CN112710955B - Algorithm for improving battery capacity estimation precision - Google Patents

Algorithm for improving battery capacity estimation precision Download PDF

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CN112710955B
CN112710955B CN202011482543.3A CN202011482543A CN112710955B CN 112710955 B CN112710955 B CN 112710955B CN 202011482543 A CN202011482543 A CN 202011482543A CN 112710955 B CN112710955 B CN 112710955B
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capacity
voltage
charging curve
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CN112710955A (en
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郑岳久
周旋
秦超
周龙
崔一凡
晏莉琴
吕桃林
解晶莹
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Shanghai Institute of Space Power Sources
University of Shanghai for Science and Technology
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/3644—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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization

Abstract

The invention discloses an algorithm for improving the estimation precision of battery capacity, which comprises the following steps: the fourth-order extended Kalman filtering module takes a first-order RC equivalent circuit as a battery model, identifies battery parameter open-circuit voltage Voc, ohmic resistance R0, polarization resistance Rp and polarization capacitance Cp, then takes SOC, up, R0 and 1/Ccap as state variables, current I as an input variable and terminal voltage Ut as an output variable, and carries out fourth-order extended Kalman filtering to estimate a battery capacity Ccap value; performing voltage characteristic point optimization identification on the scaled and translated charging curve by using a genetic algorithm, and estimating a battery capacity Cr value according to a time interval between voltage characteristic points; extended Kalman filter re-fusion module with CDIs a state space variable, ccap is an input variable, cr is an output variable, and iteration is carried out to obtain a capacity estimated value CD. According to the invention, the capacity estimation error can be reduced, the health state of the battery can be estimated more accurately, and the safety of the new energy vehicle using the lithium battery as a power source can be improved.

Description

Algorithm for improving battery capacity estimation precision
Technical Field
The invention relates to the technical field of battery management, in particular to an algorithm for improving the estimation precision of battery capacity.
Background
In the situation of shortage of fossil fuels, attention is now being turned to electric automobiles as an automobile industry in which consumption of fossil fuels is extremely high. The lithium ion power battery has the advantages of high energy density, high power density, low spontaneous discharge rate, no memory effect and the like, and is widely applied to electric automobiles. At present, electric automobiles face a plurality of problems, such as short driving range, short battery life, high price and the like, which restrict the further development of the electric automobiles. Durability is an important parameter of lithium ion batteries and is directly related to the life of the battery, the better the durability, the longer the battery life. The degree of capacity fade of a battery characterizes the length of the battery life, and therefore, the capacity of the battery needs to be estimated.
The battery capacity estimation separates the two types of loop estimation and closed loop estimation. The open-loop estimation method is to directly predict capacity attenuation and internal resistance change based on a battery durability model by establishing a durability model, the most common is an Arrhenius model and an extended model thereof, but the model is open-loop and is an empirical model obtained by testing under a constant condition, and the actual vehicle operation variable working condition is not considered, so that an accurate capacity estimation value cannot be obtained. The closed-loop estimation method is mainly based on the existing battery model, adopts an optimal state estimation technology such as least square method, kalman filtering and other algorithms, and identifies the parameters of the battery model such as capacity, internal resistance and the like according to the running data, but the precision of the closed-loop estimation method mainly depends on the SOC estimation precision, and once the SOC estimation has deviation, the capacity estimation generates larger errors.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide an algorithm for improving the battery capacity estimation precision, which is beneficial to reducing capacity estimation errors, more accurately evaluating the health state of a battery and improving the safety of a new energy vehicle taking a lithium battery as a power source. To achieve the above objects and other advantages in accordance with the present invention, there is provided an algorithm for improving battery capacity estimation accuracy, comprising the steps of:
s1, establishing a first-order RC equivalent circuit battery model based on a kirchhoff voltage law and a kirchhoff current law, wherein the formula is as follows:
Figure BDA0002838006390000021
s2, identifying battery parameters through a static capacity test, a mixed pulse test, a direct current resistance test, a dynamic stress test and a federal city driving plan test, wherein Rp and Cp are extracted by using a recursive least square algorithm;
s3, updating and estimating Ccap by using the SOC, the capacitance voltage Up, the ohmic internal resistance R0 and the reciprocal 1/Ccap of the capacitance as state variables and utilizing a fourth-order extended Kalman filtering algorithm, wherein the calculation formula of the capacitance is as follows:
Figure BDA0002838006390000022
s4, obtaining complete charging curves of the battery monomer at different attenuation stages by performing a durability cycle life test;
s5, under the condition of constant current charging with the same current, the time interval of the charging curve reaching any two voltage points A, B is in direct proportion to the actually chargeable electric quantity between the two voltage points, namely the following relational expression is satisfied,
Figure BDA0002838006390000023
since the amount of electricity actually charged between the two voltage points depends on the current capacity of the battery cell, the following relationship is obtained,
Figure BDA0002838006390000031
therefore, the current capacity Cr of the battery can be estimated only by knowing the time interval and the initial capacity of the charging curve between two voltage characteristic points;
s6, extended Kalman filterWave re-fusion module with CDIs a state space variable, ccap is an input variable, cr is an output variable, and iteration is carried out to obtain a capacity estimated value CD。
Preferably, the step S2 further comprises the steps of:
Figure BDA0002838006390000032
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0002838006390000033
preferably, the capacity of the lithium ion battery is identified on line by using the voltage characteristic point, and the r-th charging curve after attenuation satisfies the following relation relative to the 1 st charging curve (initial state of the battery):
Figure BDA0002838006390000034
the electric quantity actually charged between the two voltage points depends on the current capacity of the single battery, and the following formula is satisfied:
Figure BDA0002838006390000035
from companies (1) and (2), the following equations can be obtained:
Figure BDA0002838006390000036
preferably, the step of searching for the voltage characteristic point comprises the following steps:
1) Carrying out a durability cycle life test on the battery monomer, and carrying out a standard capacity test at intervals to obtain complete charging curves and capacities of the battery monomer at different stages from the beginning to the end of the life;
2) Selecting an initial charging curve of the battery and an r charging curve in the process of battery capacity attenuation, and carrying out scaling translation on the r charging curve by taking the 1 st charging curve as a reference so as to enable the end point of the r charging curve to be superposed with the end point of the 1 st charging curve;
3) So that the time interval between the two characteristic points of the scaled r-th charging curve is the same as the time interval between the two characteristic points of the 1 st charging curve,
Δt′r(AB)＝Δt1(AB)
in the formula (I), the compound is shown in the specification,
Δt′r(AB)＝t′rB-t′rA，Δt1(AB)＝t1B-t1A
the shape of the composite material is changed into the shape of the composite material,
t′rA-t1A＝t′rB-t1B。
preferably, in the process of battery capacity fading, as long as each scaled charging curve can satisfy the above formula, even if the result of the following formula is minimal, the voltage characteristic point A, B can be found,
Figure BDA0002838006390000041
using genetic algorithms, with sigmaABAnd solving two voltage characteristic points by taking the minimum as an optimization target, wherein the constraint condition is that the two voltage characteristic points are separated by at least 100mV.
Preferably, the updating the battery capacity value by using the extended kalman filter includes the following steps:
1) Establishing a linearized state space model, wherein the equation is as follows:
Figure BDA0002838006390000042
Figure BDA0002838006390000043
2) The initialization assignment is performed as follows:
when k =0, the signal is transmitted,
Figure BDA0002838006390000051
3) The state variable time updates are as follows:
Figure BDA0002838006390000052
4) The error covariance time update is as follows:
Figure BDA0002838006390000053
5) The extended kalman gain matrix is updated as follows:
Figure BDA0002838006390000054
6) The state variable measurements are updated as follows:
Figure BDA0002838006390000055
7) The error covariance measurement is updated as follows:
Figure BDA0002838006390000056
wherein, the capacity value Ccap estimated by the fourth-order extended Kalman filtering algorithm is an input variable ukThe capacity value Cr estimated based on the voltage characteristic point is used as an output variable ykFusion algorithm estimate CDThe value being a state variable xk。
Compared with the prior art, the invention has the beneficial effects that: the method has the advantages that the initial capacity identification is carried out by using the voltage characteristic points, the accuracy is high, the reliability is good, more accurate correction values can be provided for subsequent model correction, capacity online identification values and a four-order extended Kalman filtering algorithm are fused, the battery capacity is updated through the extended Kalman filtering algorithm, and the capacity estimation accuracy is further improved.
Drawings
FIG. 1 is a flow chart of a fusion algorithm for an algorithm for improving battery capacity estimation accuracy in accordance with the present invention;
FIG. 2 is a first order RC battery equivalent circuit structure diagram of an algorithm for improving battery capacity estimation accuracy according to the present invention;
FIG. 3 is a charge curve scaling translation graph for an algorithm for improving battery capacity estimation accuracy in accordance with the present invention;
FIG. 4 is a schematic of the time difference calculation for the algorithm for improving the accuracy of the battery capacity estimation according to the present invention;
fig. 5 is a graph of estimated capacity of a fusion algorithm for improving the accuracy of estimation of battery capacity 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 obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
Referring to fig. an algorithm for improving the accuracy of battery capacity estimation, comprising the steps of: s1, establishing a battery equivalent circuit model according to battery characteristics, considering polarization, and selecting a first-order RC equivalent circuit battery model established based on kirchhoff' S voltage law and current law, wherein the circuit structure is shown in figure 2, and the formula is as follows:
Figure BDA0002838006390000061
for BMS applications, the circuit model needs to be discretized into a discretization formula:
Figure BDA0002838006390000071
s2, identifying battery parameters through a static capacity test, a mixed pulse test, a direct current resistance test, a dynamic stress test and a federal city driving plan test, wherein Rp and Cp are extracted by using a recursive least square algorithm;
s3, updating and estimating Ccap by using the SOC, the capacitance voltage Up, the ohmic internal resistance R0 and the reciprocal 1/Ccap of the capacitance as state variables and utilizing a fourth-order extended Kalman filtering algorithm, wherein the calculation formula of the capacitance is as follows:
Figure BDA0002838006390000072
the current I is an input variable, the terminal voltage Ut is an output variable, and the state variable is as follows:
Ax＝[SOC up R0 1/Ccap]T，
the state space equation is:
Figure BDA0002838006390000073
wherein A (k) is a state matrix:
Figure BDA0002838006390000074
b (k) is an input matrix:
Figure BDA0002838006390000081
c (k) is an observation matrix:
Figure BDA0002838006390000082
the extended kalman filter algorithm is as follows:
Figure BDA0002838006390000083
Figure BDA0002838006390000084
Figure BDA0002838006390000085
Figure BDA0002838006390000086
Figure BDA0002838006390000087
wherein P is a covariance matrix, G is a Kalman gain, Q is a system noise covariance, and W is an observation noise covariance.
S4, obtaining complete charging curves of the battery monomer at different attenuation stages by performing a durability cycle life test;
s5, under the condition of constant current charging with the same current, the time interval of the charging curve reaching any two voltage points A, B is in direct proportion to the actually chargeable electric quantity between the two voltage points, namely the following relational expression is satisfied,
Figure BDA0002838006390000088
since the amount of electricity actually charged between the two voltage points depends on the current capacity of the battery cell, the following relationship is obtained,
Figure BDA0002838006390000091
therefore, the current capacity Cr of the battery can be estimated only by knowing the time interval and the initial capacity of the charging curve between two voltage characteristic points;
s6, an extended Kalman filter re-fusion module and CDIs a state space variable, ccap is an input variable, cr is an output variable, and iteration is carried out to obtain a capacity estimated value CD。
Further, battery model parameters are identified (assuming the initial voltage of the entire RC network is equal to zero). The open-circuit voltage Voc is determined by the steady-state voltage, the ohmic resistance R0 is determined by the ratio of the pulse instantaneous voltage drop to the pulse current, and the polarization resistance Rp and the capacitance CP are extracted by adopting a recursive least square method, and the step S2 further comprises the following steps:
Figure BDA0002838006390000092
wherein the content of the first and second substances,
Figure BDA0002838006390000093
from the above two formulae, a1，a2Value of,
and then the polarization resistance Rp and the capacitance CP value are obtained by the following formula:
Figure BDA0002838006390000094
further, the capacity of the lithium ion battery is identified on line by using the voltage characteristic points, and the r-th charging curve after attenuation satisfies the following relation relative to the 1 st charging curve (initial state of the battery):
Figure BDA0002838006390000101
the electric quantity actually charged between the two voltage points depends on the current capacity of the single battery, and the following formula is satisfied:
Figure BDA0002838006390000102
from companies (1) and (2), the following formulas are available:
Figure BDA0002838006390000103
further, the step of searching for the voltage characteristic point comprises the following steps:
1) Carrying out a durability cycle life test on the battery monomer, and carrying out a standard capacity test at intervals to obtain complete charging curves and capacities of the battery monomer at different stages from the beginning to the end of the life;
2) Selecting an initial charging curve of the battery and an r charging curve in the process of battery capacity attenuation, and carrying out scaling translation on the r charging curve by taking the 1 st charging curve as a reference so as to enable the end point of the r charging curve to be superposed with the end point of the 1 st charging curve;
3) So that the time interval between the two characteristic points of the scaled r-th charging curve is the same as the time interval between the two characteristic points of the 1 st charging curve,
Δt′r(AB)＝Δt1(AB)
in the formula (I), the compound is shown in the specification,
Δt′r(AB)＝t′rB-t′rA，Δt1(AB)＝t1B-t1A
the shape of the composite material is changed into the shape of the composite material,
t′rA-t1A＝t′rB-t1B。
further, in the process of battery capacity attenuation, as long as each zoomed charging curve can satisfy the above formula, even if the result of the following formula is minimum, the voltage characteristic point A, B can be found,
Figure BDA0002838006390000111
using genetic algorithms, in sigmaABAnd solving two voltage characteristic points with the minimum as an optimization target, wherein the two voltage characteristic points are separated by at least 100mV under the constraint condition.
Further, updating the battery capacity value by using the extended kalman filter includes the following steps:
1) Establishing a linearized state space model, wherein the equation is as follows:
Figure BDA0002838006390000112
Figure BDA0002838006390000113
since the model is non-linear, it needs to be linearized by taylor's formula
f(xk，uk) And g (x)k，uk) Expanding, and omitting more than two terms to obtain:
Figure BDA0002838006390000114
Figure BDA0002838006390000115
and defines:
Figure BDA0002838006390000116
obtaining a linearized state space model;
Figure BDA0002838006390000117
Figure BDA0002838006390000118
the extended kalman filter algorithm is as follows:
initialization: when k =0, the signal is transmitted,
Figure BDA0002838006390000119
and (3) calculating: when k =1,2, …
Figure BDA0002838006390000121
Figure BDA0002838006390000122
Figure BDA0002838006390000123
Figure BDA0002838006390000124
Figure BDA0002838006390000125
Wherein k is the number of capacity measurements, P is the covariance matrix, L is the extended Kalman gain, w is the system noise covariance, and θ is the observation noise covariance.
2) The initialization assignment is performed as follows:
when k =0, the number of the bits is set to zero,
Figure BDA0002838006390000126
3) The state variable time updates are as follows:
Figure BDA0002838006390000127
4) The error covariance time update is as follows:
Figure BDA0002838006390000128
5) The extended kalman gain matrix is updated as follows:
Figure BDA0002838006390000129
6) The state variable measurements are updated as follows:
Figure BDA00028380063900001210
7) The error covariance measurement is updated as follows:
Figure BDA00028380063900001211
the battery capacity is further estimated by using the extended kalman filter on the capacity value Ccap estimated in step 3 and the capacity value Cr estimated in step 4. The extended Kalman Filter algorithm is the most widely used optimal state estimation algorithm for nonlinear systems, in CDIs a state space variable xkCcap is an input variable ukCr is an output variable ykIteration is carried out, and the capacity value Ccap estimated by the fourth-order extended Kalman filtering algorithm is taken as an input variable ukThe capacity value Cr estimated based on the voltage characteristic point is used as an output variable ykFusion algorithm estimate CDThe value being a state variable xk。
The battery capacity estimation curve graph is shown in fig. 5, the fusion algorithm estimation capacity curve is between the single fourth-order extended kalman filter estimation capacity curve and the single voltage characteristic point-based estimation capacity curve, and the algorithm can effectively reduce estimation errors and enable the estimated capacity to gradually approach the true value of the battery.
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 (2)

1. An algorithm for improving accuracy of battery capacity estimation, comprising the steps of:
s1, establishing a first-order RC equivalent circuit battery model based on kirchhoff' S voltage law and current law, wherein the formula is as follows:
Figure FDA0003845317330000011
wherein Up is capacitor voltage, rp is polarization resistance, voc is open-circuit voltage, R0 is ohmic internal resistance, ibatt is input current, vt is output voltage, and Cp is capacitor;
s2, identifying battery parameters through a static capacity test, a mixed pulse test, a direct current resistance test, a dynamic stress test and a federal city driving plan test, wherein Rp and Cp are extracted by using a recursive least square algorithm;
s3, updating and estimating Ccap by using the SOC, the capacitance voltage Up, the ohmic internal resistance R0 and the reciprocal 1/Ccap of the capacitance as state variables and utilizing a fourth-order extended Kalman filtering algorithm, wherein the calculation formula of the capacitance is as follows:
Figure FDA0003845317330000012
wherein Ccap is capacity;
s4, obtaining complete charging curves of the battery monomer at different attenuation stages by performing a durability cycle life test;
s5, under the condition of constant current charging with the same current, the time interval of the charging curve reaching any two voltage points A, B is in direct proportion to the actually chargeable electric quantity between the two voltage points, namely the following relational expression is satisfied,
Figure FDA0003845317330000021
since the amount of electricity actually charged between the two voltage points depends on the current capacity of the battery cell, the following relationship is obtained,
Figure FDA0003845317330000022
therefore, the current capacity Cr of the battery can be estimated only by knowing the time interval and the initial capacity of the charging curve between two voltage characteristic points; wherein, is Δ Qr(AB)、ΔQ1(AB)The r-th and 1-th charged electric quantities, delta t, of A, B are providedr(AB)、Δtr(AB)The interval time is the interval time of the r time and the 1 st time, C1 is the initial capacity, and Cr is the current capacity of the r time;
s6, an extended Kalman filter re-fusion module and CDIs a state space variable, ccap is an input variable, cr is an output variable, and iteration is carried out to obtain a capacity estimated value CD；
The voltage characteristic point searching method comprises the following steps:
1) Carrying out a durability cycle life test on the battery monomer, and carrying out a standard capacity test at intervals to obtain complete charging curves and capacities of the battery monomer at different stages from the beginning to the end of the life;
2) Selecting an initial charging curve of the battery and an r charging curve in the process of battery capacity attenuation, and carrying out scaling translation on the r charging curve by taking the 1 st charging curve as a reference so as to enable the end point of the r charging curve to be superposed with the end point of the 1 st charging curve;
3) So that the time interval between the two characteristic points of the scaled r-th charging curve is the same as the time interval between the two characteristic points of the 1 st charging curve,
Δt′r(AB)＝Δt1(AB) (4)
in the formula (I), the compound is shown in the specification,
Δt′r(AB)=t′rB-t′rA，Δt1(AB)＝t1B-t1A (5)
the shape of the composite material is changed into the shape of the composite material,
t′rA-t1A＝t′rB-t1B (6)；
in the process of battery capacity attenuation, as long as each scaled charging curve can satisfy the formula (6), even if the result of the formula (7) is minimum, the voltage characteristic point A, B can be found,
Figure FDA0003845317330000031
using genetic algorithms, with sigmaABAnd solving two voltage characteristic points with the minimum as an optimization target, wherein the two voltage characteristic points are separated by at least 100mV under the constraint condition.
2. The algorithm for improving the estimation accuracy of the battery capacity according to claim 1, wherein the step S6 of updating the battery capacity value by using the extended kalman filter comprises the following steps:
1) Establishing a linearized state space model, wherein the equation is as follows:
Figure FDA0003845317330000032
Figure FDA0003845317330000033
wherein，
Figure FDA0003845317330000034
2) Carrying out state variables
Figure FDA0003845317330000035
Error covariance
Figure FDA0003845317330000036
And (4) initializing assignment, wherein the process is as follows:
when k =0, the number of the bits is set to zero,
Figure FDA0003845317330000037
3) Variable of state
Figure FDA0003845317330000041
The time updates are as follows:
Figure FDA0003845317330000042
4) Error covariance
Figure FDA0003845317330000043
The time updates are as follows:
Figure FDA0003845317330000044
5) Extended Kalman gain matrix LkThe update is as follows:
Figure FDA0003845317330000045
6) Variable of state
Figure FDA0003845317330000046
The measurements are updated as follows:
Figure FDA0003845317330000047
7) Error covariance
Figure FDA0003845317330000048
The measurements are updated as follows:
Figure FDA0003845317330000049
wherein, the capacity value Ccap estimated by the fourth-order extended Kalman filtering algorithm is an input variable ukThe capacity value Cr estimated based on the voltage characteristic point is used as an output variable ykFusion algorithm estimate CDThe value being a state variable xk。
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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110231567A (en) * 2019-07-16 2019-09-13 奇瑞新能源汽车股份有限公司 A kind of electric car SOC estimating algorithm
CN111060834A (en) * 2019-12-19 2020-04-24 中国汽车技术研究中心有限公司 Power battery state of health estimation method

Family Cites Families (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2004031254A (en) * 2002-06-28 2004-01-29 Nissan Motor Co Ltd Capacity control device and method for battery pack
JP4686139B2 (en) * 2004-05-31 2011-05-18 株式会社Ｇｓユアサ Battery charge state calculation method
CN1945345A (en) * 2005-10-09 2007-04-11 奇瑞汽车有限公司 Detecting device and method for mixed power automobile battery remainder
CN105717455B (en) * 2016-01-30 2018-10-02 清华大学 The selection method of the battery cell SOC and capacity estimation algorithm of battery pack
CN106842061A (en) * 2017-03-28 2017-06-13 上海理工大学 A kind of method of estimation of low state-of-charge
CN107589379A (en) * 2017-08-30 2018-01-16 电子科技大学 A kind of On-line Estimation lithium battery SOC and the method for impedance
CN109870651A (en) * 2019-01-22 2019-06-11 重庆邮电大学 A kind of electric automobile power battery system SOC and SOH joint estimation on line method
CN110502778B (en) * 2019-07-02 2021-06-22 江苏大学 Adaptive optimization method for estimating battery SOC based on Kalman filtering framework
CN111027203B (en) * 2019-12-05 2023-05-02 中车株洲电力机车有限公司 Super capacitor SOC calculation method
CN111239625A (en) * 2020-03-09 2020-06-05 天津市捷威动力工业有限公司 SOC range selection method for self-discharge screening, storage medium and equipment
CN111474487A (en) * 2020-04-13 2020-07-31 重庆大学 Battery state of charge-internal temperature joint online estimation method

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110231567A (en) * 2019-07-16 2019-09-13 奇瑞新能源汽车股份有限公司 A kind of electric car SOC estimating algorithm
CN111060834A (en) * 2019-12-19 2020-04-24 中国汽车技术研究中心有限公司 Power battery state of health estimation method

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