present moment, GkAnd solving the Jacobian matrix of the state predicted value at the current moment by the observation function.
Step 4.8 is specifically as follows:
adjusting the process noise covariance of state of charge, SOC, estimation:
Figure BDA0002285343920000122
Qk＝KkSkKk T
wherein m represents the window width of the statistical measurement information history information, SkMean value, u, representing the first m measured valuesjIndicating system measurement information, KkFor the Kalman filter gain, Q, of the current timekAnd k represents the iterative computation step number at the current moment, and j represents the computation step number at a certain historical moment.
In order to verify the accuracy of SOC estimation, a certain lithium iron phosphate battery with the nominal capacity of 1.7Ah is taken as a research object to perform a simulation working condition experiment. The simulated working condition is a dynamic stress test working condition dst (dynamic stress test), and the charge and discharge current of a single period is shown in fig. 4. After the simulation condition experiment is completed, the SOC is estimated by using an extended Kalman filtering method and the adaptive dual extended Kalman filtering algorithm provided by the invention respectively, and the obtained SOC estimation result and error are shown in FIG. 5.

Claims (10)

1. A lithium battery SOC estimation method based on a self-adaptive double-extended Kalman filtering method is characterized by comprising the following steps:
step 1, establishing a second-order RC equivalent circuit model of a lithium battery;
step 2, determining open-circuit voltages and battery equivalent model parameters at different SOC positions of the lithium battery through a pulse charge-discharge experiment, then obtaining a specific function relation between the open-circuit voltages and the SOC through function fitting, and constructing relations between other model parameters and the different SOC positions, wherein the other model parameters comprise ohmic internal resistance, electrochemical polarization capacitance, concentration difference polarization resistance and concentration difference polarization capacitance values;
step 3, respectively establishing a state space equation with SOC and polarization voltage as state variables and a state space equation with ohmic internal resistance as the state variables according to an equivalent circuit model of the lithium battery;
and 4, estimating the SOC of the battery by using a self-adaptive extended Kalman filtering algorithm, estimating the ohmic internal resistance of the battery by using the Kalman filtering algorithm, and finally performing iterative computation to obtain the SOC value of the lithium battery in real time.
2. The SOC estimation method based on the adaptive bi-extended Kalman filtering method according to claim 1, characterized in that the second-order RC equivalent circuit model of the lithium battery in the step 1 comprises two RC parallel circuits connected in series, wherein the electrochemical polarization resistance R of one RC parallel circuit isLAnd electrochemical polarization capacitance CLThe concentration difference polarization resistor R of the other RC parallel circuit is connected with an open-circuit voltage source in the charging and discharging directions and then connected with voltageSAnd concentration difference polarization capacitance CSCommon series ohmic internal resistance RoAnd then voltage is switched on.
3. The SOC estimation method based on adaptive double-extended Kalman filtering method according to claim 2, characterized in that the open-circuit voltage is divided into two parts in step 2 according to charging and discharging directionsWhen in the discharge state, the discharge open-circuit voltage UOCAnd the diodes connected in series work to charge an open-circuit voltage U'OCNot working; when in a charging state, charging an open-circuit voltage U'OCAnd the diode connected in series works, discharges open circuit voltage UOCThe lithium battery charging or discharging circuit does not work, and in the two processes of charging or discharging the lithium battery, each process selects a respective open-circuit voltage source U due to different directions of charging or discharging currentOCOr U'OCTherefore, the relation between the open-circuit voltage and the SOC in the charging and discharging processes and the corresponding relation between the ohmic internal resistance, the electrochemical polarization capacitance, the concentration difference polarization resistance and the concentration difference polarization capacitance value and the SOC in the discharging process are respectively obtained.
4. The SOC estimation method based on the adaptive dual-extended Kalman filtering method according to claim 3, characterized in that the established SOC and electrochemical polarization voltage U of the lithium battery with SOC and polarization voltage as state variablesLPolarization voltage U different from concentrationSThe state space equation of (a) is:
Figure FDA0002285343910000021
the equation for the terminal voltage of the lithium battery is as follows:
U(k)＝UOC(SOCk)-US(k)-UL(k)-Ro(k)i(k)+vk
where Δ T is the sampling time, RoIs the ohmic internal resistance, R, of the batteryL、CLElectrochemical polarization resistance and polarization capacitance, R, of the cellS、CSConcentration difference polarization resistance and polarization capacitance of the battery, tauL、τSRespectively represents electrochemical polarization time constant and concentration difference polarization time constant, whereinL＝RLCL，τS＝RSCS，USIs RSVoltage across, ULIs RLVoltage across, UOCIs the open circuit voltage of the battery iIs the operating current of the battery, U is the operating voltage of the battery, wkFor SOC estimation process noise, vkMeasurement noise for SOC estimation, CNRepresenting the current capacity of the lithium battery, k representing the iterative computation step number at the current moment, and SOC (k) representing the SOC value in the k-th computation;
the state space equation which is established in the step 3 and takes the ohmic internal resistance as the state variable is as follows:
Ro,k＝Ro,k-1+rk
U(k)＝UOC(SOCk)-US(k)-UL(k)-Ro(k)i(k)+qk
wherein r iskTo obtain ohmic internal resistance RoEstimation of process noise, q, for state variables by Kalman filteringkTo obtain ohmic internal resistance RoEstimation of measurement noise, R, for state variables by means of Kalman filteringo,kFor the ohmic internal resistance estimated value in the k iterative calculation, Ro,k-1The ohmic internal resistance estimation value of the previous time, namely the k-1 time.
5. The SOC estimation method based on the adaptive dual-extended Kalman filtering method according to claim 4, characterized in that the step 4 is implemented specifically according to the following steps:
step 4.1, setting initial values of state error covariance P in state of charge (SOC) and Kalman filtering algorithm of lithium battery, and selecting system noise value as 10-4Obtaining an initial value of the ohmic internal resistance according to the initial value of the SOC and the corresponding relation between the other model parameters and the SOC in the step 2;
step 4.2, obtaining the electrochemical polarization internal resistance R in the equivalent circuit model according to the estimation result of the SOC at the moment and by combining the corresponding relation between other model parameters and the SOC in the step 2LAnd a capacitor CLConcentration difference polarization resistance RSAnd a capacitor CSA value of (d); replacing the SOC value which is not in the corresponding relation table by a parameter value corresponding to the SOC close to the left side;
step 4.3, calculating the state of charge SOC and the ohmic internal resistance RoRespective state equations, respectivelyCalculating a state prediction value and an error covariance prediction value of the SOC and the ohmic internal resistance;
4.4, calculating Kalman filtering gain P of the SOC and the ohmic internal resistance;
step 4.5, substituting the state predicted values of the SOC and the ohmic internal resistance into an observation equation to obtain a predicted value of the observed quantity;
step 4.6, calculating the system measurement innovation;
step 4.7, obtaining the state estimation quantity of the current moment by the Kalman filtering gains of the measurement information, the state of charge (SOC) and the ohmic internal resistance, and updating the error covariance;
step 4.8, adjusting the process noise covariance of the SOC estimation system;
and 4.9, substituting the state quantity and the error covariance obtained in the step 4.3, the gain P obtained in the step 4.4, the observation quantity predicted value obtained in the step 4.5 and the innovation obtained in the step 4.6 into the step 4.2 to obtain a value of the SOC of the lithium battery, and starting a new round of cycle iteration.
6. The SOC estimation method based on the adaptive dual-extended Kalman filtering method according to claim 5, characterized in that the step 4.3 is specifically as follows:
the state prediction value formula is calculated as follows:
Figure FDA0002285343910000041
wherein the content of the first and second substances,
Figure FDA0002285343910000042
is a predicted value of the state at the current moment,
Figure FDA0002285343910000043
is the state quantity of the previous moment, ukF is an input variable at the current moment, f is a state prediction function, and k represents the iterative computation step number at the current moment;
the error covariance predictor is calculated as:
Pk/k-1＝Fk-1Pk-1Fk-1 T+Qk
wherein, Pk/k-1For the state prediction value at the current time, Pk-1Is a state quantity of the previous moment, QkIs the process noise covariance, F, at the current timek-1By state prediction function
Figure FDA0002285343910000044
And solving a Jacobian matrix for the state predicted value at the previous moment to obtain the state predicted value.
7. The SOC estimation method based on the adaptive dual-extended Kalman filtering method according to claim 6, wherein the step 4.4 is as follows:
calculating the Kalman filtering gain as follows:
Kk＝Pk/k-1Gk T(GkPk/k-1Gk T+R)-1
wherein, Pk/k-1For the state prediction value at the present time, KkFor the Kalman filter gain at the current time, R is the measurement noise covariance, GkAnd solving the Jacobian matrix of the state predicted value at the current moment by the observation function.
8. The SOC estimation method based on the adaptive dual-extended Kalman filtering method according to claim 7, wherein the step 4.6 is as follows:
the computing system measures innovation:
υj＝yj-yj/j-1
wherein upsilon isjRepresents the system measurement information, yjIndicating the system measurements at a previous time, yj/j-1The predicted value of the system measurement at a certain previous moment is shown, and j represents the iterative computation step number at a certain historical moment.
9. The SOC estimation method based on the adaptive dual-extended Kalman filtering method according to claim 8, wherein the step 4.7 is as follows:
calculating the state estimator at the current moment:
Figure FDA0002285343910000051
wherein, ykG is an observation function;
updating the error covariance:
Pk＝(E-KkGk)Pk/k-1
wherein E represents an identity matrix, Pk/k-1For the state prediction value at the present time, KkFor the Kalman filter gain at the present moment, GkAnd solving the Jacobian matrix of the state predicted value at the current moment by the observation function.
10. The SOC estimation method according to claim 9, wherein the step 4.8 is as follows:
adjusting the process noise covariance of the state of charge SOC:
Figure FDA0002285343910000052
Qk＝KkSkKk T
wherein m represents the window width of the statistical measurement information history information, SkMean value, u, representing the first m measured valuesjIndicating system measurement information, KkFor the Kalman filter gain, Q, of the current timekAnd k represents the iterative computation step number at the current moment, and j represents the computation step number at a certain historical moment.
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Cited By (27)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110780204A (en) * 2019-11-11 2020-02-11 北京理工大学 SOC estimation method for battery pack of electric vehicle
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CN111948560A (en) * 2020-07-30 2020-11-17 西安工程大学 Lithium battery health state estimation method based on multi-factor evaluation model
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CN111999654A (en) * 2020-08-04 2020-11-27 力高(山东)新能源技术有限公司 Adaptive extended Kalman estimation SOC algorithm
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CN112213644A (en) * 2020-09-30 2021-01-12 蜂巢能源科技有限公司 Battery state of charge estimation method and battery management system
CN112578286A (en) * 2020-11-23 2021-03-30 经纬恒润(天津)研究开发有限公司 Battery SOC estimation method and device
CN112611972A (en) * 2020-11-30 2021-04-06 上海理工大学 Method for estimating SOC (state of charge) of lithium battery under condition of low-frequency sampling data
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CN113156321A (en) * 2021-04-26 2021-07-23 中国矿业大学 Estimation method for state of charge (SOC) of lithium ion battery
CN113219344A (en) * 2021-03-17 2021-08-06 国家计算机网络与信息安全管理中心 Lead-acid storage battery SOC estimation method
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CN113447821A (en) * 2021-06-30 2021-09-28 国网北京市电力公司 Method for estimating state of charge of battery
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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107219466A (en) * 2017-06-12 2017-09-29 福建工程学院 A kind of lithium battery SOC estimation method for mixing EKF
US20180246173A1 (en) * 2017-02-28 2018-08-30 Honeywell International Inc. Online determination of model parameters of lead acid batteries and computation of soc and soh
CN108594135A (en) * 2018-06-28 2018-09-28 南京理工大学 A kind of SOC estimation method for the control of lithium battery balance charge/discharge
CN109061496A (en) * 2018-08-10 2018-12-21 安徽力高新能源技术有限公司 A method of lithium battery SOC is estimated using expanded Kalman filtration algorithm
CN110058160A (en) * 2019-04-29 2019-07-26 西安理工大学 The prediction technique of lithium battery health status based on SREKF
CN110395141A (en) * 2019-06-27 2019-11-01 武汉理工大学 Dynamic lithium battery SOC estimation method based on adaptive Kalman filter method

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20180246173A1 (en) * 2017-02-28 2018-08-30 Honeywell International Inc. Online determination of model parameters of lead acid batteries and computation of soc and soh
CN107219466A (en) * 2017-06-12 2017-09-29 福建工程学院 A kind of lithium battery SOC estimation method for mixing EKF
CN108594135A (en) * 2018-06-28 2018-09-28 南京理工大学 A kind of SOC estimation method for the control of lithium battery balance charge/discharge
CN109061496A (en) * 2018-08-10 2018-12-21 安徽力高新能源技术有限公司 A method of lithium battery SOC is estimated using expanded Kalman filtration algorithm
CN110058160A (en) * 2019-04-29 2019-07-26 西安理工大学 The prediction technique of lithium battery health status based on SREKF
CN110395141A (en) * 2019-06-27 2019-11-01 武汉理工大学 Dynamic lithium battery SOC estimation method based on adaptive Kalman filter method

Non-Patent Citations (6)

* Cited by examiner, † Cited by third party
Title
XIN LEI LIU ETC.: "SOC calculation method based on extended Kalman filter of power battery for electric vehicle", 《2017 12TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS AND KNOWLEDGE ENGINEERING (ISKE)》 *
刘国繁等: "一种MH/Ni动力电池模型及其SOC预测方法", 《电源技术》 *
华显等: "基于双扩展卡尔曼滤波的电池荷电状态估计", 《测控技术》 *
李华等: "基于自适应卡尔曼滤波器的锂电池SOC估计策略", 《太原科技大学学报》 *
颜湘武等: "基于自适应无迹卡尔曼滤波的动力电池健康状态检测及梯次利用研究", 《电工技术学报》 *
齐慧颖: "《医学信息资源智能管理》", 31 July 2019 *

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