CN110850318A - Lithium battery health state estimation method with filtering method weight distribution - Google Patents

Lithium battery health state estimation method with filtering method weight distribution Download PDF

Info

Publication number
CN110850318A
CN110850318A CN201911075498.7A CN201911075498A CN110850318A CN 110850318 A CN110850318 A CN 110850318A CN 201911075498 A CN201911075498 A CN 201911075498A CN 110850318 A CN110850318 A CN 110850318A
Authority
CN
China
Prior art keywords
charging
data
section
current capacity
capacity
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.)
Pending
Application number
CN201911075498.7A
Other languages
Chinese (zh)
Inventor
郑岳久
厉凯
陆一凡
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
University of Shanghai for Science and Technology
Original Assignee
University of Shanghai for Science and Technology
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by University of Shanghai for Science and Technology filed Critical University of Shanghai for Science and Technology
Priority to CN201911075498.7A priority Critical patent/CN110850318A/en
Publication of CN110850318A publication Critical patent/CN110850318A/en
Pending legal-status Critical Current

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]
    • G01R31/392—Determining battery ageing or deterioration, e.g. state of health
    • 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

Abstract

The invention relates to a lithium battery health state estimation method with filtering method weight distribution, which comprises the steps of firstly screening battery related data collected by a vehicle Battery Management System (BMS), including data such as current of a lithium battery in the charging process, state of charge (SOC) of the battery and the like, estimating the SOH of the lithium battery by using a lithium battery capacity estimation method based on a multi-section charging curve, considering the influence of the initial state of charge (minSOC) of a selected charging section on the current capacity calculation result, namely the smaller the minSOC value is, the closer the current capacity value obtained by calculation is to a real value, and distributing a larger value to the filtering method weight used in the estimation process by using a specific function, so that the SOH of the battery estimated on line is more consistent with the actual running condition of a vehicle, and the estimation result is more accurate.

Description

Lithium battery health state estimation method with filtering method weight distribution
Technical Field
The invention relates to a battery detection technology, in particular to a lithium battery health state estimation method with filtering method weight distribution based on a multi-section charging curve.
Background
The electric automobile is a new product produced since the 21 st century, meets the requirements of the modern society on energy conservation and environmental protection under the large background of the modern times, and has wide development prospect.
For an electric vehicle, the capacity of the battery is an important index for measuring the performance of the battery of the vehicle. SOC, which is called State of Charge, battery State of Charge, also called remaining capacity, represents the ratio of the remaining dischargeable capacity to the capacity in its fully charged State after a battery has been used for a period of time or left unused for a long period of time, and is usually expressed as a percentage. The SOH is fully called State of Health, the battery capacity, the Health degree and the performance State are the ratio of the performance parameters to the nominal parameters after the battery is used for a period of time, the battery which is newly delivered is 100 percent, and the total scrappage is 0 percent. The ratio of the capacity discharged by the battery discharging to the cut-off voltage with a certain multiplying factor from the full charge state to the corresponding nominal capacity.
At present, the weight distribution when the current capacity is calculated by a filtering method in the SOH estimation process of a lithium battery with a multi-section charging curve is generally according to a fixed coefficient, the calculation mode of the algorithm is simple and is convenient to implement, but the estimation result is easy to deviate.
Disclosure of Invention
The invention provides a lithium battery health state estimation method with filtering method weight distribution aiming at the problem of accurate estimation of the lithium battery health state, and the method takes the influence of the initial state of charge (minSOC) of a selected charging section on the current capacity calculation result into consideration, and distributes the filtering method weight 'β' used in the estimation process by using a function, so that the SOH of the battery estimated on line is more consistent with the actual running condition of a vehicle, and the estimation result is more accurate.
The technical scheme of the invention is as follows: a lithium battery health state estimation method with filtering method weight distribution is characterized in that a screening battery management system acquires charging current and charge state data of a battery in multiple charging processes, screens out data used for calculating initial capacity and current capacity, obtains the initial capacity and the current capacity according to a charging data section and ampere-hour integration, considers the influence of a selected charging section minSOC on a current capacity calculation result, calculates again through a filtering method to obtain accurate current capacity, distributes weights for calculating the current capacity through functions in the filtering method, and the ratio of the obtained current capacity to the initial capacity is the estimated health state of the battery.
The lithium battery health state estimation method with filtering method weight distribution specifically comprises the following steps:
1) acquiring charging current and charge state data of the battery in multiple charging processes through a battery management system on the automobile, and screening out data meeting conditions according to required conditions;
2) screening data used for calculating initial capacity, wherein the screening requirement is as follows: recording the initial SOC and the cut-off SOC of the charging section as minSOC and maxSOC respectively, wherein minSOC is less than a, (maxSOC-minSOC) is more than b,0 is more than a, b is less than 100%, a is an upper limit threshold value of the initial SOC of the screening charging section, and b is the span of the initial SOC and the final SOC of the selected charging section;
3) acquiring a first charging data segment meeting the requirement, and recording the initial capacity obtained by using ampere-hour integral calculation in the charging data segment as Capinit (ii), wherein ii is 1;
4) and obtaining the ii-th charging data section meeting the requirement, calculating the ii-th initial capacity by using ampere-hour integral, wherein ii is more than or equal to 2 and is recorded as: capinitk (ii);
the equation for ampere-hour integration is as follows:
Figure BDA0002262306220000021
wherein t is1Represents a charging start time of the charging section; t is t2Indicating a charge end time of the charge segment; t is t3Representing the corresponding time of SOC as c, c is a set reference point, and the value range of c is 40%<c<70 percent; i (t) represents a current value at time t in the charging section;
Capinitk(ii)＝AHsum/(min{maxSOC,c}-minSOC)
5) and calculating the accurate initial capacity by using a filtering method after obtaining the two data, wherein the formula is as follows:
Figure BDA0002262306220000022
α is an initial capacity filter coefficient, and since the change of the initial capacity value is very small and the filtering is very strong, the requirement can be met by taking α as a value smaller than 0.1;
6) screening a charging data section for calculating the current capacity, wherein the current capacity is marked as Cap;
screening requirements: the minSOC of the charging section is less than d, maxSOC is more than or equal to e,0 is less than d, e is less than 100%, d is an upper limit threshold value of the screening charging section initial SOC, e is a lower limit threshold value of the screening charging section cut-off SOC, and the values of d and e are determined according to actual data conditions;
7) acquiring a first charging data section meeting the requirement, and recording the current capacity obtained by using ampere-hour integral calculation in the charging data section as Cap (ii), wherein ii is 1;
the equation for ampere-hour integration is as follows:
Figure BDA0002262306220000031
wherein t is1Represents a charging start time of the charging section; t is t2A charge end time indicating the charge period, capk (ii) Ahsum/(maxSOC-minSOC);
8) and calculating the ii current capacity by using ampere-hour integral in the ii charging data section meeting the requirement, wherein ii is more than or equal to 2 and is recorded as: capk (ii);
9) and calculating the accurate current capacity Cap (ii) by a filtering method, wherein the formula is as follows:
Figure BDA0002262306220000032
wherein β is the current capacity filtering method weight β;
10) the state of health (SOH) of the automobile lithium ion battery can be obtained through the Cap (ii) obtained in the step 5) and the Capinit (ii) obtained in the step 8),
SOH(ii)＝Cap(ii)/Capinit(ii)(ii≥1)。
the filtering method weight β is selected as follows:
selecting function curve characteristics as weight distribution basis, wherein the function selects any one of quadratic function, linear function and piecewise function;
b, setting a reference quantity for screening the minimum state of charge of the charging section, wherein the difference between the reference quantity and x is a function term, x is the initial SOC value of the selected charging section, β (x) the current capacity weight, and establishing a distribution formula according to the step A;
and C, extracting charging data segments corresponding to different x, fitting the data by using MATLAB, and determining each coefficient in a distribution formula to obtain a final distribution formula of the current capacity weight β.
Compared with the filtering method of fixed weight 'β' used for calculating the current capacity in the current online pre-SOH process, the lithium battery SOH estimation method with filtering method weight distribution is more suitable for the lithium battery SOH estimation process based on the multi-segment charging curve, the influence of the minSOC of the selected charging segment on the current capacity calculation result is considered, namely the smaller the minSOC value is, the more accurate the calculated current capacity is, the function according with the change trend that the weight value is reduced along with the increase of the minSOC is used for distributing the filtering method weight 'β' used for calculating the current capacity in the SOH estimation process, so that the current capacity calculated by the charging segment with the smaller minSOC value obtains larger weight in the filtering process, the SOH of the online estimated automobile lithium battery is more real in line, and the estimation result is more accurate.
Drawings
FIG. 1 is a diagram of a lithium battery health status estimation method with filtering method weight distribution according to the present invention;
FIG. 2 is a graph of quadratic function weight distribution according to the present invention;
FIG. 3 is a graph of linear function weight distribution according to the present invention;
FIG. 4 is a graph of piecewise function weight assignment according to the present invention.
Detailed Description
The method for estimating the health state of the lithium battery based on the multi-segment charging curve with filtering method weight distribution considers the influence of the initial charge state minSOC of the selected charging segment on the current capacity calculation result, and distributes the filtering method weight for calculating the current capacity by using a function.
The following is the lithium battery state of health (SOH) estimation process:
1. the method comprises the steps of acquiring data such as charging current, state of charge (SOC) and the like of a battery in multiple charging processes through a Battery Management System (BMS) on an automobile, and screening out data meeting conditions according to required conditions. 2. Screening data for calculating initial capacity, the screening requiring: the initial SOC and the cut-off SOC of the charging section are recorded as minSOC and maxSOC respectively, wherein minSOC is less than a, (maxSOC-minSOC) > b,0 is less than a, b is less than 100%, a is an upper limit threshold value of the initial SOC of the screening charging section, the reliability of a calculation result is high when the initial SOC of the selected charging section is lower than the value, b is the span of the initial SOC and the last SOC of the selected charging section, and the larger the span is, the higher the reliability of the calculation result is. a and b need to be determined according to actual data conditions.
3. And acquiring a first (ii-1) qualified charging data segment, and recording the initial capacity obtained by using ampere-hour integral calculation in the charging data segment as Capinit (ii).
4. Obtaining the ii (ii is more than or equal to 2) charging data section meeting the requirement, and calculating the ii initial capacity by using ampere-hour integration, and recording as: capinitk (ii).
The equation for ampere-hour integration is as follows:
Figure BDA0002262306220000051
wherein t is1Represents a charging start time of the charging section; t is t2Indicating a charge end time of the charge segment; t is t3Representing the corresponding time when the SOC is c, c is a set reference point, and the value range of c is: 40 percent of<c<70%, the initial capacity value calculated by taking the range as a reference point is more accurate; i (t) represents the current value at time t in the charging section,
Capinitk(ii)＝AHsum/(min{maxSOC,c}-minSOC)；
5. the accurate initial capacity can be calculated by a filtering method after the two data are obtained, and the formula is as follows:
Figure BDA0002262306220000052
6. since the initial capacity value changes very little and the filtering is very strong, it can be satisfied to take α as a value less than 0.1.
7. And screening a charging data segment for calculating the current capacity, wherein the current capacity is marked as Cap.
Screening requirements: the minSOC of the charging section is less than d, maxSOC is more than or equal to e,0 is less than d, e is less than 100%, d is an upper limit threshold value of the screening charging section initial SOC, and e is a lower limit threshold value of the screening charging section cutoff SOC. The values of d and e need to be determined according to actual data conditions.
8. Acquiring a first (ii ═ 1) satisfactory charging data segment, and recording the current capacity obtained by using ampere-hour integral calculation in the charging data segment as Cap (ii).
The equation for ampere-hour integration is as follows:
Figure BDA0002262306220000053
wherein t is1Represents a charging start time of the charging section; t is t2Indicating the end-of-charge time for that charge segment.
Capk(ii)＝Ahsum/(maxSOC-minSOC)
9. And (ii) obtaining the current capacity of the ii th charging data section (ii is more than or equal to 2) meeting the requirement by using ampere-hour integral calculation, and recording the current capacity as: capk (ii).
10. The exact current capacity cap (ii) is calculated by filtering, and the formula is as follows:
Figure BDA0002262306220000061
11. the filtering weights β are given according to the embodiments described below.
12. The state of health (SOH) of the lithium ion battery of the vehicle can be obtained by cap (ii) obtained in step 5 and capinit (ii) obtained in step 10.
SOH(ii)＝Cap(ii)/Capinit(ii)(ii≥1)。
In the present embodiment, a filtering method weight "β" used in the calculation of the current capacity cap (ii) in the SOH estimation process is assigned, and a quadratic function curve characteristic is used as a weight assignment basis in the present embodiment, a reference amount d for screening the minimum state of charge of the charging section is taken as 25%, and the specific assignment method is as follows:
the allocation formula:
current capacity weight (β) formula β (x) tβ(25-x)2+bβ
Wherein x is more than or equal to 0 and less than 25 percent, tβAs a factor, x is the selected initial SOC value of the charging section, bβIs a constant term.
From the effect of the starting SOC on the calculation results, it can be assumed that,
β (x) is 0.99 when x is 0, β (x) is 0.01 when x is 25%.
From the above condition tβ＝0.001568,bβFitting the data by using MATLAB (matrix laboratory) to obtain a fitting curve formula as follows:
β(x)＝0.001568x2-0.00784x+0.99(0≤x＜25％)
the resulting special point weight values are shown in table 1.
TABLE 1
Figure BDA0002262306220000062
Figure BDA0002262306220000071
In the SOH estimation process of the lithium battery with the multi-section charging curves, the following groups of minSOC are taken from the currently selected charging sections, and the weights of the secondary linear embodiment with the random experimental points shown in the table 2 are obtained through the algorithm provided by the embodiment.
TABLE 2
x(％) β(x)
20 0.0492
16 0.1370
3 0.7689
13 0.2358
8 0.4632
25 0.01
19 0.0664
1 0.9132
22 0.0241
7 0.518
In addition, the present invention provides a method for estimating a health status of a lithium battery based on a multi-segment charging curve with filtering method weight distribution, wherein an embodiment of the method is variable 'β' of a quadratic function, in addition, other variable methods such as linear variable 'β' are available for the weight according to the function change, and a linear weight distribution embodiment is taken as an example below, in the embodiment, a reference d of screening the minimum state of charge of a charging segment is taken as 25%, and weight distribution demonstration is performed with reference to fig. 1.
The allocation formula:
current capacity weight (β) formula β (x) tβ(25-x)+bβ
Wherein x is more than or equal to 0 and less than 25 percent, wherein tβAs a factor, x is the initial SOC value of the selected charging section, bβAs the effect of the constant term on the calculation results from the starting SOC, it can be assumed that,
when x is 0, β (x) is 0.99, when x is 25%, β (x) is 0.01β＝0.0392,bβ0.01, β (x) — 0.0392x +0.99(0 ≦ x < 25%).
The following sets of initial states of charge (minSOC) are taken from the currently selected charging segment, and the weights for calculating the current capacity are obtained by the algorithm provided in this embodiment as shown in table 3 for the random experimental point linear function embodiment.
TABLE 3
x(％) β(x)
20 0.2060
16 0.3628
3 0.8724
13 0.4804
8 0.6764
25 0.01
19 0.2452
1 0.9508
22 0.1276
7 0.7156
In addition to the above embodiments of quadratic function and linear function, a piecewise function may also be used as a basis for allocating filter parameters when calculating the current capacity, and a piecewise function weight allocation embodiment is taken as an example in the following, in which a reference d of the minimum state of charge of the screening charging stage is taken as 25%, and a weight allocation demonstration is performed with reference to fig. 1.
The allocation formula:
current capacity weight (β) formula:
Figure BDA0002262306220000081
the following sets of minSOC are taken from the currently selected charging section, and the weight for calculating the current capacity is obtained through the algorithm provided by the embodiment, and is shown in the table 4 as the weight of the random experiment point piecewise function embodiment.
TABLE 4
x(％) β(x)
20 0.405
16 0.721
3 0.9519
13 0.8249
8 0.8884
25 0.01
19 0.484
1 0.9773
22 0.247
7 0.9011
The weight distribution coefficient of the embodiment accords with the rule of influence of the initial state of charge (minSOC) on the state of health (SOH) estimation result of the lithium battery of the multi-segment charging curve, namely, the smaller the initial state of charge (minSOC) of the selected charging segment is, the more accurate the obtained current capacity value result is, so the weight "β" occupied by the battery is larger, and the weight is distributed according to the thought, so that the SOH of the battery estimated on line can better accord with the actual running condition of the vehicle, and the result is more accurate.
It should be noted that, in addition to the filtering variable weight "β" mentioned in the above embodiment, there are many other weight variable ways that meet the requirement of the function change, and this description does not mention one way to describe the method for estimating the state of health of a lithium battery based on the multi-segment charging curve with filtering weight distribution.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made in the above embodiments by those of ordinary skill in the art without departing from the principle and spirit of the present invention.

Claims (3)

1. A lithium battery health state estimation method with filtering method weight distribution is characterized in that charging current and charge state data of a battery in multiple charging processes are obtained through screening data collected by a battery management system, data used for calculating initial capacity and current capacity are screened out, the initial capacity and the current capacity are obtained according to charging data sections and ampere-hour integration, the accurate current capacity is obtained through re-calculation by a filtering method in consideration of the influence of a selected charging section minSOC on a current capacity calculation result, the weight for calculating the current capacity is distributed by a function in the filtering method, and the ratio of the obtained current capacity to the initial capacity is the estimated battery health state.
2. The lithium battery health state estimation method with filtering method weight distribution according to claim 1, characterized by comprising the following steps:
1) acquiring charging current and charge state data of the battery in multiple charging processes through data acquired by a battery management system on the automobile, and screening out data meeting conditions according to required conditions;
2) screening data used for calculating initial capacity, wherein the screening requirement is as follows: recording the initial SOC and the cut-off SOC of the charging section as minSOC and maxSOC respectively, wherein minSOC is less than a, (maxSOC-minSOC) is more than b,0 is more than a, b is less than 100%, a is an upper limit threshold value of the initial SOC of the screening charging section, and b is the span of the initial SOC and the final SOC of the selected charging section;
3) acquiring a first charging data section meeting the requirement, and recording the initial capacity obtained by using ampere-hour integral calculation in the charging data section as Capinitk (ii), wherein ii is 1;
4) and obtaining the ii-th charging data section meeting the requirement, calculating the ii-th initial capacity by using ampere-hour integral, wherein ii is more than or equal to 2 and is recorded as: capinitk (ii);
the equation for ampere-hour integration is as follows:
Figure FDA0002262306210000011
wherein t is1Represents a charging start time of the charging section; t is t2Indicating a charge end time of the charge segment; t is t3Representing the corresponding time of SOC as c, c is a set reference point, and the value range of c is 40%<c<70 percent; i (t) represents a current value at time t in the charging section;
Capinitk(ii)＝AHsum/(min{max SOC,c}-min SOC)
5) and calculating the accurate initial capacity by using a filtering method after obtaining the two data, wherein the formula is as follows:
Figure FDA0002262306210000021
α is an initial capacity filter coefficient, and since the change of the initial capacity value is very small and the filtering is very strong, the requirement can be met by taking α as a value smaller than 0.1;
6) screening a charging data section for calculating the current capacity, wherein the current capacity is marked as Cap;
screening requirements: the minSOC of the charging section is less than d, maxSOC is more than or equal to e,0 is less than d, e is less than 100%, d is an upper limit threshold value of the screening charging section initial SOC, e is a lower limit threshold value of the screening charging section cut-off SOC, and the values of d and e are determined according to actual data conditions;
7) acquiring a first charging data section meeting the requirement, and recording the current capacity obtained by using ampere-hour integral calculation in the charging data section as Cap (ii), wherein ii is 1;
the equation for ampere-hour integration is as follows:
Figure FDA0002262306210000022
wherein t is1Represents a charging start time of the charging section; t is t2Indicating the end-of-charge time of the charge segment,
Capk(ii)＝Ahsum/(maxSOC-minSOC)；
8) and calculating the ii current capacity by using ampere-hour integral in the ii charging data section meeting the requirement, wherein ii is more than or equal to 2 and is recorded as: capk (ii);
9) and calculating the accurate current capacity Cap (ii) by a filtering method, wherein the formula is as follows:
Figure FDA0002262306210000023
wherein β is the current capacity filtering method weight β;
10) the state of health (SOH) of the automobile lithium ion battery can be obtained through the Cap (ii) obtained in the step 5) and the Capinit (ii) obtained in the step 8),
SOH(ii)＝Cap(ii)/Capinit(ii)，ii≥1。
3. the lithium battery state of health estimation method with filtering method weight distribution according to claim 1 or 2, characterized in that the filtering method weight β is selected as follows:
selecting function curve characteristics as weight distribution basis, wherein the function selects any one of quadratic function, linear function and piecewise function;
b, setting a reference quantity for screening the minimum state of charge of the charging section, wherein the difference between the reference quantity and x is a function term, x is the initial SOC value of the selected charging section, β (x) the current capacity weight, and establishing a distribution formula according to the step A;
and C, extracting charging data segments corresponding to different x, fitting the data by using MATLAB, and determining each coefficient in a distribution formula to obtain a final distribution formula of the current capacity weight β.
CN201911075498.7A 2019-11-06 2019-11-06 Lithium battery health state estimation method with filtering method weight distribution Pending CN110850318A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201911075498.7A CN110850318A (en) 2019-11-06 2019-11-06 Lithium battery health state estimation method with filtering method weight distribution

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201911075498.7A CN110850318A (en) 2019-11-06 2019-11-06 Lithium battery health state estimation method with filtering method weight distribution

Publications (1)

Publication Number Publication Date
CN110850318A true CN110850318A (en) 2020-02-28

Family

ID=69599804

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201911075498.7A Pending CN110850318A (en) 2019-11-06 2019-11-06 Lithium battery health state estimation method with filtering method weight distribution

Country Status (1)

Country Link
CN (1) CN110850318A (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113967609A (en) * 2021-10-09 2022-01-25 上海空间电源研究所 Screening and matching method of high-power lithium ion batteries for carrier rocket
CN116381540A (en) * 2023-06-05 2023-07-04 石家庄学院 Battery health monitoring system under computer running state

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101813754A (en) * 2010-04-19 2010-08-25 清华大学 State estimating method for automobile start illumination type lead-acid storage battery
CN103823188A (en) * 2014-02-25 2014-05-28 宁德时代新能源科技有限公司 Lithium-ion battery pack health state assessment method
CN106054080A (en) * 2016-06-06 2016-10-26 电子科技大学 Joint estimation method for State of Charge (SOC) and State of Health (SOH) of power battery
CN108663621A (en) * 2017-03-29 2018-10-16 比亚迪股份有限公司 A kind of the state-of-charge computational methods and system of power battery pack
CN110045291A (en) * 2019-04-26 2019-07-23 上海什弋维新能源科技有限公司 A kind of lithium battery capacity estimation method
CN110320473A (en) * 2019-07-26 2019-10-11 上海理工大学 One kind being based on Kalman filtering and fuzzy logic automobile lithium battery capacity estimation method

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101813754A (en) * 2010-04-19 2010-08-25 清华大学 State estimating method for automobile start illumination type lead-acid storage battery
CN103823188A (en) * 2014-02-25 2014-05-28 宁德时代新能源科技有限公司 Lithium-ion battery pack health state assessment method
CN106054080A (en) * 2016-06-06 2016-10-26 电子科技大学 Joint estimation method for State of Charge (SOC) and State of Health (SOH) of power battery
CN108663621A (en) * 2017-03-29 2018-10-16 比亚迪股份有限公司 A kind of the state-of-charge computational methods and system of power battery pack
CN110045291A (en) * 2019-04-26 2019-07-23 上海什弋维新能源科技有限公司 A kind of lithium battery capacity estimation method
CN110320473A (en) * 2019-07-26 2019-10-11 上海理工大学 One kind being based on Kalman filtering and fuzzy logic automobile lithium battery capacity estimation method

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113967609A (en) * 2021-10-09 2022-01-25 上海空间电源研究所 Screening and matching method of high-power lithium ion batteries for carrier rocket
CN113967609B (en) * 2021-10-09 2023-11-17 上海空间电源研究所 Screening and grouping method of high-power lithium ion battery for carrier rocket
CN116381540A (en) * 2023-06-05 2023-07-04 石家庄学院 Battery health monitoring system under computer running state
CN116381540B (en) * 2023-06-05 2023-08-22 石家庄学院 Battery health monitoring system under computer running state

Similar Documents

Publication Publication Date Title
CN108919129B (en) Service life prediction method of power battery under time-varying working condition
CN111142036B (en) Lithium ion battery online rapid capacity estimation method based on capacity increment analysis
Goh et al. Capacity estimation algorithm with a second-order differential voltage curve for Li-ion batteries with NMC cathodes
CN110320473B (en) Automobile lithium battery capacity estimation method based on Kalman filtering and fuzzy logic
EP4019321A1 (en) Battery managing method and device, and vehicle
CN106772101A (en) The modification method of battery SOC, correcting device and battery SOH evaluation methods
DE102012207815A1 (en) SYSTEMS AND METHOD FOR DETERMINING CELL CAPACITY VALUES IN A BATTERY WITH MANY CELLS
CN109975717B (en) Online calculation method for internal resistance of power battery
CN110850318A (en) Lithium battery health state estimation method with filtering method weight distribution
CN108246658A (en) A kind of method of ferric phosphate lithium cell conformity classification
EP3796015B1 (en) State-of-charge correction method and device
CN111376792B (en) Estimation method for endurance mileage of pure electric vehicle
CN106154175B (en) Rechargeable battery state-of-charge estimating system based on dynamic differential pressure and workflow
CN111967194B (en) Battery classification method based on cloud historical data
CN109596986A (en) Power battery pack internal resistance estimation on line method and battery management system
CN107121642B (en) SOH coefficient adjusting method and device for power battery
CN115407206A (en) SOH self-adaptive estimation method based on capacity accumulation
DE102013203174A1 (en) A method and apparatus for increasing available capacity in a battery string by adjusting cell charge levels, battery management system, battery, and battery charger
CN110045291B (en) Lithium battery capacity estimation method
CN109782182B (en) Online estimation method and device for energy state of series battery pack
DE102015000721A1 (en) Apparatus for estimating a residual capacity of an electrical storage device
DE102015016361A1 (en) Method for determining a state of charge and aging of an electric battery
DE102011079159A1 (en) DEVICE AND METHOD FOR DETERMINING A STATE PARAMETER OF A BATTERY
CN114252797B (en) Uncertainty estimation-based lithium battery remaining service life prediction method
JP6469485B2 (en) Battery deterioration determination device, battery pack, battery deterioration determination method, and battery deterioration determination program

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
WD01 Invention patent application deemed withdrawn after publication
WD01 Invention patent application deemed withdrawn after publication

Application publication date: 20200228