CN106443473B - SOC estimation method for power lithium ion battery pack - Google Patents
SOC estimation method for power lithium ion battery pack Download PDFInfo
- Publication number
- CN106443473B CN106443473B CN201610877189.1A CN201610877189A CN106443473B CN 106443473 B CN106443473 B CN 106443473B CN 201610877189 A CN201610877189 A CN 201610877189A CN 106443473 B CN106443473 B CN 106443473B
- Authority
- CN
- China
- Prior art keywords
- estimation
- soc
- lithium ion
- ion battery
- battery
- 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.)
- Expired - Fee Related
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/3644—Constructional arrangements
- G01R31/3648—Constructional arrangements comprising digital calculation means, e.g. for performing an algorithm
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Secondary Cells (AREA)
Abstract
The invention relates to a power lithium ion battery pack SOC estimation method, and belongs to the field of new energy measurement and control. Aiming at the power working characteristics of the lithium ion battery, the method accurately represents the working process of the battery by constructing a specific-Equivalent Circuit Model (S-ECM); the accurate description of the self-discharge effect is realized by adding parallel resistors at two ends of an ideal voltage source; a resistor parallel circuit connected with a reverse diode in series is introduced to solve the expression problem of charge-discharge internal resistance difference; the parallel capacitance is increased to improve the description of the surface effect. The method integrates an S-ECM state space equation by providing a simplified Particle-Unscented Kalman Filter (RP-UKF) algorithm so as to improve the calculation efficiency; the front end is integrated with simplified particle filter transformation, the estimation offset problem is solved by optimizing a nonlinear processing process, and the estimation precision is further improved; on the basis of the feedback correction of the terminal voltage, the rear end introduces the influence of the State of Balance (SOB) among the monomers, and realizes the online estimation of the whole set of SOC values.
Description
Technical Field
The invention relates to a SOC (state of charge) estimation method of a power lithium ion battery pack, which is characterized in that an S-ECM (specific-electronic control module) Model is improved on the basis of the existing battery Equivalent Model, the self-discharge effect is represented by increasing parallel internal resistance, the difference of charge and discharge internal resistance is represented by series-connected parallel resistance of a backward diode, and the surface effect is represented by parallel capacitance, so that the accurate description of the working process of the power lithium ion battery is realized. The method provides an RP-UKF (Reduced Particle-unknown Kalman Filter) estimation model by improving a Kalman estimation process, integrates a state space equation of an improved battery equivalent model to improve the calculation efficiency, optimizes a linearization processing process by simplifying Particle filtering to eliminate estimation offset, and improves the grouped SOC estimation precision by feedback correction of a state of equilibrium (SOB) (State of balance). The method is an online estimation method of the SOC value of the power lithium ion battery pack based on a modern control theory, and belongs to the field of new energy measurement and control.
Background
The lithium ion battery has the advantages of high working voltage, high energy density, large capacity, small self-discharge rate and the like, and is increasingly applied to the field of power energy supply. However, safety concerns for lithium ion battery powered battery pack applications are of great concern, where unreasonable energy management will directly impact its capacity usage efficiency and life, even causing serious accidents. In the whole life cycle of the lithium ion battery pack, the core parameter SOC control in the matched BMS equipment influences the power supply effect, so that it is very necessary to estimate the SOC value in real time and evaluate the working performance of the whole lithium ion battery pack. The SOC value of the state parameter is an important factor of a high-power energy storage and supply system based on a lithium ion battery pack, and the online estimation of the SOC value is an indispensable part in the energy management of a matched BMS (battery management system) no matter in various energy supply power applications.
Lithium ion is the leading battery technology at present, requiring reliable matched BMS devices due to its complex reactions, where estimation of SOC is crucial. Due to the necessity and urgent need for reliable SOC estimation, a great deal of research work has been done by related researchers in recent years around the SOC estimation problem in lithium ion battery pack applications, effectively improving the safety and energy utilization efficiency in the use process thereof. Since the SOC is an internal state parameter of the battery, it cannot be obtained by direct measurement, and only indirectly estimates the SOC by measuring parameters such as voltage, current, and temperature. At present, relevant researchers at home and abroad make certain research progress in the aspect of battery SOC estimation, and provide an Ampere-hour integral (Ah) method, an Open Circuit Voltage (OCV) method, Kalman Filtering (KF) and an extended algorithm thereof, Particle Filtering (PF) and Neural Network (NN) estimation methods. Because of the influence of various factors such as charging and discharging current, temperature, internal resistance, self-discharge, aging and the like, a method with high accuracy for realizing SOC online estimation is not available, and an effective systematic method for SOC estimation is still lacked for the power lithium ion battery pack.
An online SOC estimation model is constructed, a multivariate parameter estimation theory based on battery equivalent simulation is used as an important way for accurately estimating the SOC of the lithium ion battery pack, an optimal balance point is sought between the improvement of precision and the reduction of calculated amount, and optimization and improvement are carried out continuously. By inquiring the invention patents related to the project in the national intellectual property office library, the existing patents only apply for the SOC estimation of the lithium ion battery monomer, and no report is found about the SOC estimation of the power grouping of the lithium ion battery. By consulting the relevant national standards, a definite systematic estimation method and a comprehensive and effective solution are not provided for the SOC estimation problem of the lithium ion battery pack.
Disclosure of Invention
The invention aims to overcome the defects of the existing method, provides an SOC estimation method of a power lithium ion battery pack, and solves the problem of accurate estimation of SOC of power grouping application of lithium ion batteries.
The method mainly obtains the change rule of key factors such as the open-circuit voltage, the temperature and the working current of the battery through a working characteristic experiment, improves the existing battery equivalent model by using the modern control theory idea and utilizing the equivalent simulation of electronic components of the battery characteristic, and provides an S-ECM model by adding a resistance and capacitance enhancement characteristic description mode to realize the accurate model expression of the power lithium ion battery characteristic; an RP-UKF estimation model is built, a Kalman estimation process is improved, a simplified particle processing optimization linearization processing mechanism is utilized in combination with S-ECM state space description, and working voltage feedback and equilibrium state SOB calculation are combined, so that the influence of imbalance among monomers on estimation is solved, and the online accurate estimation of the SOC of the power lithium ion battery pack is realized.
The SOC online estimation method of the power lithium ion battery pack is realized based on a mode of combining an equivalent circuit model and a Kalman filtering algorithm, and has strong environmental applicability. Aiming at the working characteristics of the power lithium ion battery, the invention constructs a targeted equivalent circuit model S-ECM to realize accurate expression of the working process of the battery. The electromotive force in the battery equivalent model S-ECM comes from an ideal voltage source UOCTwo ends of the resistor are added with a parallel large resistor RSTo characterize the self-discharge effect by series internal resistance RΩCharacterizing ohmic effect, using a first-order RC parallel circuit to characterize polarization effect, and increasing parallel capacitance CeTo describe surface effects, to improve the increase of the resistance R of a series of diodes in reverse directiondAnd RcThe parallel circuit represents the difference of charge and discharge internal resistances, so that the accuracy of working state description is further improved; the method constructs a simplified particle unscented Kalman filter RP-UKF to perform SOC estimation recursive operation of the power lithium ion battery pack, and realizes comprehensive solving of a grouped SOC value under the influence of an inter-cell equilibrium state SOB. Aiming at the characteristics of dynamic working conditions, the RP-UKF estimation model improves a linearization processing mechanism and solves the estimation offset problem caused by the loss of Taylor series expansion high-order terms through a front-end fusion simplified particle transformation process on the basis of a Kalman estimation algorithm; aiming at the problem of inconsistency among grouped working monomers, the rear end of the RP-UKF estimation model disclosed by the invention is integrated with the influence of the state of equilibrium (SOB) among the monomers, the comprehensive SOC value of the power grouping work of the lithium ion battery is comprehensively obtained, and the estimation precision is further improved. The method can accurately estimate the SOC value of the power lithium ion battery pack on line, and has the advantages of simple calculation, good working condition adaptability and high precision.
Drawings
FIG. 1 is a block diagram of SOC estimation incorporating the S-ECM model and RP-UKF algorithm of the present invention;
FIG. 2 is a schematic diagram of an embodiment of an SOC estimation model of a power lithium ion battery pack of the present invention;
FIG. 3 is a graph showing the relationship between OCV and SOC of the lithium ion battery according to the present invention;
FIG. 4 is a graph of discharge voltage characteristics for different rates in accordance with the present invention;
fig. 5 is a graph showing discharge capacity variation characteristics at different temperatures according to the present invention.
Detailed Description
The SOC estimation method of the power lithium ion battery pack combining the S-ECM model and the RP-UKF algorithm is further described in detail in the following with the attached drawings. Aiming at the problem of SOC estimation when the power of the lithium ion battery is applied in a grouping mode, the SOC estimation method utilizes S-ECM to simulate the working process of the battery and construct a state space equation, realizes SOC estimation of the power lithium ion battery pack through RP-UKF, utilizes the feedback of equilibrium state SOB to correct the estimation process, and constructs a scheme of an SOC estimation model system. In order to better embody the present invention, the present embodiment is described by taking an aviation lithium ion battery pack as an example, but it should be well known to those skilled in the art that various SOC estimation of the power lithium ion battery pack can be realized according to the technical idea of the present invention. The following describes the steps of implementing the SOC estimation method for the power lithium ion battery pack in detail.
Referring to fig. 1, the S-ECM model used in the method for estimating the SOC of the power lithium ion battery pack according to the present invention has: equivalent U of voltage sourceOCCharacterizing the open circuit voltage of the battery; megaohm-level large resistor RSCharacterizing the self-discharge effect of the battery; small resistance R in milliohm rangeΩCharacterizing the ohmic internal resistance of the battery; rPIs the cell polarization resistance, CPFor polarizing the capacitance of the battery, RPAnd CPThe parallel circuit of (a) reflects the generation and elimination of the cell polarization process; u shapeLThe terminal voltage after the battery is connected with an external circuit; parameter RdIs the discharge internal resistance during discharge, and represents the internal resistance difference of the lithium ion battery monomer during discharge, and the parameter RcIs the charging internal resistance during charging, and represents the internal resistance difference of the single lithium ion battery during discharging; ceIs the surface effect capacitance of the cell. Aiming at structural analysis of S-ECM circuit, applying a circuity analysis method, setting CePressure drop over is VCe，UOCIs an open circuit voltage, with U when the battery is openOC＝VCe＝ULAccording to the model and kirchhoff's law, the state equation of the battery at the time of discharge is shown as the following formula.
When the lithium ion battery is in a charging state, the representation relationship among the terminal voltages of the circuit components is shown as the following formula, and the structures of other equations are unchanged.
UL＝UOC(SOC)-i(t)RΩ-Up-i(t)Rd(2)
By using the obtained state space model of the charge and discharge process, a basic equation framework of the estimation process can be established, and the basic equation framework is combined with the modularized SOC estimation calculation processing process in the FIG. 1 for the subsequent SOC estimation research.
Referring to fig. 2, the RP-UKF algorithm used in the power lithium ion battery pack SOC estimation method of the present invention has the following sub-calculation processing modules, and the specific implementation steps are as follows. Firstly, in step 1, measuring working current and temperature signal as input of SOC estimation system, parameter I is working current, and 1C is adopted5Discharging by using the current A, wherein in the embodiment, for an aviation lithium ion battery pack with the rated capacity of 45Ah, the discharging current with the mean value of 45 and the variance of 1 and according with Gaussian distribution is selected; the parameter T represents the working temperature, and should consider the heating phenomenon and good heat dissipation condition of the power lithium ion battery pack during the charging and discharging processes, in this embodiment, a random temperature signal with a variance of 1 at 35 ℃ is selected as the mean value of the working temperature of the power lithium ion battery pack.
Setting state space equation coefficients, realizing integrated parameter matrix input by using a design module P as a parameter input matrix submodule, and realizing the obtaining process of each parameter in a polynomial curve fitting mode through an OCV-SOC relation curve in the figure 3, wherein a horizontal axis represents an SOC value, a vertical axis represents an OCV value, the obtaining of discrete points of the relation is realized through an intermittent discharge and resting mode, and the accurate relation between the OCV value and the OCV value is obtained through a curve fitting mode, the accurate initial parameter setting and correction of SOC estimation is realized through obtaining the relation between the OCV of the open-circuit voltage of the battery and the SOC of the battery, namely the OCV-SOC curve, and the relation between the open-circuit voltage and the SOC of the battery is inspected through the following mode that ① observes the relation between the open-circuit voltage and the SOC of the battery5Pre-discharging with current A to discharge cut-off voltage, in this example, the discharge cut-off voltage is 2.8V, ② is left for 1 hr, ③ is 0.2C5A constant current charging to the charge cut-off voltage, in this embodiment, the charge cut-off voltage is selected to be 4.15V, then constant voltage compensation charging is carried out until the current is reduced to the compensation current cut-off voltage, in this embodiment, the compensation current cut-off is selected to be 2.5A, ④ is kept stand for 1 hour, ⑤ is 1C5Discharging current A for 12min, standing for ⑥ hr, recording OCV value, jumping to ⑤ at ⑦, circulating for 5 times, and discharging at ⑧ and 0.2 deg.C5A is charged to 4.15V by constant current, and then constant voltage compensation charging is carried out until the current is reduced to 2.5A. The OCV-SOC curve obtained by the above procedure is shown in fig. 3.
And continuing to step 3, setting the rated capacity of the power lithium ion battery pack and realizing the correction of the coulombic efficiency of the working current, wherein the parameter Qn represents the rated capacity of the aviation lithium ion battery pack, in the embodiment, an experimental sample rated capacity is 45Ah, the module C _ E is a coulombic efficiency correction submodule, real-time working current parameters I and working temperature parameters T are input, output parameters η are the coulombic efficiency, in the processing process of the current I, different working current correction processing is carried out, in the embodiment, absolute values Abs (829) are firstly carried out on the working current, then according to equations Fcn1 (3.905 u (1) ((829) (1) -123.6 u (1) +15033)/14967, curves of the equations and recognition are obtained through the fitting of the discharge voltage characteristic curves with different multiplying ratios in fig. 4, in the processing process of the temperature T, different environmental temperature correction processing needs to be carried out due to the nonlinear characteristics of the height, in the embodiment, the relationship, the equation obtaining a discharge capacity characteristic curve fitting process (5391) through the equation 631-4831-0.00000003637 (5391), and the online monitoring and the change of the parameter found through the equation 637).
Continuing to step 4, carrying out correction processing on the SOC estimation process of the power lithium ion battery pack, wherein the module Meas is the correction process of the fusion observation equation, and parameters η and I, Q are usedn、E0、R、K1、K2、K3And K4The SOC estimation and the Error covariance Error _ Cov calculation are realized as input, an input signal I represents working current, a symbol η represents coulombic efficiency, and a parameter QnA rated capacity C is indicated, and a parameter Δ T indicates a sampling time interval, and in the present embodiment, the parameter Δ T is set to 0.001; a part of factors of the predicted state parameter X (k +1) is obtained by an equation Fcn1 ═ u (1) × u (4)/(u (3) × u (2)); the state variable X (k) at time k is obtained by the first-order lag process Unit Delay, and the predicted value X (k +1) of the state variable at time k +1 is obtained by the equation X (k +1) ═ X (k) -Fcn 1. At the same time, the processThe low-power stop is realized by monitoring the state value in real time and judging in real time, in this embodiment, the low-power judgment threshold is set to be 0.003, and a corresponding calculation formula is shown as the following formula for the discharge process.
By combining the state quantity parameter X (k) and the observation equation coefficient parameter E0、R、I、K1、K2、K3And K4As an input, the output voltage signal y (k) is calculated by using an equation Fcn2 ═ u (2) -u (3) × u (4) -u (5)/u (1) -u (6) × u (1) + u (7) × log (u (1)) + u (8) × log (1-u (1)), and by combining observation noise superposition, in the present embodiment, noise is selected as gaussian white noise having a mean value of 0 and a variance of 0.00005, and low voltage stop is realized by real-time judgment, in the present embodiment, a low voltage judgment threshold is set to 2.8V, and a corresponding calculation formula is shown as the following equation.
Y(k)＝E0-Ri-K1/X(k)-K2*X(k)+K3*log(X(k))+K4*log(1-X(k)) (4)
The prediction link of the model state and the signal tracking of the output voltage are realized through the steps.
Continuing to step 5, performing SOC estimation process recursion operation according to KF estimation principle, wherein the module Est is a KF-based SOC estimation process submodule obtained by processing parameters η, I, Y (k) and Qn、E0、R、K1、K2、K3And K4Using KF-based estimation process as input to realize SOC estimation and calculation of Error covariance parameter Error _ Cov, and inputting variables I, η and QnThe SOC variation Δ SOC in the charge/discharge process is obtained from equation Fcn1, i.e., u (1) × u (4)/(u (2) × u (3)), and the SOC value at the current time X (k | k-1) is predicted by superimposing the SOC value at the current time, and the calculation process is shown in the following equation.
By combining the variables X (k | k-1), E0、R、K1、K2、K3And K4As an input, an output voltage prediction value Y (k | k-1) is obtained according to an equation Fcn3 ═ u (2) -u (3) × u (4) -u (5)/u (1) -u (6) × u (1) + u (7) × log (u (1)) + u (8) × log (1-u (1)), and a corresponding calculation formula is shown as the following equation.
Y(k)＝E0-Ri-K1/Xk-K2*Xk+K3*log(Xk)+K4*log(1-Xk) (6)
By combining the variables X (K | K-1), K1、K2、K3And K4As an input, an observation matrix h (k) is obtained according to equation Fcn4 ═ u (2)/(u (1) × u (1)) -u (3) + u (4)/u (1) -u (5)/(1-u (1)), and the corresponding calculation formula is shown in the following equation.
The kalman gain matrix Kk is obtained by substituting the equation Fun5 ═ u (3) · (1)/(u (1) · u (3) · u (1) + u (2)) with the superimposed value P (k | k-1) of the first-order lag of the observation matrix h (k), the estimated process noise variance R, the observed process noise variance Q, and the estimated error covariance P (k | k), and the corresponding calculation formula is shown in the following equation.
The estimation and correction of the battery SOC are completed by inputting the variable output voltage Y (k), the battery SOC state predicted value X (k | k-1), the voltage predicted value Y (k | k-1), and the kalman gain k (k) into equation Fcn2 ═ u (2) + u (4) × (u (1) -u (3)), and the estimation and correction are performed by monitoring the SOC value in real time and performing forced stop when the SOC value is less than 0.003, and the corresponding calculation formula is shown in the following formula.
X(k|k)＝X(k|k-1)+K(k)*[Y(k)-Y(k|k-1)](9)
In the process of obtaining the Error covariance P (k | k), i.e., the parameter Error _ Cov, the observation matrix h (k), the kalman gain k (k), and the Error covariance predicted value P (k | k-1) are input and the equation Fun6 ═ 1-u (2) × u (1)) × u (3) is substituted to complete the update of the Error covariance, and the corresponding calculation formula is shown in the following formula.
P(k|k)＝[1-K(k)*H(k)]*P(k|k-1) (10)
The estimation process is effectively monitored by taking the SOC estimation value SOC and the Error covariance matrix Error _ Cov as the output of the submodule.
Continuing to step 6, outputting the working current, the SOC estimation value and the estimation Error thereof, wherein in the embodiment, the parameter I is a simulated working condition current added with Gaussian white noise, the parameter Est _ Error represents the Error of the estimated SOC value, and the parameter YkAnd the parameter Est _ Compare is a real-time observation contrast curve of the SOC estimation value and the actual SOC value, and the parameter Error _ Covariance is an SOC estimation Error Covariance change curve.
In summary, according to the working characteristics of the power lithium ion battery pack, the S-ECM battery equivalent model is designed by combining the consideration of estimation instantaneity and calculation complexity; performing OCV-SOC experimental analysis, and finishing the initial value setting of the state space equation coefficient according to prior experimental data; establishing a coulombic efficiency correction equation and setting equation coefficients according to the influence of different working currents and working temperatures on the working process and the battery capacity, inputting real-time measured working current and temperature signals as an SOC estimation system, and integrating the influence of the real-time measured working current and temperature signals on the SOC estimation process through the coulombic efficiency correction process; observing and correcting SOC estimation by observing the terminal voltage in the working process; then, the SOC state is estimated in real time by using KF-based recursive operation, and the SOC value of the power lithium ion battery pack is comprehensively estimated by observing the voltage and correcting the SOB of the equilibrium state; and finally, the working current, the SOC estimated value and the estimation deviation value of the estimation system are output, so that the SOC estimation of the power lithium ion battery pack can be efficiently carried out at low cost and with high accuracy, and the rapid and accurate monitoring of the working state and energy management in BMS equipment matched with the power lithium ion battery pack are facilitated.
The above embodiments of the present invention have been described for the purpose of power lithium ion battery pack SOC estimation by way of example only for an aviation lithium ion battery pack, but it is to be understood that any changes and variations may be made therein by those skilled in the art without departing from the spirit and scope of the present invention.
Claims (5)
1. A power lithium ion battery pack SOC estimation method is characterized in that a specific-Battery Equivalent Model S-ECM (Special-Equivalent Circuit Model) is constructed, and a reduced particle Unscented Kalman Filter RP-UKF (reduced particle-Unscented Kalman Filter) estimation method is applied to realize recursive operation of a grouped SOC state estimation Model;
(1) in the equivalent model S-ECM, the representation of the battery effect is realized by using a circuit equivalent mode, the ohmic effect of the battery is equivalently represented by using ohmic internal resistance, and the polarization effect of the battery is represented by using polarization internal resistance and polarization capacitance;
(2) in the equivalent model S-ECM, at open circuit voltage UOCA parallel resistor Rs is added at two ends to represent self-discharge effect, and the Rs is directly connected in parallel with an open-circuit voltage UOCNo other physical element between them;
(3) in the equivalent model S-ECM, a resistance parallel circuit in series with a backward diode is introduced, a resistance Rd and an Rc parallel circuit in series with the backward diode are added to represent the difference of charging and discharging internal resistances, the resistance Rd and the Rc parallel circuit are connected in series in a load circuit, the charging and discharging current of a battery must pass through the circuit, and no other optional charging and discharging electrifying path is available except the circuit;
(4) in the equivalent model S-ECM, the parallel capacitance Ce is added to describe the surface effect by connecting with the ideal voltage source UOCOhmic internal resistance RΩThe RC circuit is connected in parallel, so that the working process of the power lithium ion battery is comprehensively and accurately represented;
(5) in an iterative algorithm RP-UKF, aiming at the working condition analysis of the power lithium ion battery pack, an improved battery equivalent model state space equation (shown as the following formula) is blended to improve the calculation efficiency;
in the formula (1), each parameter is a parameter in the battery efficiency model S-ECM modelCounting; u shapeLIs the load voltage, CpIs a polarization capacitance, i is a polarization capacitance CpThe current passing through i (t) is electromotive force UOCDischarge current of iL(t) is the current through the load, is(t) is the current during self-discharge of the battery, UpIs a polarization capacitance CpVoltage of RpIs a polarization resistance;
(6) in an iterative algorithm RP-UKF, a linearization processing process is optimized through simplified particle filtering to eliminate estimation offset, a front end is integrated into a simplified particle transformation process to improve a linearization processing mechanism, namely, the linearization processing of simplified particles is firstly carried out before SOC estimation, so that the linearization processing mechanism is improved, variable output voltage Y (k), battery SOC state predicted value X (k | k-1), voltage predicted value Y (k | k-1) and Kalman gain K (k) are taken as input, an equation Fcn2 is substituted into u (2) + u (4) (u (1) -u (3)), calculation is carried out, and estimation and correction of the battery SOC are completed, and a corresponding calculation formula is shown as the following formula; x (k | k) ═ X (k | k-1) + k (k) ([ Y (k) — Y (k | k-1) ] (2);
(7) in an iterative algorithm RP-UKF, feedback correction is carried out through a state of equilibrium (SOB) (State of balance) to improve the estimation precision of a set of SOC and realize online estimation of the whole set of SOC value, in the process of solving an Error covariance P (k | k), namely a parameter Error _ Cov, the updating of the Error covariance is completed by taking an observation matrix H (k), a Kalman gain K (k) and an Error covariance predicted value P (k | k-1) as input, and a corresponding calculation formula is shown as the following formula
P(k|k)＝[1-K(k)*H(k)]*P(k|k-1) (3) 。
2. The method of claim 1, wherein the battery equivalent model S-ECM characterizes an ideal voltage source U of the open circuit voltageOCOn the basis, two ends are connected in parallel with a large resistor RSThe method is used for representing the self-discharge effect of the power lithium ion battery so as to reduce SOC estimation errors caused by the self-discharge phenomenon.
3. The method of claim 1, wherein the method comprises estimating the SOC of the lithium ion battery packThen, the internal resistance R representing the ohmic effect is connected in series in the battery equivalent model S-ECMΩAnd a parallel capacitor C is added on the basis of an RC parallel circuit for representing the polarization effecteThe expression of the surface effect is improved, and the characterization accuracy of the working state of the power lithium ion battery is improved.
4. The SOC estimation method for the power lithium ion battery pack according to claim 1, wherein a resistor parallel circuit connected with a backward diode in series is added in a battery equivalent model S-ECM for representing the difference of charge and discharge internal resistances; using resistive devices RdAs the discharge internal resistance during discharge, the internal resistance difference of the single lithium ion battery during discharge is represented; using resistive devices RcAs the internal resistance during charging, the difference in internal resistance exhibited by the lithium ion battery cell during charging is characterized.
5. The SOC estimation method of the power lithium ion battery pack is characterized in that an S-ECM state space equation is integrated into an estimation model RP-UKF to improve the calculation efficiency; the front end is integrated with simplified particle filter transformation, and the problem of estimation offset is solved by optimizing a nonlinear processing process; and the rear end integrates the SOB influence of the inter-cell equilibrium state on the basis of terminal voltage feedback correction, and comprehensively obtains a grouped SOC value.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610877189.1A CN106443473B (en) | 2016-10-09 | 2016-10-09 | SOC estimation method for power lithium ion battery pack |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610877189.1A CN106443473B (en) | 2016-10-09 | 2016-10-09 | SOC estimation method for power lithium ion battery pack |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106443473A CN106443473A (en) | 2017-02-22 |
| CN106443473B true CN106443473B (en) | 2020-06-23 |
Family
ID=58171553
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610877189.1A Expired - Fee Related CN106443473B (en) | 2016-10-09 | 2016-10-09 | SOC estimation method for power lithium ion battery pack |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106443473B (en) |
Families Citing this family (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106951605A (en) * | 2017-03-02 | 2017-07-14 | 西南科技大学 | A kind of Li-ion batteries piles equivalent model construction method |
| CN106772108A (en) * | 2017-03-08 | 2017-05-31 | 天津大学 | A kind of lithium battery SOC Forecasting Methodologies |
| CN107247234B (en) * | 2017-05-10 | 2019-12-31 | 江苏大学 | SOC real-time online estimation method and system for vehicle power battery |
| CN108363009B (en) * | 2017-12-26 | 2020-01-14 | 浙江大学 | Method for realizing online estimation of maximum allowable power of lithium ion battery |
| CN108829911A (en) * | 2018-04-16 | 2018-11-16 | 西南科技大学 | A kind of open-circuit voltage and SOC functional relation optimization method |
| CN108828446A (en) * | 2018-04-20 | 2018-11-16 | 西南科技大学 | A kind of modification method of ageing process to capacity impact |
| CN110441691A (en) * | 2018-05-02 | 2019-11-12 | 西南科技大学 | It is a kind of based on the SOC estimation method for simplifying particle Unscented transform |
| CN108732509A (en) * | 2018-06-06 | 2018-11-02 | 哈尔滨工业大学 | A kind of On-line Estimation method of the charge states of lithium ion battery of space-oriented application |
| CN109358293B (en) * | 2018-06-07 | 2021-04-02 | 温州大学 | Lithium ion battery SOC estimation method based on IPF |
| CN108427079B (en) * | 2018-06-11 | 2019-06-07 | 西南交通大学 | A kind of power battery method for estimating remaining capacity |
| CN109633463A (en) * | 2018-11-28 | 2019-04-16 | 上海松岳电源科技有限公司 | A kind of SOC appraisal procedure based on single battery electrokinetic cell system |
| CN109655758B (en) * | 2018-12-29 | 2020-12-11 | 蜂巢能源科技有限公司 | Battery open-circuit voltage measuring method and system |
| CN110221219A (en) * | 2019-07-03 | 2019-09-10 | 中国民用航空飞行学院 | Airborne circumstance is got off the plane lithium battery SOC estimation method |
| CN112255545B (en) * | 2019-07-05 | 2022-04-26 | 西南科技大学 | Lithium battery SOC estimation model based on square root extended Kalman filtering |
| CN110600820A (en) * | 2019-09-20 | 2019-12-20 | 天合光能股份有限公司 | Design method for lithium battery interconnection group |
| CN112649746A (en) * | 2019-10-10 | 2021-04-13 | 西南科技大学 | Charge state estimation method combining circuit equivalence and recursion iteration |
| CN112666466A (en) * | 2019-10-16 | 2021-04-16 | 西南科技大学 | Method for monitoring key parameters of power lithium battery pack in real time |
| CN113678009A (en) * | 2019-11-29 | 2021-11-19 | 旻泰克科技股份有限公司 | Battery state estimating device and method |
| CN112964997B (en) * | 2021-01-21 | 2022-03-29 | 西南科技大学 | Unmanned aerial vehicle lithium ion battery peak power self-adaptive estimation method |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7525285B2 (en) * | 2004-11-11 | 2009-04-28 | Lg Chem, Ltd. | Method and system for cell equalization using state of charge |
| CN101966820B (en) * | 2010-08-26 | 2013-06-12 | 清华大学 | On-line monitoring method for self-adaptively correcting lithium ion battery state-of-charge |
| CN102981125B (en) * | 2012-11-30 | 2015-11-18 | 山东省科学院自动化研究所 | A kind of electrokinetic cell SOC method of estimation based on RC equivalent model |
| CN105182245A (en) * | 2015-09-08 | 2015-12-23 | 盐城工学院 | High-capacity battery system charge state estimation method based on unscented Kalman filter |
-
2016
- 2016-10-09 CN CN201610877189.1A patent/CN106443473B/en not_active Expired - Fee Related
Also Published As
| Publication number | Publication date |
|---|---|
| CN106443473A (en) | 2017-02-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106443473B (en) | SOC estimation method for power lithium ion battery pack | |
| CN112098851B (en) | Intelligent battery and online state of charge estimation method and application thereof | |
| CN109633472B (en) | State of charge estimation algorithm of single lithium battery | |
| Wang et al. | Adaptive state-of-charge estimation method for an aeronautical lithium-ion battery pack based on a reduced particle-unscented kalman filter | |
| CN109839599B (en) | Lithium ion battery SOC estimation method based on second-order EKF algorithm | |
| CN111856282B (en) | Vehicle-mounted lithium battery state estimation method based on improved genetic unscented Kalman filtering | |
| Wang et al. | Estimation of battery open-circuit voltage and state of charge based on dynamic matrix control-extended Kalman filter algorithm | |
| CN109752660B (en) | Battery state of charge estimation method without current sensor | |
| CN106597291A (en) | On-line battery parameter estimation method | |
| CN112305426B (en) | Lithium ion battery power state estimation system under multi-constraint condition | |
| Tan et al. | Joint estimation of ternary lithium-ion battery state of charge and state of power based on dual polarization model | |
| Wang et al. | Lithium-ion battery security guaranteeing method study based on the state of charge estimation | |
| CN115327415A (en) | Lithium battery SOC estimation method based on limited memory recursive least square algorithm | |
| CN103744027A (en) | Kalman filtering-based self-correction cell SOC estimation method | |
| CN114720881A (en) | Lithium battery parameter identification method based on improved initial value forgetting factor recursive least square method | |
| Lin et al. | A novel approach to state of charge estimation using extended kalman filtering for lithium-ion batteries in electric vehicles | |
| Jin et al. | A novel robust back propagation neural networkdual extended Kalman filter model for state-ofcharge and state-of-health co-estimation of lithiumion batteries | |
| Li et al. | A high-fidelity hybrid lithium-ion battery model for SOE and runtime prediction | |
| Zhu et al. | Online state of charge EKF estimation for LiFePO 4 battery management systems | |
| CN110927585A (en) | Lithium battery SOH estimation system and method based on self-circulation correction | |
| Zhou et al. | Research on SOC Estimation for Lithium ion batteries Based on Improved PNGV Equivalence Model and AF-UKF Algorithm | |
| He et al. | Research on battery state of charge estimation based on variable window adaptive extended Kalman filter | |
| CN112540314A (en) | UKF-based SOC estimation method for lithium battery of electric vehicle | |
| CN113884901A (en) | Battery surface temperature distribution estimation method and system | |
| CN115436810A (en) | Hybrid electric vehicle lithium battery SOC and SOH combined estimation method based on double-adaptive Kalman filtering algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20200623 Termination date: 20211009 |