CN112731157A - Lithium ion battery capacity estimation method based on data driving - Google Patents
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- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 title claims abstract description 17
- 229910001416 lithium ion Inorganic materials 0.000 title claims abstract description 17
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 35
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- 230000004927 fusion Effects 0.000 claims abstract description 6
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- 241000219496 Alnus Species 0.000 description 2
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- 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]
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- G—PHYSICS
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- 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
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Abstract
The invention discloses a lithium ion battery capacity estimation method based on data driving, which comprises the following steps: the capacity estimation results of the three-order Kalman filtering algorithm and the discrete life model are fused through a double Kalman filtering algorithm, and the method comprises the following steps: s1, constructing a three-order Kalman filtering algorithm containing capacity based on a first-order RC equivalent circuit model, and driving capacity estimation by using dynamic working condition data of different aging stages; s2, aiming at the problem that model parameters of an Arrhenius model are mismatched in real vehicle application, a DEKF algorithm is designed by combining three-order EKF estimation results, and optimal estimation of the model parameters and fusion estimation of capacity are achieved. According to the method, the battery capacity can be estimated on line, a large amount of battery capacity attenuation data is not needed to train a model, and the mathematical algorithm is simple.
Description
Technical Field
The invention relates to the technical field of lithium ion battery capacity estimation, in particular to a lithium ion battery capacity estimation method based on data driving.
Background
With the petroleum crisis and the attention of people to environmental protection, new energy automobiles are more and more favored by consumers. Lithium ion batteries have become the main choice of current vehicle-mounted power batteries due to their outstanding comprehensive properties in energy, efficiency, life, environmental protection, safety, and other aspects. However, the lithium ion battery will age and have capacity attenuation along with use and storage, which will directly affect the driving range of the electric vehicle. How to accurately estimate the capacity of the battery in the battery pack and predict the life of the battery becomes a new challenge for the Battery Management System (BMS) of today. At present, methods for estimating the battery capacity at home and abroad mainly comprise an empirical model method and a data driving method. The empirical model method is mainly used for realizing the estimation of the battery capacity according to parameters such as working temperature, charging and discharging multiplying power, cycle times, accumulated charging and discharging amount and the like. However, the method needs a large amount of offline calibration data to fit parameters, and the model is open-loop and is difficult to adapt to the conditions of variable working conditions and the inconsistency decline of different electric bodies in the battery pack; the data driving method mainly utilizes a large amount of capacity attenuation data to train a model and predict the future trend of the battery capacity attenuation through mathematical methods such as wavelet analysis, particle filters, machine learning and the like without knowing the attenuation mechanism and the aging path of the battery. However, the method also requires a large amount of battery capacity attenuation data to train the model, and the applied mathematical algorithm is complex in calculation, so that the battery capacity cannot be estimated online at present.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a lithium ion battery capacity estimation method based on data driving, which can estimate the battery capacity on line, does not need a large amount of battery capacity attenuation data to train a model, and is simple in mathematical algorithm. To achieve the above objects and other advantages in accordance with the present invention, there is provided a data-driven lithium ion battery capacity estimation method, comprising:
s1, establishing a first-order RC equivalent circuit model, and identifying parameters of the fresh battery in different regions by using dynamic working condition data;
s2, constructing a third-order Kalman filtering algorithm containing capacity, and driving capacity estimation by using dynamic working condition data;
s3, designing a double Kalman filtering algorithm by combining the capacity estimation result of the third order Kalman filtering algorithm;
s4, the double-Kalman filtering algorithm is used for realizing the optimized estimation of the parameters of the Allen-ius life model;
and S5, inputting the optimized and estimated model parameters into the third-order Kalman filtering algorithm to realize the fusion estimation of the capacity.
Preferably, in step S2, the state space variables of the third-order kalman filter algorithm are as follows:
X＝[SOC U 1 1/Cr]T
in the formula: SOC is the state of charge of the battery, U1Is terminal voltage of RC circuit, CrIs the battery capacity to be estimated.
Preferably, in step S4, the arrhenius life model is as follows:
in the formula: xi (n) is the relative capacity decrement of the battery after n times of circulation, and the unit percent; a is a constant greater than zero; eaFor activation energy, the unit is J/mol; r is a gas constant and has the unit of J/(mol.k); t is the absolute temperature in K; n is the cycle number; z is an index. Actual model parameters A, Ea/R, z generalAnd fitting the real capacity results of the battery in different aging stages.
Preferably, the arrhenius life model is deformed to establish a discrete life model:
in the formula, k1，k2，k3Respectively as follows:
preferably, the discrete lifetime model parameters A, E are useda/R, z as state space variables:
in the formula: x is the number ofkIs the state variable of the model parameter at time k.
Preferably, the step S2 further includes the steps of:
s21, k time model parameter xkInput into discrete life model to predict battery cycle nk+1Capacity C after the next timeA(xk,nk+1) Third order EKF capacity estimate C at time k +1r(nk+1) Comparing, calibrating and updating to x by using a Kalman filterk+1Realizing the optimized estimation of the model parameters;
s22, updating the model parameter xk+1Inputting the data into the discrete life model again to obtain an estimation result CA(xk+1,nk+1) And then with the estimated value C at the time k +1r(nk+1) Fusing by using another Kalman filter to obtain an estimation result CD(nk+1) And realizing the fused estimation of the capacity.
Preferably, the two independent kalman filters used in steps S21 and S22 estimate the model parameters and the battery capacity, respectively.
Compared with the prior art, the invention has the beneficial effects that: the advantages of the data-driven method and the empirical model method in the estimation of the battery capacity are effectively fused. Based on a first-order RC equivalent circuit model, identifying model parameters (R) required by each section of the fresh battery according to SOC sections0、τ1And the like), a third-order Kalman filtering equation containing the capacity is constructed, dynamic working condition data is more during real vehicle operation, more opportunities are provided for driving battery capacity estimation, and capacity online estimation at different aging stages is realized. Considering that the actual use process does not necessarily accord with the requirement condition of the estimation method, and the method can only intermittently obtain the capacity estimation result, the Alnus life model is further adopted for real-time capacity estimation, but the parameters of the Alnus life model possibly have the mismatch problem, so that the real-time update of the model parameters and the online estimation of the battery capacity are realized by designing the double-Kalman filtering algorithm.
The double Kalman filtering estimation algorithm has the advantages that: 1) dynamic working condition data of the electric automobile in the daily use process are more, the battery capacity estimation process can be driven more frequently, and then model parameters can be corrected to a certain extent in time; 2) identifying the capacities of different monomers in the battery pack to enable the corrected model to have different parameters for different monomers, so that the model is adaptive to each battery monomer, and the capacity of each monomer is accurately estimated; 3) due to the fact that the discrete Arrhenius life model with parameter updating is used, accurate capacity continuity estimation can be achieved.
Drawings
Fig. 1 is a block flow diagram of a data-driven-based lithium ion battery capacity estimation method according to the present invention;
FIG. 2 is a first-order RC equivalent circuit diagram of the data-driven-based lithium ion battery capacity estimation method according to the present invention;
FIG. 3 is a schematic diagram of capacity estimation by a third-order EKF algorithm based on the data-driven lithium ion battery capacity estimation method according to the present invention;
fig. 4 is a schematic diagram of the capacity estimation by the DEKF algorithm based on the data-driven lithium ion battery capacity estimation method according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1 to 4, a method for estimating capacity of a lithium ion battery based on data driving includes the steps of: s1, establishing a first-order RC equivalent circuit model, and identifying parameters of the fresh battery in different regions by using dynamic working condition data;
s2, constructing a third-order Kalman filtering algorithm containing capacity, and driving capacity estimation by using dynamic working condition data;
s3, designing a double Kalman filtering algorithm by combining the capacity estimation result of the third order Kalman filtering algorithm;
s4, the double-Kalman filtering algorithm is used for realizing the optimized estimation of the parameters of the Allen-ius life model;
and S5, inputting the optimized and estimated model parameters into the third-order Kalman filtering algorithm to realize the fusion estimation of the capacity.
Further, in step S2, the state space variables of the third-order kalman filter algorithm are as follows:
in the formula: SOCKIs the state of charge at time K; u shape1,KTerminal voltage of the RC loop at the moment K; crAnd (n) is the capacity value to be estimated after the battery is cycled for n times.
Then, establishing a discrete space state equation and an observation equation of a third-order Kalman filtering algorithm:
wherein,
in the formula: Δ t is the time interval; η is coulombic efficiency; tau is1，KAnd R1,KThe parameter value of the first-order RC loop at the K moment is obtained; i isKThe current value of the dynamic working condition data at the K moment is defined as positive discharge and negative charge; OCV is open circuit voltage; y isKThe voltage value of the dynamic working condition data at the K moment is obtained; mKAnd NKRepresenting the system noise and the observed noise, respectively.
Thus, dynamic working condition data under a certain aging degree can be used for driving a three-order EKF algorithm to estimate the aged battery capacity, as shown in FIG. 3. It can be seen that the designed three-order EKF algorithm utilizes dynamic condition data to drive capacity estimation, so that a capacity estimation result gradually approaches to a capacity true value. Here, the average of the latter stable estimates is taken as the third order EKF estimate for this aging stage.
Further, in step S4, for the problem of mismatch of model parameters in the real-vehicle application of the arrhenius model, the arrhenius life model is as follows:
in the formula: xi (n) is the battery cycleRelative capacity delta after n times, unit%; a is a constant greater than zero; eaFor activation energy, the unit is J/mol; r is a gas constant and has the unit of J/(mol.k); t is the absolute temperature in K; n is the cycle number; z is an index. Actual model parameters A, Eathe/R, z is obtained by matching the capacity real results of different aging stages of the battery.
Further, the arrhenius life model is deformed, and a discrete life model is established:
in the formula, k1，k2，k3Respectively as follows:
further, the discrete lifetime model parameters A, E are useda/R, z as state space variables:
in the formula: x is the number ofkIs the state variable of the model parameter at time k.
Further, the step S2 further includes the following steps:
s21, k time model parameter xkInput into discrete life model to predict battery cycle nk+1Capacity C after the next timeA(xk,nk+1) Third order EKF capacity estimate C at time k +1r(nk+1) Comparing, calibrating and updating to x by using a Kalman filter k+1, realizing the optimized estimation of model parameters;
here, discrete spatial state equations and observation equations of model parameters are constructed:
in the formula: y isk+1As observed at time k + 1, i.e. Cr(nk+1)；g(xk,nk+1) For the measurement function, the Arrhenius capacity-attenuation model is expressed at parameter xkIn the next cycle nk+1The result of the estimation of time, i.e. CA(xk,nk+1)；WkAnd VkRespectively representing system noise and observation noise; f is a state transition matrix.
S22, updating the model parameter xk+1Inputting the data into the discrete life model again to obtain an estimation result CA(xk+1,nk+1) And then with the estimated value C at the time k +1r(nk+1) Fusing by using another Kalman filter to obtain an estimation result CD(nk+1) And realizing the fused estimation of the capacity.
Here, discrete spatial state equations and observation equations of battery capacity are established:
in the formula: lk+1Is the optimal solution of the system state at the time of k +1, namely CD(nk+1)；g(xk+1,nk+1) For the transfer function, the life model is expressed at the parameter xk+1Under, cell cycle nk+1Capacity estimation of time, i.e. CA(xk+1,nk+1)；uk+1Is the observed value at time k + 1, i.e. Cr(nk+1) (ii) a H is a measurement system parameter; w is akAnd vkRepresenting the system noise and the observed noise, respectively.
FIG. 4 is a diagram illustrating the estimated capacity of the DEKF algorithm. The discrete life model estimation value represents that the model parameters after each update are input into the life model and are represented in a capacity mode; the DEKF estimated value is a fusion result of a discrete life model estimated value and a third-order EKF estimated value at a corresponding position; the five-pointed star discrete point is a standard capacity test experiment result; the actual model parameters are real model parameters fitted based on experimental values, and are also expressed in the form of capacity, which represents the actual capacity fading result after each cycle of the battery. It can be seen that the designed DEKF algorithm can implement two functions: 1) optimizing and estimating model parameters; 2) and (4) fusion estimation of capacity. Therefore, the capacity estimation result can better approximate to the real attenuation track of the battery.
Further, the two independent kalman filters used in steps S21 and S22 estimate the model parameters and the battery capacity, respectively.
The third-order EKF algorithm in the embodiment of the invention is based on a first-order RC equivalent circuit model, is not limited to the adoption of the first-order RC equivalent circuit model in practical specific use, and is suitable for the n (n > -1) order RC equivalent circuit model.
The number of devices and the scale of the processes described herein are intended to simplify the description of the invention, and applications, modifications and variations of the invention will be apparent to those skilled in the art.
While embodiments of the invention have been described above, it is not limited to the applications set forth in the description and the embodiments, which are fully applicable in various fields of endeavor to which the invention pertains, and further modifications may readily be made by those skilled in the art, it being understood that the invention is not limited to the details shown and described herein without departing from the general concept defined by the appended claims and their equivalents.
Claims (7)
1. A lithium ion battery capacity estimation method based on data driving is characterized by comprising the following steps:
s1, establishing a first-order RC equivalent circuit model, and identifying parameters of the fresh battery in different regions by using dynamic working condition data;
s2, constructing a third-order Kalman filtering algorithm containing capacity, and driving capacity estimation by using dynamic working condition data;
s3, designing a double Kalman filtering algorithm by combining the capacity estimation result of the third order Kalman filtering algorithm;
s4, the double-Kalman filtering algorithm is used for realizing the optimized estimation of the parameters of the Allen-ius life model;
and S5, inputting the optimized and estimated model parameters into the third-order Kalman filtering algorithm to realize the fusion estimation of the capacity.
2. The method according to claim 1, wherein in step S2, the state space variables of the third-order kalman filter algorithm are as follows:
X＝[SOC U1 1/Cr]T
in the formula: SOC is the state of charge of the battery, U1Is terminal voltage of RC circuit, CrIs the battery capacity to be estimated.
3. The method according to claim 1, wherein in step S4, the arrhenius lifetime model is as follows:
in the formula: xi (n) is the relative capacity decrement of the battery after n times of circulation, and the unit percent; a is a constant greater than zero; eaFor activation energy, the unit is J/mol; r is a gas constant and has the unit of J/(mol.k); t is the absolute temperature in K; n is the cycle number; z is an index. Actual model parameters A, Eathe/R, z is obtained by matching the capacity real results of different aging stages of the battery.
4. The method according to claim 3, wherein the Arrhenius life model is deformed to establish a discrete life model:
in the formula, k1，k2，k3Respectively as follows:
5. the method of claim 4, wherein the discrete life model parameters A, E are used for estimating the capacity of the lithium ion battery based on data drivinga/R, z as state space variables:
in the formula: x is the number ofkIs the state variable of the model parameter at time k.
6. The method for estimating capacity of lithium ion battery based on data driving according to any of claims 1 or 5, wherein the step S2 further comprises the steps of:
s21, k time model parameter xkInput into discrete life model to predict battery cycle nk+1Capacity C after the next timeA(xk,nk+1) Third order EKF capacity estimate C at time k +1r(nk+1) Comparing, calibrating and updating to x by using a Kalman filterk+1Realizing the optimized estimation of the model parameters;
s22, updating the model parameter xk+1Inputting the data into the discrete life model again to obtain an estimation result CA(xk+1,nk+1) And then with the estimated value C at the time k +1r(nk+1) Fusing by using another Kalman filter to obtain an estimation result CD(nk+1) And realizing the fused estimation of the capacity.
7. The method of claim 6, wherein the step S21 and the step S22 use two independent Kalman filters to estimate the model parameters and the battery capacity, respectively.
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